esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
## 1 Introduction
In the setup of the experiment, similarly to the experiments of [9], four dataset types were used--normal, left-half, right-half, and uniform distributions--in order to appropriately test the proposed strategy. Accordingly, different kinds of distributions were adopted to gain insight into the performance trends of the proposed strategy. Most applications, such as web services, run for a long duration, which results in variations in their CPU demands. High-performance computing (HPC) applications have a short life expectancy; however, they place an extreme pressure on the CPU. Thus, the statistically chosen models for task sizes speak to the divergent settings of simultaneously scheduling web applications and HPC. The normal distribution indicates a circumstance where web applications and HPC are balanced. Left-half circulation delineates a situation wherein web applications are less likely than HPC applications to be scheduled, and right-half appropriation indicatesthe opposite. A uniform distribution indicates a situation where a single application type is scheduled [9]. The proposed strategy can also be utilised in order to find solutions that are near-optimal to well-outlined problems along discrete space, such as scheduling, production planning, design problems, optimisation of network synthesis, and inventory control. These problems come up in in many domains, including engineering, management, telecommunications, etc. The major contributions of this paper are as follows:  
1. Proposal of a strategy for enhancing the diversification of the SOS algorithm for optimal task schedules. 2. Assessment of the proposed strategy by measuring the performance metrics of the makespan and response time between VMs. 3. Statistical validation of the results obtained with eDSOS against those of DSOS by utilising paired \(t\)-tests and a one-tailed distribution. The rest of this paper is organised as follows: the related work, which includes metaheuristic algorithms used for problems of scheduling tasks in the cloud, the standard SOS, and the processes of discrete symbiotic organism search, is presented in Section 1. In Section 2 the description and formulation of the problem are presented. In Section 3, the proposed system's design and its depiction are exhibited. The simulation settings are described in Section 4. Section 5 presents the results and discussion, and Section 6 presents the conclusion.
# A CSAR 3D Imaging Method Suitable for Edge Computation
###### Abstract  
Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).  
edge computation; circular synthetic aperture radar (CSAR); three-dimensional (3D) imaging: inverse Radon transform (IRT); small rotor unmanned aerial vehicle (SRUAV)  
Academic Editor: [PERSON] Received: 20 March 2023  
Received: 27 April 2023  
Accepted: 28 April 2023  
Published: 4 May 2023
# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 2 Materials and Methods
### Segmentation  
To segment the breast, the interface of the air-tissue and the breast-pectoral muscle had to be identified. For segmentation, there are various strategies (from thresholding to more sophisticated algorithms). Accurate segmentation is an important preliminary step to extract quantitative features from the breast parenchyma and be sure of not having an overestimation of the density due to an inaccurate segmentation of the pectoral muscle, which, by an automatic classification algorithm, would be classified as fibroglandular tissue. In this study, it was therefore decided to validate two software tools for automatic segmentation of the breast [34]: OpenBreast ([[https://github.com/spertuz/openbreast](https://github.com/spertuz/openbreast)]([https://github.com/spertuz/openbreast](https://github.com/spertuz/openbreast)), accessed on 15 September 2024) [35] and Libra ([[https://www.med.upenn.edu/sbi/libra.html](https://www.med.upenn.edu/sbi/libra.html)]([https://www.med.upenn.edu/sbi/libra.html](https://www.med.upenn.edu/sbi/libra.html)), accessed on 15 September 2024) [36]. The objective of the two software is different: in the first case, a risk score is produced, and in the second case, only a density assessment. Therefore, only the first part relating to breast segmentation alone was considered in this study. Manual segmentation by expert radiologists represents the ground truth. The  
\begin{table}
\begin{tabular}{l l} \hline Number of women & 161 \\ \hline Age (mean \(\pm\) SD) & 56.2 \(\pm\) 9.0 \\ \hline Age at first menstrual period (mean \(\pm\) SD) & 12.0 \(\pm\) 1.5 \\ \hline Women in menopause & 116 \\ \hline Age menopause ( mean \(\pm\) SD) & 49.8 \(\pm\) 5.0 \\ \hline BMI (mean \(\pm\) SD) & 25.0 kg/mm\({}^{2}\)\(\pm\) 4.0 \\ \hline \end{tabular}
\end{table}
Table 1: Study population.  
Figure 1: Operational workflow.  
most accurate segmentation method was evaluated on the basis of the calculation of the parameters Dice index and Cohen's kappa.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 2 Model Test
### Scaling Law  
The model slope has three parts: Slope #1 (slope height: 0.68 m, slope ratio: 1:0.59 (height: width), inclination about 58\({}^{\circ}\)), platform (width: 0.38 m), and Slope #2 (slope height: 0.52 m, slope ratio: 1:0.75 (height: width) inclination around 53\({}^{\circ}\)). The micropiles were installed vertically into the platform and in a crossed layout. The length and diameter of the model micropiles were 1.12 m and 3.5 cm, respectively. The spacing of the micropiles and the row spacing were designed to be 24 cm and 14 cm, respectively. In addition, to improve the integrity of the supporting structure of the micropiles, the tops of each micropile were connected by a top slab. The side facing the slope top was defined as the back of the pile, and the other side was defined as the front of the pile. The plan and section view of the landslide model are shown in Figure 2.  
According to the dimensions and carrying capacity of the shaking table apparatus as well as the geometry size of the prototype micropile, in the experiment, the geometric size L (\(C_{L}\) = 8), mass density \(\rho\) (\(C_{\rho}\) = 0.75), and elastic modulus E (\(C_{E}\) = 3) were selected as the control variables. The similitude relationships between the other physical quantities were derived according to Bukingham theory. The main similitude relationships and similitude constants are shown in Table 1.  
Figure 1: (**a**) Static strain indicator, (**b**) displacement meter, (**c**) shaking table system, and (**d**) strain gauge and pressure box.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 3 System Model and Assumption
### Assumptions  
We assume the data transmission between an eNB and an uplink user \(z\in Z\) follows a SC-FDMA (Single carrier frequency division multiple access) signal, which follows LP-OFDMA (Linearly precoded orthogonal frequency division multiplexing). In this system, a single channel \(c\in C\) is modulated by multiple sub-carriers, and different time slots \(t\in T\) of single a time frame \((T)\) are mapped with those sub-carriers. Here, the eNB allocates each uplink user \(z\in Z\) a particular time slot \(t\in T\) according to its priority \(p\in P\) in a single channel \(c\in C\). An uplink user \(z\in Z\) can enjoy the maximum peak data rate \(r_{z}^{max}\) (66\(k\)\(p\)s) through a channel using a one-time slot. The notations used in this paper are summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(N\), \(V\) & Set of UEs and vital signs, respectively \\ \hline \(S\), \(E\) & Set of hypotheses and Events, respectively \\ \hline \(\partial_{C}\), \(\partial_{R}\), \(\partial_{N}\) & Critical, Relevant, and Normal training data, respectively \\ \hline \(\rho\) & Received power of a user \\ \hline \(h\) & Channel gain \\ \hline \(I\) & Interference \\ \hline \(N_{0}\) & Additive White Gaussian Noise \\ \hline \(r_{z}\) & Data rate of uplink user, \(z\in Z\) \\ \hline \(U\) & User Utility \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 1: A smart healthcare application architecture using NB-IoT.
## 3 Local Observability Conditions for the Extended Battery ECM  
The concept of observability is useful in reconstructing the unmeasurable state variables from the measurable signals. Therefore, assessing the observability for the used model before executing the state and parameter estimation is necessary. However, due to the numerous states and nonlinearity, observability analysis for the extended battery model (3) is challenging and usually overlooked by most of the research works.  
In [6], linear and nonlinear observability analysis methods were compared for the battery SOC estimation with ECM. It was pointed out that the local observability of a nonlinear dynamic system at an operating point is not the same as the observability of the system linearized around this point. Recently, the observability analysis for the extended ECM without extending the battery capacity was done in [19], where non-zero input battery current was deduced as the observability condition. However, the observability analysis for the extended battery model (3) needs further discussion.  
Hence, a theoretic and systematic observability analysis method is proposed specifically for the extended battery ECM in this paper. Firstly, the characteristic of the battery ECM is used to decompose the original ECM into two sub-models. Then, nonlinear observability analysis method is applied for each sub-model. A schematic diagram of the proposed analysis method is shown in Figure 4, where the arrows represent the analysis process.  
Figure 4: Schematic diagram of the analysis process.
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 7 Penultimate Layer Activations with Adversarial Images
### Relevance of Analyzing the Activation of \(c_{t}\)- and of \(c_{a}\)-Related Units  
The features extracted by the convolutional CNN layers pass through the next group of CNN layers, namely the classification layers. We focus on the penultimate classification layer, i.e., the layer just before the last one that gives the output classification vector.  
When a CNN \(\mathcal{C}\) is exposed to an adversarial image \(\mathcal{D}(\mathcal{A})\), the perturbation of the features propagates and modifies the activation of the classification layers, which in turn leads to an output vector \(o_{\mathcal{D}(\mathcal{A})}^{\mathcal{C}}\) (drastically different from the output vector \(o_{\mathcal{A}}^{\mathcal{C}}\) for the ancestor) in which the probability corresponding to the target class \(c_{t}\) is dominant. To achieve this result, it is certain that previous classification layers are modified in a meaningful manner, with higher activations of the units that are relevant to \(c_{t}\).  
However, it is not clear how the changes in these classification layers occur. Since the penultimate layer has a direct connection with the final layer and the impact of changes in activation are thus traceable, we delve into the activations of the CNNs' penultimate layers to answer two questions essentially: Do all \(c_{t}\)-relevant units have increased activation? Do \(c_{a}\)-related units have decreased activations?  
The connection between the penultimate and final layers is made through a weight vector \(W\), which, for each class in the output vector, provides the weights by which to multiply the penultimate layer's activation values. Whenever a weight that connects one penultimate layer unit with one class is positive, that particular unit of the penultimate layer is indicative of that class' presence in the image, and vice-versa for negative weights. We can thus determine which penultimate layer units are \(c_{a}\)- or \(c_{t}\)-related.
## 4 Discussion
### Real-Life Conditions Analysis  
As shown in Figure 6, the percentage of acceptable segments collected during the 8 h shifts for each condition highlights the importance of an ECG quality assessment algorithm. Interestingly, a higher proportion of low-quality ECG recordings (67.4% unacceptable) were observed during sedentary periods. This outcome is unexpected, as one might assume that resting conditions would produce more acceptable ECG segments due to reduced physical activity.  
Being able to differentiate between acceptable and unacceptable ECG signals in these varying conditions is crucial for ensuring the reliability of data used to monitor the physiological state of firefighters in the field. Accurate ECG readings facilitate real-time assessments of their cardiac health, allowing for prompt interventions if any abnormal patterns arise. Additionally, this classification can be incorporated into automated monitoring systems to filter out poor-quality signals, leading to better decision-making and improved outcomes. By concentrating on high-quality data, these systems can offer valuable insights into the physical and mental well-being of firefighters, helping to mitigate fatigue-related accidents or health concerns.
# Malicious Powershell Detection Using Graph Convolution Network
## 4 Experimental Results
### Results
#### 4.2.3 Adjacency Matrix Generation  
Figure 11 shows the adjacent matrix generation time measured according to the number of powershell scripts. When the number was 250, the generation time was 8 s. When the number was 500, the generation time was 31 s. When the number was 750, the generation time was 70 s. When the number was 1000, the generation time was 139 s. The adjacency matrix generation time was proportional to the number of powershell scripts. When the number was 1000, the time per powershell script was 139 ms. Thus, we concluded that these results were reasonable for malicious powershell detection.
# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
###### Abstract  
Human action recognition has become a key direction in computer vision. Deep learning models, particularly when combined with sensor data fusion, can significantly enhance various applications by learning complex patterns and relationships from diverse data streams. Thus, this study proposes a new model, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN), in order to enhance performance in the classification of the main tennis strokes: forehand, backhand, volley forehand, and volley backhand. The proposed network incorporates seven basic blocks that are combined with two types of module: an Adaptive Consecutive Attention Module, and Graph Self-Attention module. They are employed to extract joint information at different scales from the motion capture data. Due to focusing on relevant components, the model enriches the network's comprehension of tennis motion data representation and allows for a more invested representation. Moreover, the FFGCAN utilizes a fusion of motion capture data that generates a channel-specific topology map for each output channel, reflecting how joints are connected when the tennis player is moving. The proposed solution was verified utilizing three well-known motion capture datasets, THETIS, Tennis-Mocap, and 3 DTennisDS, each containing tennis movements in various formats. A series of experiments were performed, including data division into training (70%), validating (15%), and testing (15%) subsets. The testing utilized five trials. The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. The proposed model demonstrated excellent tennis movement prediction ability.  
feature fusion graph consecutive-attention network; tennis movement recognition; THETIS; Tennis-Mocap; 3 DTennisDS 2025
## 1 Introduction
In fact, machine learning and AI approaches are critically important in the development of logistics service automation. In logistics service automation, AI algorithms have not only been adopted in driving the automation of robotic processes [13; 14] in a smart warehouse and manufacturing but have also been adopted to measure the data flow to form digital twin models [15], as well as in an automated trading system [16] and in automating the return on investment [17] in an enterprise. In the future, supply chain management will rely more on big data and models. In addition, the information between each link of the supply chain will be more transparent, and adjustments will accordingly be more rapid. The problems to be discussed in our study mainly focus on cross-border supply chain inventory forecasting. Through conducting a thorough literature review, the authors have identified that most of the quantitative analysis of supply chain inventory forecasting has been inclined toward the manufacturing, industrial, and agricultural fields. Relevant research studies of the supply chain are under-researched because conducting research on the supply chain using artificial intelligence requires a large amount of business and inventory data. In reality, big data is one of a company's core assets. Big data is not only very intensive for computation, but also difficult for researchers to obtain. This study addresses the main research problems (RPs) as follows. RP1: The accuracy of the traditional time series model in inventory forecasting is low. RP2: Some commonly used models are inefficient in inventory forecasting due to the underutilization of a complete set of data-intensive data. RP3: Quantitative research using AI associated with inventory forecasting analysis of cross-border e-commerce is insufficient. Thus, this research aims to study the key research questions of exploring the AI models that are suitable for performing supply chain inventory forecasting in a cross-border e-commerce company. We also explore how AI approaches can be efficiently employed to handle huge amounts of data to facilitate companies to produce more accurate inventory forecasts. This paper is organized into six main sections. The current section (Section 1) provides the research background, settings, and objectives. Section 2 provides a comprehensive literature review including: inventory forecasting methods of the supply chain; the concept, influence, and development of cross-border e-commerce; supply chain and logistics management of cross-border e-commerce; and inventory optimization development of cross-border e-commerce. Section 3 illustrates the approaches for data preprocessing and the key AI model adopted in this study. In Section 4, inventory problem analysis and inventory forecasting optimization are also presented. The findings are discussed and concluded in Sections 5 and 6, respectively.
## 4 The Proposed Distributed Image Codec
### The Decoder
#### 4.3.1 Reconstruction Approach  
There are three approaches by which the full image can be recovered from the block-based CS measurements. The first one is to reconstruct each block from its CS measurements independently of the other blocks, using the corresponding sensing and block sparsifying matrices. This is the independent block recovery approach. The second way is to place a block sparsifying transform in to a block diagonal sparsifying matrix and then use the block diagonal sparsifying transform to reconstruct the full image [40]. In this approach, instead of reconstructing independent blocks, CS measurements of all the blocks are used to reconstruct the full image. The reconstruction performances of these two  
Figure 3: Block-based CS reconstruction comparison: (**a**) The original image; (**b**) independent block recovery; and (**c**) joint recovery. PSNR, peak signal-to-noise ratio.  
approaches are almost the same [40]. Alternatively, all image blocks can be reconstructed jointly using a full sparsifying matrix [40]. This approach is known as joint recovery. Instead of using the block sparsifying matrix or the block diagonal sparsifying matrix, a full sparsifying transform is applied to the CS measurements of all sampled blocks combined to reconstruct the full image. This is better than independent and diagonal reconstruction, as a full transform promotes a sparser representation and blocking artefacts can be avoided, as well. Generally speaking, the difference in the peak signal-to-noise ratio (PSNR) between independent and joint recovery is not significant. However, blocking artefacts can be an issue for independently recovered images, as shown in Figure 3.  
Based on this knowledge, the strategy adopted for our decoder is as follows. Each individual image block is recovered as they become available at the decoder. This is used as an initial solution. Once all the blocks are available at the decoder, a joint reconstruction is performed to provide a visually better result.