esa-sceva/satcom-chunk-collection huggingface dataset:
# A Realistic Hand Image Composition Method for Palmprint ROI Embedding Attack
## 4 Experiments
### ROI Harmonization
#### 4.1.2 Ablation Study  
Experiments have been devised to elucidate the impact of iteration and losses. The Zhou dataset, characterized by diverse image conditions, was selected for the ensuing ablation study to provide comprehensive insights. The visual results across different iterations are depicted in Figure 10, and the corresponding evaluation results are shown in Figure 11.  
In Figure 10, images from iteration 0 are deemed unsatisfactory, while those from iteration 30 demonstrate a significant enhancement, approaching the quality of the original images. Subsequent iterations yield only marginal improvements. Figure 11 reveals that all losses' elbow points are reached before iteration 30, which means that the mean and std of all losses are relatively lower, and there is not much room for dropping after this point. Considering effectiveness and efficiency, we designate 30 iterations as the optimal parameter for all experiments.  
Building upon the findings at iteration 30, an exploration into the impact of \(\mathcal{L}_{cycle}\) was conducted. The visual outcomes are presented in Figure 12, while a comprehensive evaluation is enumerated in Table 4. The results indicate that the inclusion of additional \(\mathcal{L}_{lips}\) and \(\mathcal{L}_{2}\) significantly enhances both visual quality and realism compared to the original cycle loss. Moreover, our devised texture loss exhibits the ability to preserve more texture while imparting slight alterations to visual quality.  
Figure 10: Harmonized images in different iterations.  
Figure 11: The L1, L2, Lpips, and texture loss in different iterations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setting** & \(\mathcal{L}_{1}\) & \(\mathcal{L}_{2}\) & \(\mathcal{L}_{lips}\) & \(\mathcal{L}_{T}\) \\ \hline Baseline(\(\mathcal{L}_{1}\)) & \(0.145\pm 0.070\) & \(0.034\pm 0.030\) & \(0.496\pm 0.140\) & \(0.026\pm 0.013\) \\ \(+\mathcal{L}_{2}+\mathcal{L}_{lips}\) & \(0.145\pm 0.066\) & \(\mathbf{0.033\pm 0.028}\) & \(\mathbf{0.381\pm 0.120}\) & \(0.027\pm 0.016\) \\ \(+\mathcal{L}_{T}\) & \(\mathbf{0.144\pm 0.070}\) & \(0.033\pm 0.030\) & \(0.384\pm 0.128\) & \(\mathbf{0.024\pm 0.013}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The evaluation results of ablation study of cycle losses; the bolded values are the best.  
Figure 12: The images of ablation study of cycle losses.
## 5 State of the Art on IoT Systems for Industrial Process Control
The platform offers adjustable and interoperable methods for gathering, organizing, and directing data streams from heterogeneous cyber-physical production systems; additionally, it facilitates sophisticated data analytics using a unique machine learning architecture that uses quantitative rule mining. [PERSON] et al. [119] developed a fault-detection system for the CHRIST Os-motron water purifier using certain sensors and data-mining techniques. Six sensors were used to gather data every two weeks before and after the system refurbishment, including good and bad operations. Their research developed two anomaly-detection approaches in IIoT, specifically for water cleaning systems based on ML and data mining. [PERSON] et al. [120] dealt with monitoring and controlling pipeline transportation systems before the failure that leads to fatal accidents. The NarrowBand Internet of Things (NB-IOT) module,  
Figure 19: Aims of the SM technologies based on IoT solutions in industrial fields. which has a high-level engineering interface, embeds the IoT into the Supervisory Control and Data Acquisition (SCADA) platform to enhance supervisory performance. SCADA provides local intelligence using an LQR-PID controller in their suggested technique. When the local intelligence cannot respond to threats in advance, the web server uses an NB-IoT module to quickly disable its performance. To connect virtual and real systems, a DT was suggested by [PERSON] et al. to model production cells, mimic system behaviors, forecast process faults, and regulate manipulated variables adaptively [121]. They created a network of connections across the environments, starting with the production cell that was adjusted to settings like computer-aided applications, product lifecycle management software, and process control platforms to achieve almost synchronous controls. With this work, the authors demonstrated that implementing virtual commissioning as a first step toward system-level digital twinning will accelerate intelligent control systems' training, testing, and validation [122]. [PERSON] et al. presented an Operation-Constrained Process Control (OCPC) to eliminate the time delay errors in the operation cycles of intelligent machines [123]. With the proposed method, the authors consider the completion time, operation cycles, and efficiency metrics to identify production errors and time delays. Based on the productivity data, further modifications or allocations of ordinary operation cycles are defined. Productivity analysis is performed based on historical data using federated learning [124]. AI indicators are used in IIoT smart factories to facilitate unattended interventions and streamline production. [PERSON] et al. provide a novel engineering approach in smart manufacturing systems engineering (SMSE) by achieving smart product quality monitoring systems (SmartPQMS) [125]. This approach has been implemented in the production cycle of steel and iron.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Proposed Digital Twin Architecture
#### 3.2.2 Virtual Model Creation Module  
After collecting essential data on the construction site and equipment, the next step is to create virtual simulation models that have similar characteristics to the physical models. At this stage, it is assumed that the collected data are sufficiently accurate to be used for virtualization.  
This module is responsible for the as-planned and as-built model generation. The as-planned model represents the design models that are scheduled to be excavated or filled. These models include the preprocessing of terrain and terrain modeling, subsurface modeling, design target modeling, and conversion of the design model into a voxel-based model. Additionally, they include the semantic assignment to each voxel, such as material type, strength, moisture content, excavation time, cost, and construction status (a detailed process of voxelization is discussed in Section 3.3.3). This information is appended to each voxel with the aim of utilization in the downstream stages. For example, the material information attached to each voxel is used for material tracking. Moreover, the equipment is configured within the virtual environment that is used for model-equipment simulation. On the other hand, the as-built model is acquired through a sensing technique and continuously updated to reflect the as-built condition. The as-built is integrated with the as-planned to reflect the current state of information.
## 4 Results and Analysis
### Length of the Transition Area
The driving workload was relatively low and showed significant differences compared to the adjacent areas, and when the transition area length was 80 m, it was consistent with the transition area lengths of 120 m and 160 m. The overall traffic safety risk was higher than when the transition area length was 120 m and 160 m, and the length was relatively large. To better characterize the non-linear effects of different transition area lengths on vehicle speed and driving workload, a cubic function was used for fitting, as shown in Figure 8. The fitting results were good (R\({}^{2}>0.999\)), and the fitting formulas are shown in Equations (8) and (9):  
\[v_{0}=0.8383L^{3}-7.08L^{2}+20.662L+41.1\ 80\leq L\leq 200 \tag{8}\]  
where \(v_{0}\) is the average vehicle speed in the transition area, km/h, and \(L\) is the transition area length, m.  
\[K_{0}=-0.0006L^{3}+0.0057L^{2}-0.0171L+0.03\ 80\leq L\leq 200 \tag{9}\]  
where \(K_{0}\) is the average driving workload in the transition area.
# The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches
## 5 Conclusions  
In this study, we propose a method for detecting aggressive driving in two-wheeled vehicles. Our emphasis is on the significance of this research in enhancing the safety of two-wheeled riders and preventing traffic accidents. The key outcomes of our investigation entail the proposal of a model for identifying aggressive driving patterns, such as rapid turns and aggressive rapid lane changes. We derived a subjective concept of aggressiveness through surveys and gathered data on risky driving behavior via simulations. Notably, we explored an approach to detect aggressive lane changes without relying on camera technology, which can be challenging due to the difficulties of installing cameras on two-wheeled vehicles, by relying solely on sensor technology.  
Through this research, we illustrate the potential for identifying aggressive driving in two-wheeled vehicles using sensor data. We formulated criteria for rapid turns through observations and delineated patterns for aggressive lane changes through various feature engineering techniques and the CNN-LSTM model. The results of practical application further validated the correspondence with these patterns. We anticipate that these research findings will contribute to the advancement of two-wheeled driver safety and the prevention of traffic accidents.  
Conceptualization, D.K.; methodology, D.K. and H.K.; software, D.K. and H.K.; validation, D.K. and H.K.; formal analysis, D.K.; resources, H.K.; writing--original draft preparation, D.K.; writing--review and editing, D.K.; visualization, D.K.; project administration, C.J.; funding acquisition, C.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number (2022-MOIS41-005) of the Citizen-customized Life Safety Technology Development Program funded by the Ministry of the Interior and Safety (MOIS, Republic of Korea).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of the experimental participants and these data need to be protected.  
The authors declare no conflict of interest.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 1 Introduction  
Truss systems are commonly implemented in several structural systems including space structures, high-span bridge systems and bracing the skeleton buildings. Recently, it is reported that the implementation of the linear analysis to investigate the structural applications is not sufficiently reliable without considering nonlinearities [1; 2; 3; 4]. Instead, the development of engineering science and growth in computer processing power have made it possible to conduct an efficient nonlinear analysis. The linear theory is used for structures that are under service loads, but if there were slender elements in the structure or external loads exceeding the design loads (e.g., buckling load in the case of structural stability), ignorance of nonlinear behavior would cause considerable errors in a computational process due to large nonlinear deflections. Several studies in nonlinear analysis methods are presented [5; 6; 7; 8; 9], in which the stability of trusses under static and dynamic loads is investigated.  
Along the same lines, [PERSON] et al. [10] studied geometric and material nonlinearity for space trusses. Large deformation of space trusses is investigated with a generalized displacement control method by [PERSON] and [PERSON] [11]. [PERSON] and [PERSON] [12] developed a new method for geometrically nonlinear analysis of the space trusses. [PERSON] et al. [13] used perturbation algorithms for building membranestructure. There are many methods for nonlinear analysis such as iterative, incremental, and simple incremental-iterative methods. Along the same lines, [PERSON] [14] compared the Newton-Raphson and incremental methods with geometrically nonlinear analysis, among which the Newton-Raphson method is used widely in nonlinear problems. Although this method is an efficient technique, a long computational cost is needed to achieve the convergence requirements [15].  
Moreover, fast algorithms were presented to reduce the computational analysis of various structures [16; 17; 18; 19; 20; 21; 22]. An iterative perturbation method to solve a nonlinear system is proposed by [PERSON] and [PERSON] [23]; however, the proposed method was not able to effectively reduce the convergence time despite reducing the number of iterations. In this paper, the presented method by [PERSON] and [PERSON] [23] is improved to achieve an efficient nonlinear algorithm for further investigation of structures. The homotopy methodology is employed previously as a persice perturbation method for inelastic analysis of 2D frames [20]. The proposed procedures decrease both the iteration number and the cost of analysis. In this study, it is shown that the proposed methodology efficiently improves the inability of the Newton-Raphson algorithm in passing limit points due to load control limitations; hence, it improves the cost of analysis significantly.
## 3 Materials and Methods
### Methodology for Comparative Analysis of Platforms to Address Question 2
#### 3.2.2 Criteria for Comparative Analysis  
On the basis of a review of the relevant literature, we established a set of criteria to guide our comparative analysis of the four platforms. These criteria focus on key aspects such as adaptability and personalization mechanisms, artificial intelligence techniques, domain specificity, and the target audience.  
We employed four specific criteria to evaluate adaptive learning platforms:  
* Adaptivity and personalization mechanisms: We assessed how each platform personalized the learning experience, distinguishing between content-oriented approaches (e.g., delivering lessons and recommendations on the basis of progress) and interaction-oriented approaches (e.g., continuous communication with a virtual assistant). This distinction is supported by [73], which suggests that learning outcomes can be influenced by the type of personalization mechanism employed;
* Artificial intelligence mechanism: We examined the specific AI techniques used by each platform, such as large language models, long- and short-term memory networks, gated recurrent units (GRUs), or bidirectional gated recurrent units (BiGRUs). According to [74], these techniques play crucial roles in enabling adaptive learning and personalizing the learning path. We also explore how these techniques are applied in practice, including the use of long- and short-term memory to recognize learning styles taking the example based on deep learning from [75] and GRUs/BiGRUs to understand implicit information as presented in [76];
* Focus domain: we determined whether the platforms catered to a specific subject area or offered content across multiple domains, as this can impact the breadth and depth of the learning experience [77];
* Target audience: We identified the intended age range for each platform, considering whether they focused on specific groups (e.g., young children) or offered content suitable for a wider range of learners [77]. This information is crucial to understanding the potential applicability of platforms in different educational contexts. As the authors demonstrated in [78], age is a significant factor because adult users benefit from the flexibility of these systems, but at the same time younger users need more guidance and help. As demonstrated by the results from [79], the adoption of learning platforms is different depending on the age group.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Ontology-Based Stock Selection  
#### 4.2.1 Financial Ontology Construction  
The financial ontology was developed to map and categorize stocks based on fundamental financial indicators, as illustrated in Figure 5. According to [PERSON] (2023) [27], these indicators are essential for assessing companies' financial performance. For instance, the financial analysis of Chevron Corporation illustrates the importance of indicators such as liquidity, profitability, debt, and growth in identifying promising stocks.  
* Liquidity: Indicators such as the Current Rati o and Quick Ratio measure a company's ability to meet short-term obligations.
* Profitability: Indicators such as Return on Assets (ROA) and Return on Equity (ROE) assess efficiency in utilizing assets and shareholder returns.
* Debt: The Debt-to-Assets Ratio (DAR) and Debt-to-Equity Ratio (DER) reflect financial leverage and capital structure.
* Growth: Metrics like Growth Rate and Revenue Growth Rate evaluate potential future expansion.  
These indicators form the foundation of a Lattice Graph, which hierarchically organizes financial metrics, enabling pattern identification and classification. The process outlined in Figure 5 demonstrates how data ingestion, processing, and ontology creation are streamlined to ensure consistency and accuracy in stock categorization.  
Figure 4: This chart highlights market stress during the COVID-19 pandemic, showing the performance of the S&P500 and the VIX from March 2018 to July 2020. The S&P500 experienced significant declines, while the VIX reached unprecedented levels, reflecting extreme market volatility. These trends illustrate the challenging conditions that served as a testing ground for the framework’s robustness under adverse market scenarios.
# P System with Fractional Reduction
## 4 Fractional Simplification P System
### P System for Fractional Simplification of More Phase Derogation Algorithm
#### 4.1.1 Definition of the P System for More Phase Derogation Algorithm
In the initial state, a partial rule is executed in membrane \(M_{1}\) to determine the magnitude of the numerator \(x\) and denominator \(y\), passing the larger and smaller values to object \(a\) and object \(b\) in membrane \(A_{1}\), respectively. For e.g., if \(x=12\) and \(y=9\), then the rule is executed to generate a multiset \(a^{12}b^{9}\) in membrane \(A_{1}\), i.e., \(x_{1}=12\) and \(y_{1}=9\) in the corresponding sequence. 2. In membrane \(A_{1}\), the multiset \(a^{x_{1}}b^{y_{1}}\) is consumed by partial execution of the rule, a judgment is made as to whether \(z_{1}\) is produced while a new membrane \(A_{2}\) is produced, and the multiset \(a^{x_{2}}b^{y_{2}}\) is produced by passing some material into the submembrane. This step is repeated until no new membranes are generated when the stopping condition for more phase loss is met in membrane \(A_{t}\), and the computation of the sequence \(\{x_{i}\}\), \(\{y_{i}\}\), \(\{u_{i}\}\) is completed. 3. In general, within the membrane \(A_{i}\) (\(2\leq\) i < t \(-1\)), the multisets \(a^{x_{i}}\) and \(b^{y_{i}}\) generated by substances transported by the membrane \(A_{i-1}\) are then transformed into new multisets into the submembrane according to the corresponding rules, respectively, until the end when \(y_{t}\) is equal to \(u_{t}\).
## 1 Introduction
### Related Work  
The first results about local algorithms were given by [PERSON] [2]. He gives the first bounds on locality distances for constructing a maximal independent set and a 3-coloring in an \(n\)-cycle. He also proves that at least \(\sqrt{d}\) colors are needed for coloring a \(d\)-regular tree with radius \(r\) when the locality is restricted to \(2r/3\). In [3] [PERSON] and [PERSON] provide a framework for local algorithms for Locally Checkable Labeling Problems (LCL). All problems which we discuss in this paper are LCLs. In his book [4], [PERSON] gives a locality sensitive perspective of distributed algorithms.  
In general graphs all problems which we study - dominating and connected dominating set, independent set and vertex cover - are NP-hard [5]. Apart from vertex cover they do not even admit constant ratio approximation algorithms [6; 7]. For vertex cover there are several 2-approximation algorithms known, e.g., [8]. When restricting the case to unit disk graphs, the problems remain NP-hard [9], but constant ratio approximations [10] and PTASs [11; 12; 13] are known. However, all these algorithms are global in the sense that in order to determine whether a given vertex is in the computed set, we need knowledge of the entire graph. When looking for local algorithms, [PERSON] et al. [14] proposed local approximation schemes for maximum independent set and dominating set for growth-bounded-graphs. This class of graphs includes UDGs. However, in these algorithms the status of a vertex depends on the vertices which are up to \(O\left(\log|V|\right)\) hops away from it, which is not local in our sense. [PERSON] and [PERSON] presented a distributed PTAS for dominating set with the same locality properties [15]. In [16] the authors provide a marking process in conjunction with two dominant pruning rules in order to reduce the size of a dominating set.  
In [17] [PERSON] et al. presented the first local constant ratio approximation algorithms for our set ting with performance ratios \(5\) and \(7.453+\epsilon\) for dominating and connected dominating set, respectively. The locality distances of the dominating set algorithm is \(11\), the locality for the connected dominating set algorithm is not given in the paper. In [PERSON] and Kranakis (2008) [PERSON] and [PERSON] presented local PTASs for these two problems. However, their locality distances, though constant, can be very large.
Hence, coarse resolution data from AVHRR and MODIS are still needed in many applications. For crop yield modeling, remote sensing data are often extracted using a cropland mask. A general cropland mask may be applicable in many cases [21; 24]; however, its application may be impacted by mixed signals from different crops. This may not be critical in regions such as the Canadian Prairies, wherein the majority of annual crops are seeded in spring and have similar phenological cycles. In Southern and Western Ontario, cropland is dominated by annual crops, including winter wheat, corn, and soybean, rotated with perennial forage crops. These crops have quite different phenological cycles [30]. To decompose signals from different crops, a fuzzy-decision tree classifier was developed to identify the major crops in this region using 250 m time-series MODIS data with minimal ground truth data [31]. Crop-specific masks were then generated from the identified \"pure\" pixels of these major crops for extraction of remote sensing data representative to each crop type for each year. Crop yields in Ontario are usually available through field crop reporting conducted by Statistics Canada. A remote sensing-based approach allows for crop yield estimation at different spatial scales in a consistent way. No study was found in the literature for yield estimation at the county level in this region. The objectives of this study were to (1) evaluate the use of time-series MODIS data for crop grain yield estimation at the county level in Ontario, Canada, (2) assess the impact of different cropland masks on crop yield estimation, and (3) investigate the capability of the yield estimation models in capturing the spatiotemporal variability of grain yields.