dataesr/scientific-paragraphs-categorization huggingface dataset:
We aim to develop a model that is appropriate for our situation and can facilitate the automated processing of the intended dataset with an appropriate level of accuracy. The importance of the welding defect inspection is to improve the welding quality and decrease the fault identification degree produced by human influences, such as incorrect detection and incompetent manual examination. Researchers have suggested a diversity of weld flaw identification approaches founded on computer vision, which can make effective defect detection intelligent and standardized  [7, 8, 13]. For this reason, we are brought to work with this tendency industrial vision. Further, we use a particular deeplearning architecture for welding verification with specific data that remains challenging. Furthermore, to control the limitations of the preceding work and participate with our dataset consisting of six types of defects-lack of fusion, cavity, cracks, inclusion, shape defects, and normal defects-we use the X-ray images to evaluate the NDT method for the weld imperfections.
The approach of data augmentation results in an increase in the dataset size, while simultaneously decreasing the occurrence of overfitting. However, we apply the CNN model to achieve the classification of the six defects, then the results are presented in a confusion matrix and curve form. Figure  2  presents some X-ray weld defect images that we use to create our new dataset-based on the public "GDxray" dataset  [14]. In many of the previous studies, as discussed in the related works section, the CNNs were utilized with precise goals in mind. However, several researchers have devised new and innovative methods to address the limitations of CNNs from dissimilar viewpoints. This work aims to automate and improve the precision of the multiclass categorization of welding defects in X-ray images.
• Developing a CNN model that directly processes the augmented dataset for the automated multiclass categorization of welding defects in X-ray images. Section 2 details some research on weld detection and classification with traditional methods, arriving at new models founded on deep learning. Then, in Section 3, welding identification issues, mostly in the dataset, are presented and the moving-on performance for the CNN is inspected and educated in detail. The outcomes are introduced in Section 4, and Section 5 accomplishes our paper.
Additionally, the SVM and the ANN, applying four texture features assigned to defect detection and classification, were judged and then required supplementary performance accuracy  [15]. Also, Ajimi et al. [16]  developed a method for assessing the performance of deep learning networks across a range of parameter and hyperparameter configurations. They also incorporated an improved learning process into the dataset, which led to a 3% increase in the accuracy of the model. Hou et al. [17]  advanced a deep learning model that could directly analyze X-ray images using a neural network. They evaluated the model's ability to classify features and compared its performance to that of traditional techniques, using dissimilar datasets; the model had 97. 2% accuracy, which is much more advanced than feature extraction methods.
Zhang et al. [20]  suggested a transfer learning classification technique for training two deep neural networks on an improved image dataset. The images are classified into different categories using a multi-model addition structure. In  [21] , the same researcher devised an eleven-layer CNN model for the classification and identification of weld defects. He proposed a non-pixel technique for a quantifiable evaluation and visualization of the model performance. Jiang et al. [22]  offered an enhanced pooling approach that accounts for both the pooling region and characteristic map delivery, which leads to a notable increase in identification precision. To further highlight the significance of computer vision, Cardellicchio et al. [12]  presented a pioneering method that combines laser profilometry and machine learning algorithms to automatically detect defects in welds.
Methodology In our study, the challenge of the curse of dimensionality emerged due to the scarcity of training data. To surmount this obstacle, we employed two primary approaches: data augmentation and modifications to the model's architecture. Data augmentation involved applying diverse transformations to the existing dataset, effectively expanding our training set artificially. This technique fostered dataset diversity, mitigated overfitting risks, and improved the model's ability to generalize. Simultaneously, we streamlined the model's complexity by reducing the number of layers and parameters. This architectural modification aimed to counteract overfitting tendencies, enabling the model to better accommodate the limitations of the available data and enhance its generalization capabilities.
The combination of these strategies enabled us to effectively address the curse of dimensionality, optimize model training, and achieve superior results, despite the constraints imposed by our limited dataset. In our research, we implemented transfer learning and fine-tuning techniques specifically tailored to a convolutional neural network (CNN) model for the classification of various types of weld defects in images. By adapting the model to our specific task, our objective was to enhance its accuracy and efficacy in identifying and categorizing different types of weld flaws. Although we did not conduct explicit evaluations of advanced layers, such as residuals or global average pooling, we explored alternative models beyond the initial implementation of AlexNet. Through our experiments, we assessed the performance of CNN, VGG16, random forest, and Inception V3 models, all of which exhibited significantly improved results compared to AlexNet.
Dataset In this research, we use a public experimental X-ray inspection database (GDXray dataset) created for research and educational purposes. The dataset of primary X-ray inspection includes a section of X-ray weld images that were gathered by BAM, the Federal Institute for Materials Research and Testing in Berlin, Germany. The subset of welds consists of 10 X-ray images that are 4K in length and have varying widths, known as the W0001 series. Additionally, this subset includes explanations of bounding cases and binary images that represent the actual data for weld flaws, referred to as the W0002 series. The Welds W0003 sequence now includes more than 67 X-ray images to better support network training  [14]. The welding process has numerous kinds of imperfections (ISO6520-12007)  [23]. The key categories of welding imperfections are cavity, crack, inclusion slag, lack of fusion, shape defects, and dimensions.
• False negative (FN): A "false negative" in a confusion matrix is when the model incorrectly predicts the absence of an event among all actual positive instances in the data. It is a measure of the model's tendency to miss positive cases or make Type II errors. The harmonic mean of precision and recall is provided by the F1-score. The number of samples of the true response that belong to an intended class is known as the support. The support in the realm of data analysis and machine learning represents the quantitative measure of the frequency or occurrence of an item, event, or class within a given dataset. It encapsulates the prevalence of an item set or the significance of a class by quantifying their respective frequencies in the dataset. Through the assessment of support, insights are derived regarding the distribution of data and the evaluation of patterns or categories within the dataset.
The Dataset Preparation The community database (GDXray) supplied by BAM Federal Institute for Materials Research and Testing in Berlin, Germany  [14] , was used as the data source for upcoming experimental studies. This database's "welding" defects contain 67 defect images of diverse sizes and types. Using previous knowledge, defects in cropping were identified by manually selecting examples from images showing imperfections. These flaws were then classified and labeled with class labels before being included in the 'weld defects' dataset as depicted in Figure  7. The overview of the suggested process is exposed in Figure  7. The proposed system aims to address the limitations of existing systems by using a balanced dataset with X-ray defect images from the GDXray dataset. The GDXray dataset contains various types of weld defects, such as cavities (C), cracks (CR), inclusion (I), lack of fusion (LOF), shape defects (SDs), and normal defects (NDs).
The first step in the proposed system is the manual segmentation of X-ray images to collect a new database. This segmentation process helps in identifying and isolating the regions of interest (ROIs) containing the weld defects, which are then used for further analysis. After segmentation, the technique of data augmentation is applied to increase the size of the database. Data augmentation techniques, such as rotation, scaling, flipping, and adding noise, can be used to artificially create more training samples, thereby enhancing the volume and diversity of the dataset. The dataset is then split into training (80%) and test sets (20%), which are used for training and evaluating the CNN architectures for defect classification. The CNN models are trained using the training dataset, and the accuracy and the loss are computed after each epoch to monitor the model's performance during training.
Validation loss and accuracy are also calculated to evaluate the model's performance on the unseen data from the test set. The overall system performance is measured using a confusion matrix, which provides insights into the model's classification performance for each class. Accuracy, precision, recall, and F1 score are also calculated to assess the system's performance in terms of correctly identifying the weld defects. Figure  8  likely depicts the flowchart or schematic of the proposed system, illustrating the steps involved in the defect classification process.
Proposed CNN Model The proposed CNN model used in the classification of the X-ray image defects; it is shown in Table  1. The proposed model has five 2D convolutional layers, five ReLU activation layers, five pooling layers, and one dropout layer. The dropout rate that is added to the model is a method of regulation in the neural network that supports decreased interdependent learning between the neurons. Dropout mentions overlooking units through the training stage of a confident set of neurons, which is selected at random. The first convolution layer creates a 32-feature map, the second convolution layer makes 64 characteristic maps, the third and the fourth have 128 feature maps, and finally, 128 feature maps are generated at the last convolution layer. Python programming language is used for model creation with Keras-TensorFlow as the backend.
Discussion In this topic of research, the multiclass classification method is designed to detect different weld defects from the public dataset "GDXray" based on a CNN model. To devise the model, all images are resized to 128 × 128. Additionally, the total dataset is divided into training (80%) and testing (20%). Multiclass classification methods use a CNN model, where the hyperparameters are applied to improve the accuracy of the weld defect classification; for this algorithm, we use an epoch size equal to 100 and batch sizes equal to 16 to advance the accuracy of several weld flaw detections. After that, mixing the greatest optimizer (Adam) and a loss function is a categorical cross-entropy with a multiclass classification; using the CNN model attained a better accuracy of 92%. The utilization of a convolutional neural network (CNN) model for weld defect classification is well supported by scientific and formal justifications.
Collectively, these considerations contribute to the CNN model's ability to deliver accurate and precise weld defect classification outcomes. The value is utilized for an extra validation procedure and applied as a predictor. The training and validation curve of the algorithm is presented in Figures  10  and 11. Due to the variety of welding defects in the considered dataset, the accuracy score alone may not provide a reliable evaluation of the performance. Therefore, other known evaluation measures such as precision, recall, and F1-score, are also considered in this study. The performance evaluations are summarized in Table  2. Many parameters can influence the outcomes of a CNN model, including the number of convolutional layers, the kernel size, and the number of filters. Other important parameters include the learning rate, batch size, activation function, and pooling strategy.
For a more comprehensive evaluation of the model's performance, the F1 score can be calculated based on precision and recall. The F1 score is a weighted average of precision and recall and is particularly useful in situations where both precision and recall are important for the problem being solved (the parameters are summarized in Figure  6 ). In our research, we conduct an investigation using multiple models, namely Incep-tionV3, random forest (RF), LeNet, and CNN, to address our research objectives. Following rigorous simulations and experiments, we carefully evaluate and compare the performance of these models. Among the evaluated models, the CNN model emerges as the most promising and delivers excellent results. This finding suggests that the CNN architecture possesses advantageous characteristics that enable it to effectively handle the complexities and nuances of the dataset.
To provide a comprehensive summary of our findings, Table  4  presents the various hyperparameter values. Table  5  displays various accuracy results obtained through research on welding defect classification in X-ray images. The studies conducted demonstrate that the CNN method proposed in our research paper exhibits better accuracy than the method used in their previous work. In recent years, several research studies have been conducted to classify welding defects in X-ray images using different methods. The accuracy achieved by each technique or model depends on several factors, such as the complexity of the problem, the size and quality of the dataset, the features extracted, and the choice of the algorithm used. For instance, Jian et al. [24]  used the SVM method to classify six welding defects and achieved an accuracy of 90.
Random forest is an ensemble learning algorithm that combines multiple decision trees to improve accuracy and reduce overfitting. Our main contribution to this research is the creation of a new dataset through manual segmentation of the original X-ray images. This process allows us to extract the relevant portions of the images that contain welding defects, resulting in a more focused and representative dataset for training our CNN model. This approach not only improves the accuracy of our model but also ensures that the training data are more robust and diverse, thus making them more suitable for real-world applications. For more comprehension, the creation of a new dataset for training a CNN model can be beneficial in many ways. For example, it can lead to better accuracy by providing more relevant and representative data, which can reduce the number of false positives and false negatives in the model's predictions.
Additionally, a new dataset can increase diversity by including a wider range of variations, such as different types and sizes of welding defects, which can improve the robustness of the model. By overcoming the limitations of the original dataset, a new dataset can also provide customization by selecting specific types of welding defects or adjusting the size and resolution of the images to optimize the model's performance. Finally, a new dataset can offer reusability for future projects by serving as a foundation for other similar applications or projects, thus saving time and resources. Based on the methodology we employ, including the creation of a new dataset through manual segmentation and the use of data augmentation and fine-tuning strategies, our CNN model achieves a significantly better accuracy rate of 92% in detecting welding defects in X-ray images.
Conclusions In our research, we perform a multi-class classification method to detect weld flaws in X-ray images by employing a CNN model. The model's creation relies on GDX-ray image databases that are publicly available. This database contains only a minimum number of samples. Therefore, we extract the sub-images and create our dataset, which is used for the training and test process. The technique of data augmentation and fine-tuning strategies helps to achieve the appropriate performance of the model. The multi-class classification is combined with the optimizer algorithm. The model achieves an accuracy score of 92% for the case of the CNN classifier. This performance is comparable or superior compared to its counterparts. The applicability of the proposed method is only tested for the intended dataset.
These capable missions-VERITAS, DAVINCI, and EnVision-will help end a thirty-year drought when visits to Venus were rare-a historical anomaly. Overall, more than forty missions have been launched with Venus on their itinerary (e. g. ,  Taylor et al. 2018 )-virtually the same total number that have been sent towards Mars. However, the last NASA-led mission to Venus (Magellan) death-spiraled into the atmosphere in 1994 after finishing its successful radar mapping campaign. Since then, Venus has been relatively lonely, hosting only three flybys (MESSENGER, Galileo, and Cassini) and two orbiters (Venus Express and Akatsuki, which is still operating as of 2023). In the last three decades, spacecraft data from Venus helped illuminate the workings of its modern atmosphere and hinted at its past. New missions will let us better address the profound question: How has Venus evolved over time? Scientists can tell an alluring story about the evolution of Venus that is impossible to prove or disprove using available data (Fig.
Magellan The NASA Magellan Mission entered orbit around Venus in August 1990 and operated until purposefully plunging into the atmosphere and burning up in October 1994. Magellan was a scaled-down version of a concept called Venus Orbiting Imaging Radar-to save money, Magellan was designed to re-use hardware from other flight programs as much as possible (e. g. ,  Saunders et al. 1990 ). Magellan's primary instrument was a large radar sensor built on a high-gain antenna with a diameter of 3. 7 m. For imaging, the antenna was operated as a synthetic aperture radar (SAR) instrument with a wavelength of 12. 6 cm (S-band) and a look angle of ∼ 25 • away from the vertical direction (i. e. , off-nadir). A small horn antenna was nadir-pointed and collected altimetry data. In the burst-mode of data collection, the horn and high-gain antennas were operated in a careful sequence so their transmissions and the reflections from the Venus surface would not overlap (e.
Towards the end of the mission, the spacecraft dedicated itself to acquiring gravity data via Doppler ranging in its original, elliptical orbit (Cycle 4) and after aerobraking that circularized the orbit (Cycle 5). Ultimately, the best gravity data have a resolution approaching spherical harmonic degree 180, equating to a horizontal resolution of > 250 km  (Konopliv et al. 1999 )-a thousand times worse than the image resolution. Overstating the scientific importance of the Magellan datasets is likely impossible. Although the images, topography, and gravity data are now more than three decades old, they are still being mined for new scientific insights. Magellan revealed most of the properties of the surface and interior discussed below in Sects. 3. 2 and 3. 3. Globally, Magellan provided a snapshot of the planet's current geologic state, including a catalog of thousands of tectonic and volcanic features-some of which are analogous to those observed on other terrestrial planets, whereas others are superlative in the Solar System.
Comparisons between the terrestrial planets in our Solar System suggest that Earth is currently unique as it operates within a plate tectonic regime. Perhaps consequently, Earth has had a habitable climate over geologic time scales. Plate tectonics (as defined on Earth) is characterized by a coherent network of fractured lithosphere, which self-organizes the surface into a series of rigid surface plates. Motion of these discrete plates is accommodated by localized failure along relatively narrow plate boundary zones. The cold surface plates of a plate tectonic regime participate in mantle overturn and in turn are associated with the cooling of the planetary interior. As a result, plate tectonics is considered to be a specific example of the mobile-lid style of mantle convection. Earth is the only body in the Solar System for which a large and robust dataset of its thermal, geologic, and tectonic evolution is accessible.
However, our understanding of Earth's evolution remains contentious despite (or perhaps because of) this expansive dataset. For example, the onset time of plate tectonics and thus its duration-and the mechanisms that initiated it-are far from certain (e. g. ,  O'Neill et al. 2007; Debaille et al. 2013; Gerya 2014; Foley et al. 2014; Weller and Lenardic 2018). The starting condition for Earth is uncertain. However, a long-standing consensus, from a thermal standpoint, is that plate tectonics will eventually wane as Earth cools-and Earth will eventually move into stagnant-lid regime, perhaps not dissimilar to Mars today. In contrast to the current day Earth, observations of Mars suggest a planet operating within a stagnant-lid tectonic regime (e. g. ,  Nimmo and Stevenson 2000). Within this regime, the cold and stiff outermost rock layer does not participate in mantle overturn.
1969; Russell 1976). No crustal magnetism was detected at this particular location. Crustal magnetization is not apparent either in the PVO and VEx datasets. Orbital missions are only able to search for magnetization that is spatially coherent at length scales comparable to orbital altitudes (≥ 150 km above the equator and north pole). No mission has yet made magnetometer measurements at low orbital altitudes over the south pole. Magnetometer surveys below the ionosphere (e. g. , from an aerial platform as in O'Rourke et al. 2021) are best suited to searching for intrinsic magnetism. Any low-altitude survey should be able to definitively test the dynamo hypothesis, which would produce global, albeit weak, fields. A non-detection of crustal magnetism is more equivocal because of the possible spatial variability.
What NASA calls small-cost missions are commonly led by a single Principal Investigator (PI) and designed to achieve a focused set of science objectives. These missions are typically developed and launched within ∼ 3-5 years after their selection. The new decadal survey endorsed continuing three successful programs of competed, PI-led missions that include Venus as a possible destination. First, the Small Innovative Missions for Planetary Exploration (SIMPLEx) program includes very small, low-cost missions with a PI-managed mission cost cap of $55 million in 2018, which the decadal survey recommended raising to roughly $80 million (in fiscal year 2025 dollars, not including the launch). Similarly, ESA's Fast (F) mission opportunities support relatively small, low-cost spacecraft that launch along with a primary mission but then address any area of space science.