esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 5 The Advent of Artificial Intelligence in Geometric Dimensioning and Tolerancing (GD&T)  
The rapid development of Artificial Intelligence may be considered among the hottest topics in research in countless industry sectors today. GD&T makes no exception. Recent works deal with the implementation of AI-based methods, mostly making use of Convolutional Neural Networks (CNNs) to enable automated detection of geometric features from CAD models and scanned parts and to extract GD&T symbols from 2D and 3D drawings to assess tolerance zones [30]. Another important contribution of IA in tolerance analysis is related to the use of reinforcement learning and genetic algorithms to optimize tolerance allocation by reducing manufacturing costs while maintaining quality [31]. In the context of Additive Manufacturing, AI-driven optimization methods could help compensate for material shrinkage, warping, and roughness, ensuring more reliable dimensional control. Finally, deep learning models integrated with Control Measurement Machines and optical inspection systems can analyze dimensional deviations in real time, improving inspection speed and reliability [32]. AI-driven anomaly detection techniques can also assist in predicting potential defects before they occur. Despite its advantages, demonstrated by scientific literature, AI-driven GD&T faces challenges such as data availability, model interpretability, and integration with existing manufacturing workflows. Accordingly, future research should focus on developing explainable AI models, improving AI robustness, and standardizing AI-based GD&T methodologies.
# FenceTalk: Exploring False Negatives in Moving Object Detection
###### Abstract
However, even after capturing images with targets in the desired new area for labeling and using this annotated data for training the deep learning model, the model often fails to recognize certain moving objects. Identifying these images where recognition was unsuccessful and using them as training data for the model can further enhance its accuracy. Extracting image frames containing moving objects from a vast amount of image data as training data for the model incurs significant labor and time costs. For instance, at the common camera recording rate of 30 frames per second (FPS), a camera can generate 2,592,000 image frames in a single day. Given that the moving objects to be recognized in a fixed area exhibit movement characteristics, FenceTalk designs an algorithm to define a background image, and compares the current image frame with the background image  
Figure 1: FenceTalk user GUI. (Red polygon: fenced region; Pink square: the moving object). using SSIM to analyze differences in the images. Subsequently, based on the brightness of the current image, FenceTalk employs an optimal threshold to separate background noise from the moving objects of interest. This process determines whether the current image frame contains a suspicious image with moving objects. By filtering these selected suspicious images, FenceTalk enables users to choose the model's training data with reduced human resource costs. To minimize hardware costs, we deployed FenceTalk on an embedded device, the Nvidia Jetson Nano, equipped with a graphics processing unit. The Jetson Nano GPU features 128 CUDA cores and 4 GB LPDDR4 memory, making it suitable for running CNN models that require extensive matrix operations. This paper is organized as follows: Section 2 reviews the previous work; Section 3 describes the FenceTalk architecture; Section 4 describes how SSIM is used in FenceTalk; Section 5 evaluates the object detection performance of FenceTalk and the time and space complexities of Nvidia Jetson Nano.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 4 Application Direction of Dynamic Measurement
### Target Detection  
Because the gravity information of large targets, such as submarines, cannot be concealed and camouflaged, target detection based on gravity has the advantage of anti-jamming. The magnitude of the gravity change caused by the target entering the detection area is related to its own mass. According to our calculation, taking the Ohio class submarine with a displacement of 18,700 tons as an example, when the distance above or below the detector is about 200 m, the regional gravity change is about \(\upmu\)Gal level [69], so the gravity measuring equipment is required to have high sensitivity. In addition, in order to cruise and detect in a large area, it is also required that the instrument can perform dynamic measurements. The atom gravimeter meets the above requirements and has great performance potential, so target detection is one of its possible application directions. But so far, the dynamic measurement accuracy and sensitivity of the atom gravimeter have not met the requirements. Mobile measurement can be considered for the time being, i.e., after the detector arrives in a certain area, target detection will be carried out after static deployment. Cruise detection will be implemented after a further breakthrough in the dynamic measurement performance of the atom gravimeter.
# Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection
## 2 Materials and Methods
### Object Detection with Synthetic Data  
Concerning object detection models, Scaled-YOLOv4 [22] has recently achieved state-of-the-art performance on the Microsoft Common Objects in COntext (MS COCO) dataset [23]. However, to enable a quicker iteration of experiments, the YOLOv4-Tiny model [24] was chosen for the validation of this approach due to shorter training times. Training was also carried out in a single NVIDIA Tesla V100 GPU using Darknet [25], an open source neural network framework written in C and CUDA supporting CPU and GPU computation. The base implementation is available at [[https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)]([https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)) (accessed on 9 March 2021).  
Figure 2: Basic structure of a Generative Adversarial Network (GAN). The generator \(G\) and discriminator \(D\) are generally implemented by multi-layer networks consisting of convolutional and/or fully-connected layers. Adapted from [21].  
To assess the impact of the proposed augmentation approach, two distinct models were trained over 6000 iterations each, one using only the real training set of 58 structural adhesive bead images, and the other using the full augmented set of 594 images. Afterwards, the mean Average Precision (mAP) of each model was calculated at different Intersection over Union (IoU) thresholds, first for the validation set of 58 real images, then for a second holdout set of 19 images generated in a separate experiment. This metric was chosen as it common in most modern object detection tasks [23; 26]. The holdout set included images with defects occurring in portions of the bead that were not common (or present at all) in the original dataset, representing a more difficult validation scenario.
## 5 Experiments
### Comparison with Predicative Methods  
In this section, we evaluate the efficacy of our proposed methodology in the realm of task planning capability prediction by contrasting it with baseline approaches. The selected comparative methodologies include fundamental ML algorithms like Graph Neural Networks (GNNs) [44] and Recursive Neural Networks (RecNNs) [45]. These models were chosen for their capacity to accommodate tree-structured input data, thereby ensuring a level playing field for comparative analysis. The input to all of the models was constructed using HCDP, a preprocessing technique suited for SAL. During the training phase, parameter optimization was performed across all models to ensure performance.  
Table 5 showcases a comprehensive evaluation of various algorithms, including our proposed methodology. The results elucidated in this table are a testament to the effectiveness of our technique. It is evident from the empirical data that our approach has a definitive edge over the baseline methodologies.  
In the field of SAL, the precision and accuracy of predictions hold paramount significance. Given the complexities inherent to SAL, it is imperative for algorithms to adep  
Figure 5: Comparison results for different numbers of subtasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1** \\ \hline GNN & 0.843 & 0.824 & 0.833 \\ RecNN & 0.835 & 0.817 & 0.826 \\ Our method & 0.889 & 0.825 & 0.856 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The experimental results with baseline predicative methods.  
predict and optimize task planning. As delineated in Table 5, our methodology distinctly excels in this dimension. It not only assures enhanced accuracy but also emphasizes the salience of context-aware predictions within SAL. Multiple elements bolster the pre-eminence of our approach. Primarily, the strategy we introduced is congruent with the task planning architecture intrinsic to learners. Subsequently, the Tree-LSTM exhibits remarkable efficacy in modeling and predicting tree-structured data.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Precedence Graph (Conflict Graph)  
A _precedence graph_, also known as _conflict graph_, can be used to model a situation with constraints related to the concurrency of events. Two events that cannot be processed simultaneously are in conflict and therefore need a precedence relation to be defined [128]. The concept of a precedence graph has been visualized in several ways during its existence. In most references in our dataset, the precedence graphs are visualized as a DAG.  
[PERSON], [PERSON] [129] proposed an efficient precedence graph in the flexible job shop scheduling problem. They used a similar approach to MDAG explained in Section 5.7.3. But they added a sequencing flexibility to the model. Also, they represented the precedence constraints of a single job as in Figure 15. This representation of precedence constraints seems to work well with industrial environments including the printing industry [99,130].  
Although precedence graphs are mainly categorized as directed graphs, [PERSON] and [PERSON] [131] defined an undirected graph as a precedence graph to model a flow shop scheduling problem. They discussed an interesting logical relevancy between their precedence graph and an associated agreement graph (which explained in Section 5.4) [117]. Instead of connecting agreeing jobs (nodes), this proposed precedence graph connects conflicting jobs [131]. Therefore, solving the FS problem with conflict graph is equivalent to solving FS problem with agreement graph.  
Figure 14: DAG representation of a flow shop systems.  
Figure 13: (**A**) Job shop DAG representation; (**B**) the topological sort of the DAG ([PERSON], 2021).
## 3 Results and Discussion
### Improvement of digital and digitized mammograms  
There are two basic ways to create images with X-ray radiation, namely, projection imaging, that is to pass an X-ray through the sample section and project a shadow image over the receptor, and computed tomography (CT), that is, image reconstruction by computer from X-ray penetration data [46]. The focus here is on projection imaging as the basic process employed in common X-ray radiography and fluoroscopy practice. In the field of X-ray medical image processing, image restoration has been mostly used for filtering Poisson distributed film-grain noise in breast X-rays, mammograms, and digital angiographic images [45], removal of the additive noise in Magnetic Resonance Imaging (MRI) [47], and in the area of quantitative autoradiology (QAR) [48].  
Entropic processing was carried out on optically scanned X-ray mammograms, as well as on digitized samples from mammography databases available on the Internet. Input images were full breast scannedmammograms (Figure 8), and details of clinical interest (Figure 9). The processed images were evaluated by expert radiologists from \"Fundeni\" Hospital in Bucharest, Romania, and Casa di Cura \"Salus\" and the Cancer Center, University of Trieste, Italy. Output images displayed overall superior quality as compared with their input counterparts in terms of contrast and visibility. The processed images restored existing blurred details in images that could not have been recovered by conventional analysis and facilitated their clinic interpretation by expert radiologists.  
Benefits of digital mammography in early breast cancer detection include: (i) reduced radiation dose, (ii) post-acquisition image enhancement, (iii) rapid display of image, (iv) improved imaging in dense breasts, (v) improved sensitivity and specificity, (vi) simplified archival, transmission, and retrieval, (vii) potential usage in automatic diagnosis, and (viii) direct access to worldwide experts by telemammography. Greater benefits are to come due to the increasing incidence of breast cancer with age. Generally, it is easier to detect lesions in the older breast due to fatty involutional changes. X-raying the breast does present unique technical challenges. The contrast resolution of the image must be sufficient to discriminate between soft tissue and tumor. The spatial resolution must be adequate to distinguish fine calcifications well under \(1\) mm in size.  
Figure 8: Full breast mammograms: Fundeni Hospital - normal breast (left), case mdb032, MIAS - benign ill-defined masses, fatty-glandular tissue (middle), case mdb005, MIAS - benign circumscribed masses - fatty tissue (right). Digitized X-ray images by optical scanning (top) and digitally restored images (bottom).
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 3 Novel Pooling Methods
### Max-Out Fractional Pooling  
The concept of fractional pooling applies to the modification of the max pooling score. In this case, the multiplication factor (\(\alpha\)) can only take non-integer values such as 1 and 2. The location of the pooling area and its random composition are, in fact, factors that contribute to the uncertainty provided by the largest max pooling. The region of pooling can be designed randomly or pseudo-randomly, with overlaps or irregularities, employing dropout and trained data augmentation. According to [PERSON] et al. [57], the design of fractional max pooling with an overlapping region of pooling demonstrates greater performance than a discontinuous one. Furthermore, they observed that the results of the pooling region's pseudo-random number selection with data augmentation were superior to those of random selection.  
Figure 6: Representation of the 4P module with the pooling size vector s = [5, 3, 1].
###### Abstract
Of course, flawed as it is, the numerical simulation method is still a promising tool. On the other hand, an easily developed method is needed to replace LEM and qualitative evaluation methods for fast FoS prediction. Benefiting from the vigorous development of computational intelligent technology, the advent of the machine learning (ML) model shed light on tackling the challenge of FoS prediction [34; 35; 36]. Applying the ML approaches to predict FoS based on collected slope cases became an important solution [37]. As a robust soft computing model, neural networks are widely used to assess slope stability and represent the mechanical behavior of materials and other nonlinear problems [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. Neural networks include artificial neural networks (ANN), back propagation neural networks, differential evolution neural networks, and other methods. [PERSON] [49] pioneered the concept of intelligent rock mass mechanics and  
Figure 1: Photograph of a slope case in Jiangxi Province, China (photograph by [PERSON]). established a neural network slope stability prediction model. [PERSON] et al. [50] introduced a deep learning neural network (DLNN) model in the assessment of landslides in Kon Tum Province, Vietnam, and compared its prediction performance with other classical ML methods. [PERSON] and [PERSON] [51] use two novel optimization techniques (i.e., the vortex search algorithm and equilibrium optimization) to fine-tune the neural network model used to determine the FoS of a single-layer slope. Table 1 in Appendix A summarizes published typical neural network-based methods. Until now, the vast majority of existing studies on slope stability prediction adopted a single ML technology, and more complex algorithms are still needed to improve slope stability prediction [29]. Despite many efforts, there is still a debate about which model can achieve the most stable landslide displacement prediction. Moreover, most ML models verify their superiority with only a few cases, and this strategy may lead to unreliable conclusions [52]. With the increasing requirement for FoS prediction accuracy, there is a trend to develop more reliable ML models. In this paper, the principal component analysis (PCA) method is utilized to map high-dimensional raw data to low-dimensional through matrix transformation. Then, combining the advantages of the particle swarm optimization (PSO) algorithm and ANN model, a hybrid PANN model is developed to predict FoS. It introduced evaluation indicators to evaluate the generalization performance of the model and further discussed the application of the PCA-PANNN model. The outline of this study is as follows: Section 2 briefly describes the importance of the study. Section 3 describes the selection of the influencing variables and the statistical description of the collected dataset. Section 4 presents the details of the developed model. Section 5 demonstrates the model effects with practical applications. Section 6 provides slope prevention methods as well as limitations of the current study. Section 7 summarizes the conclusions of this study.
## 1 Introduction
Firstly, QZRAM adds a parallel page-based write module, which enables QZRAM to better utilize multiple cores of computers and multiple computing units of QAT to concurrently process write requests. Secondly, QZRAM adds a compression module based on a parallel compressed stream, provides a transparent compression function entry, and manages the QAT hardware-accelerated GZIP algorithm and pure software compression algorithm libraries. In our evaluation, we use QZRAM as the swap device and measure its performance in the FIO micro-benchmark and the practical Redis benchmark. In the FIO micro-benchmark, QZRAM can attain a 1.6\(\times\) average write throughput improvement with a 1.51\(\times\) compression ratio and about a 43% reduction in computing resource consumption (from more than 49.2% CPU consumption to less than 36.6% CPU consumption). In the practical Redis benchmark, by leveraging QAT acceleration, QZRAM (1) increases memory space (1.66\(\times\)) based on ZRAM, delaying the timing of OOM triggers; and (2) significantly reduces the consumption of CPU resources during compression so that the CPU resources can be better allocated, improving the performance of the upper-layer of the Redis server. This paper makes the following contributions:  
* We propose QZRAM, which leverages the Intel(r) QAT accelerator and integrates it into ZRAM. In QZRAM, this integration can achieve efficient data compression and provide application-agnostic and cost-efficient data storage. QZRAM is designed in the kernel, making it transparent to upper-level applications. It can support various memory-intensive applications without modification. * We implement a flexible and dynamic memory compression engine in QZRAM. Since the GZIP compression algorithm is assisted by QAT, QZRAM improves the data compression ratio based on ZRAM, thereby improving memory capacity and delaying memory overflow opportunities. * We add an offload module to QZRAM, which asynchronously offloads (de)compression functions to the QAT accelerator. This way of offloading can reduce the CPU computing resource consumption during the QZRAM data compression process and address the technical challenges of long CPU idle times and low usage of QAT computing units. * We evaluated the performance of QZRAM through experiments, and the results show that QZRAM can effectively reduce CPU resources in the FIO micro-benchmark, increase memory space based on ZRAM, and alleviate the memory overflow phenomenon of the Redis benchmark.
## 5 Results
### Topical Analysis (LDA)  
The latent Dirichlet allocation (LDA) model was used to identify the main themes in the qualitative texts. The texts were processed to remove punctuation, numbers and empty words, and then the LDA model was applied to extract the topics. The results of the topic analysis are presented in Figure 3 below, showing the most significant terms in each topic.  
* Topic 1: This topic focuses on terms such as \"social\", \"performance\", \"orientation\", \"MiPYMES\", \"media\", \"entrepreneurial\", \"electronic\", \"dimension\", \"performance\", \"trade\" and \"adoption\". It focuses on the relationship between social networks and SME performance, emphasizing the importance of entrepreneurial orientation and the adoption of e-technology and e-commerce. \"Size\" and \"Performance\" indicate a consideration of firm size and market performance.
* Topic 2: Predominant terms in this topic include \"PYMES\", \"probability\", \"ml\", \"major\", \"implementation\", \"automatic\" and \"learning\". This topic discusses the implementation of machine learning in small- and medium-sized enterprises. The terms \"probability\" and \"\(p\)\" relate to the statistical techniques used in these studies.  
Figure 2: Forest diagram of individual effect sizes and combined effects. The blue diamond represents the combined effect estimated using a random-effects model.  
The \"implementation\" of \"machine learning\" indicates a focus on the adoption of these technologies and their potential results, emphasizing the importance of advanced technologies to improve business processes.
* Topic 3: This topic includes terms such as \"tqm\", \"performance\", \"practices\", \"innovation\", \"management\", \"performance\" and \"quality\". This topic analyzes the implementation of machine learning in small- and medium-sized companies. The terms \"probability\" and \"\(p\)\" relate to the statistical techniques used in these studies. It is relevant to TQM and performance practices in PYMES. \"\(\beta\)\" and \"\(p\)\" are statistical terms that indicate the relationship between variables and their significance. The inclusion of \"innovation\" and \"management\" demonstrates how the emphasis on TQM practices and innovation management affects the performance and quality of PYMES.
* Topic 4: The main terms are \"social\", \"networks\", \"PYMES\", \"product\", \"enterprise\", \"performance\", \"quality\" and \"adoption\". It focuses on the use of social networks and product quality in small- and medium-sized enterprises. The terms \"social\" and \"network\" suggest a strong relationship with the adoption of social communication technologies. \"Product\" and \"quality\" indicate that the focus of the discussion is also on how PYMES manage product quality to improve business performance.