esa-sceva/satcom-chunk-collection huggingface dataset:
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Design Optimization
#### 2.4.4 Particle Swarm Optimization (PSO)  
A population-based optimization approach with naturalistic inspiration is the particle swarm optimization method. The same as genetic algorithms and other optimization techniques of a similar nature, it is a wholly stochastic process. This algorithm replicates the movement of particles, such as the swarming or shoaling of a school of fish, the flying motion of a flock of birds or the schooling of insects. In a similar way, the swarm of potential solutions \"wings\" in the direction of the best solution.  
Due to its reputation as one of the most promising, nature-inspired algorithms, particle swarm optimization is often applied in a wide range of scientific applications. This class of optimization technique is significantly well liked because of how easily it can be applied to a variety of problems.  
PSO is one of several optimization strategies that have been chosen for this work due to some of its benefits over other approaches, including:  
1. The PSO algorithm is simple to implement, making it applicable to both engineering and scientific research problems;
2. It has fewer parameters to be adjusted;
3. PSO is more efficient since only the most optimistic particle may pass information to the other particles over the evolution of generations, and, therefore, the optimization procedure moves quite quickly to better fitness values.  
The MATLAB optimization toolbox package contains the algorithm employed in the current work. The size of the problem, the computational cost and the required accuracy are all taken into consideration while choosing the algorithm's parameters by trial and error. Specifically, the algorithm's self-adjustment and social-adjustment parameters are set to 1.49, the maximum number of iterations to 250, the inertia range to [0.1, 1.1], the stall iteration limit to 20 and the swarm size to 100 particles.  
In Figure 4, the optimization procedure is displayed. At each iteration, the MATLAB code that we developed for the FE model implementation is executed to calculate the objective function of Equation (53) needed by the optimization process. As the first step of the optimization procedure, the PSO parameters (number of design variables, swarm size, maxiter, lower and upper bounds of the design variables) are defined. At each step, the algorithm evaluates the objective function at each particle. After this evaluation, the algorithm decides on the new velocity of each particle. The particles move, then the algorithm reevaluates. The maximum number of iterations or a sufficient level of fitness serve as the convergence criteria. As long as the criterion is not satisfied, the algorithm continues to evolve the particles population. Finally, the optimal solution is obtained.
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
## 2 Modeling and Experiment Comparison
### Data and Experimental Environment  
The dataset used in this article was provided by the communication industry database, which includes a total of one million samples of existing network users, users leaving the network without porting numbers, and users porting numbers. In order to protect customer privacy, the data were strictly desensitized twice and were solely used for academic research.  
Among them, 13,000 users left the network by porting numbers, and 94,000 users left the network without porting numbers. The number of features was 48, including 35 numerical features such as \"caller calling times\", 8 discrete features containing \"primary and secondary card identification\", and 5 regional features such as \"area\", \"branch\", \"street\", and \"community\".  
Experimental environment: Windows10, Python3.7.3, tensorflow-gpu1.9, and keras2.2. Application program: Jupyter Notebook. Server environment: Intel(R) Xeon(R) CPU E5-2620 v3 @ 2.40 GHz (two processors), RAM 48.0 GB.
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 3 Materials and Methods
### Experiment Settings  
To assess the effectiveness of WTConv, we conducted an ablation study by systematically integrating WTConv layers into the baseline model (YOLOv11n) and analyzing their impact on model performance. The backbone module in YOLOv11n plays a crucial role in extracting abstract features of defects, which is why we replaced each convolutional layer in the backbone with WTConv to enhance feature extraction. The evaluation was performed using both the general test set and the specialized defect-specific test set to determine the model's capability in detecting different defect types.  
All experiments were conducted using the PyTorch 2.5.1 framework on a GeForce RTX 4090 GPU with 24 GB of memory. Each model was trained for 400 epochs with a batch size of 16, utilizing the SGD optimizer with a learning rate of 0.005 according to the official recommendations of YOLOv11. To ensure a fair comparison, image pre-processing techniques such as cropping, resizing to 512 \(\times\) 512, and data augmentation (rotations, flips, and scaling) were consistently applied across all experiments. The best-performing model parameters, determined based on validation set accuracy, were selected for the final evaluation.  
For the defect-specific test set, additional performance metrics were recorded, including precision, recall, and F1-score for each defect class. Moreover, a subset of predictions was randomly selected in proportion to compare the model's outputs with human in  
Figure 4: An example of WTConv operation on cracks.  
-spection results, providing a qualitative assessment of the model's detection accuracy in field applications.
## 3 Results
### ECAS Spray and Fog Treatment Reduced S. Enteritidis on the Egg Surface  
_S._ Enteritidis counts (log\({}_{10}\) CFU/egg) are presented in Table 2 and Figure 2. Sprayed water (45 s) showed a significant reduction (1.0 log reduction, \(p=0.0005\); Table 2) when compared with the untreated control. All the ECAS treatments significantly reduced the inoculated \(S\). Enteritidis counts on the egg surfaces compared to the untreated control: sprayed ECAS (45 s) (5.4 log reduction, \(p<0.0001\); Table 2), aerosolised ECAS (60 s) (2.8 log reduction, \(p=0.0008\); Table 2) and aerosolised ECAS (120 s) (4.2 log reduction, \(p=0.0022\); Table 2). A significant reduction in \(S\). Enteritidis counts (1.1 log reduction, \(p<0.0001\)) was also achieved with aerosolised water (120 s), whereas no significant reduction was observed with aerosolised water (60 s) when compared with the untreated control (Table 2). For the spray versus aerosol techniques comparison, sprayed ECAS (45 s) showed a significant reduction (\(p<0.0001\)) in \(S\). Enteritidis load when compared with the sprayed water (45 s) (Figure 2A). Significant reduction in \(S\). Enteritidis load was observed for aerosolised ECAS (60 s, \(p<0.01\)) and aerosolised ECAS (120 s, \(p<0.01\)) when compared with respective aerosolised water treatments (Figure 2A). \(S\). Enteritidis level was significantly reduced in spray water (45 s, \(p=0.0005\)) and aerosolised water treatment groups (120 s, \(p<0.0001\)), respectively.  
Figure 1: Effectiveness of sprayed and aerosolised ECAS on reduction of total bacterial load on egg surface. (**A**) Total bacterial load after the various sanitisation treatments. (**B**) Aerosolised ECAS sanitisation of individual (one egg/tray) and simultaneous (three eggs/tray) treated eggs for 60 s and 120 s. NaOCl—sodium hypochlorite (\(\sim\)200 mg/L FAC); ECAS—electrochemically activated solution (150 mg/L of FAC); ns—not significant; **\(p<0.01\), *** \(p<0.0001\).  
Figure 2: Effectiveness of sprayed and aerosolised ECAS on reduction of \(S\). Enteritidis counts. (**A**) \(S\). Enteritidis counts after the various sanitisation treatment. (**B**) Aerosolised ECAS sanitisation of individual (one egg/tray) and simultaneous (three eggs/tray) treated eggs for 60 s and 120 s. NaOCL—sodium hypochlorite (-200 mg/L FAC); ECAS—electrochemically activated solution (150 mg/L of FAC); ns—not significant; **\(p<0.01\), ***\(p<0.001\), ***\(p<0.0001\).  
Aerosolised ECAS (120 s) significantly reduced \(S\). Enteritidis counts for one egg/tray (_p_ < 0.0001); however, no significant reduction trend was observed in the 3 eggs/tray treatment group (Figure 1B).
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 3 Materials and Methods
### Decoupling of the LC-DM AO System
Because the DM corrects the aberrations of the first \(N\) eigenmodes, the wavefront reconstructed with the LC should not contain the first \(N\) eigenmodes, thus the first \(N\) eigenmode coefficients of \(\varphi_{LC\_response}\) is the coupling aberration that must be filtered out. Therefore, the coupling aberration can be calculated as follows:  
\[\overline{\varphi}_{\text{coup}}=M\cdot I_{N}\cdot m_{LC} \tag{18}\]  
So far, the coupling term is obtained with Formula (18), and the coefficient \(C_{coup}\) can be calculated with Formula (15). The third step is to subtract the coupling term to reset the control signal sent to the LC, thus the control signal sent to the LC after decoupling is as follows:  
\[C^{\prime}=C-C_{coup} \tag{19}\]At this time, the coupling aberration is filtered out, and the aberration decoupling between the two correctors is realized. This is a type of control signal reset method. The basic principle is to remove the coupling term from the original signal.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 3 Connectivity Issues and Communication between the Ship and SDT
Finally, the application layer includes the CIP protocol family, OPC (Open platform communications), OPC-UA (Open platform communications--Unified architecture) as used in [47] and explained in [45], MQTT (MQ Telemetry transport) as used in [14] and explained in [45], MTConnect as explained in [42,48], AMQP (Advanced message queuing protocol) as explained in [45], SoAP (Simple object access protocol), CoAP (Constrained application protocol) as explained in [45,49], NTP (Network time protocol), Wireless-HART, Modbus as used in [14], PROFIBUS DP, and PROFINET.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
## 5 Results
### Behaviour of the Different Pollutants  
Finally, in this test, we carry out an independent analysis for each pollutant, compared at the same time over the same route. These results are shown in Figure 10. We can see the differences in the pollution levels from different pollutants and how these may affect the route selection.  
From Figure 10, we see that PM indicators are the worst ones with the highest levels, compared with the other pollutants. This scenario would be a problem in case of respiratory and cardiovascular disorders [5] and the user should plan on and decide an alternative, either delaying his/her trip or changing the route, avoiding the city center.  
Figure 9: Pollution impact on different modes of transport (16 July 2023, Sunday at noon).  
From the correlation between the pollutants detailed in Table 4, we can see that NO\({}_{2}\) with PM10 and PM2.5 have a correlation of 0.75 and 0.85, respectively. The main reason behind this is due to fossil fuel combustion, as explained in [32]. On the other hand, we see the inverse relationship between O\({}_{3}\) and the other pollutants, with the exception of SO\({}_{2}\). This is because O\({}_{3}\) is a secondary pollutant which is formed in the atmosphere from the reaction of sunlight with the other pollutants (primary pollutants). Thus, when the primary pollutants grow, later O\({}_{3}\) grows by reducing the primary ones.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.4 Limited-Memory BFGS  
The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method is a nonlinear gradient-based optimization algorithm employed to minimize the quantity of perturbations introduced into images. It is a white-box adversarial attack introduced by [PERSON] et al. [14] and it differs from the FGSM in two key aspects: the Distance Metric aspect and the Precision versus Speed aspect.  
In terms of the distance metric, the L-BFGS attack is optimized for the \(L_{2}\) distance metric, whereas the FGSM is designed for the \(L_{\infty}\) (infinity) distance metric. However, from the precision versus speed metric, the FGSM is known for its computational efficiency but may not always produce AEs that are visually imperceptible from the original data. The L-BFGS attack is formulated to generate AEs exceedingly similar to original inputs, but this quest for accuracy often results in heightened computational time as a trade-off.  
By formalizing the optimization problem depicted in Equation (4), where the primary aim is to minimize the perturbations \(r\) introduced to the original input (i.e., image) while considering the \(L_{2}\) distance.  
\[Arg\ min_{r}f\ (X+r)=l\qquad s.t.\ (X+r)\in D \tag{4}\]  
here, \(X\) denotes the original input data (i.e., image), \(r\) is the perturbation simple within the input domain \(D\), \(f\) is the classifier's loss function and \(l\) is the incorrect predicted label (\(l\
eq h(X)\)) of the adversarial example \(X^{\prime}=X+r\).  
By optimizing for the \(L_{2}\) distance and prioritizing precision over speed, the L-BFGS attack aims to generate perturbations that result in small changes across all dimensions of the input, rather than focusing on maximizing the change in a single dimension. Hence, this method excels in generating AEs, yet its feasibility is limited by a computationally demanding algorithm to explore an optimal solution.
## 4 Conclusions
Notably, while this study only demonstrates reconstruction results at sparsity ratios of 0.1, 0.3, and 0.5, the network architecture's design characteristics suggest it can maintain good performance under higher sparsity or specific sparse patterns. This study verified the effectiveness of signal reconstruction, but has not yet involved the simulation of wave propagation in different strata. However, the reconstructed data provides a reliable foundation for subsequent in-depth research. In the future, research on formation wave propagation and inversion will be carried out based on this to respond to the industry's demand for geological analysis. Conceptualization, J.R.; methodology, J.R. and C.X. ; validation, J.R. and M.C. ; formal analysis, X.S. ; investigation, X.H. and X.Z. ; data curation, S.L. ; writing--original draft preparation, J.R. and X.H. ; writing--review and editing, X.S. ; visualization, J.R.; supervision, X.D. and M.C. ; project administration, M.C. ; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript. This paper is partly supported by the Sichuan province natural science fund project (2025 ZNSFSC0477,2024 NSFSC2042), The Opening Fund of Intelligent Perception and Control Key Laboratory of Sichuan Province (2024 RYY01), and the Innovation Fund of Postgraduate, Sichuan University of Science and Engineering under Grant No. Y2024288. Not applicable. The data supporting the findings of this study are subject to privacy and therefore cannot be shared publicly. However, interested parties may contact the corresponding author for further details. All authors declare no competing financial interests or personal relationships that could have influenced the work reported in this study.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 3 Association Rule Mining Algorithm
### Comparison of Three Algorithms  
Theoretically, Eclat is the best among all three algorithms as it only scans the whole database once. FP-growth is the second best since it scans database twice [38], while Apriori takes the longest time since it scans the database when generating frequent item sets [39]. To evaluate the performance of the three algorithms, five DTU fault databases of different sizes (100, 500, 1000, 5000, 10,000) were constructed, and simulation was conducted in Python 3.7 on a PC with an Intel Core i5-9400F CPU running at 2.90 GHZ and with 8.0 GB of RAM, the figure was plotted in MATLAB, version R2020b (The MathWorks, Inc., Natick, MA, USA). Time usage of three algorithms is shown in Figure 4.  
Figure 4: Time usage of the three algorithms in terms of different data sizes.  
Figure 3: Procedure of the Eclat algorithm.  
As can be seen from Figure 4, Eclat had better performance than FP-growth and Apriori. When the number of data is lower than 1000, the time usage difference of the three algorithms was nearly negligible. However, as the data size increased, the difference became much more obvious. When the number of data reached 10,000, the time usage of FP-growth was 15 times longer than Eclat, while Apriori was 103 times longer than Eclat. This outcome conformed to the theoretical analysis of three algorithms: Eclat takes the least time as it only scans the database once, while FP-growth and Apriori take longer due to additional scanning.  
The Apriori algorithm is the easiest and most direct approach to mining frequent item sets, but it needs to generate a large number of candidate item sets and repeatedly scan the database, so it is rather costly. FP-growth deals with this problem by introducing a frequent pattern tree; by this means, it only scans the database twice. However, it also has disadvantages such as complexity and large memory occupation. Compared with Apriori and FP-growth, Eclat requires less time and is thus more efficient. However, as Eclat needs to repeatedly save item sets, it needs more memory space.  
Strengths and weaknesses of three different algorithms are summarized in Table 4.
# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 5 Conclusions  
The successful resolution of event coreference in Arabic holds the potential to significantly benefit a range of applications, including information extraction, sentiment analysis, document summarization, and machine translation. Nevertheless, this task encounters substantial challenges within the Arabic linguistic landscape.  
The intricate lexical and morphological variations in Arabic, coupled with the frequent omission of subject pronouns, contribute to the complexity of event coreference resolution. Moreover, the extensive utilization of ambiguous pronouns and events ellipses further amplifies this complexity. Additionally, the scarcity of annotated corpora tailored to Arabic event coreference presents a major hindrance to the development of specialized systems.  
Arabic event coreference remains an underexplored area compared to languages with more established resources. The lack of specialized systems and comprehensive datasets highlights the need for concerted efforts in constructing suitable corpora. Addressing this gap, we have put forth a schema for annotating Arabic event coreference. This schema is designed to effectively capture the nuanced relationships between events and thereby provide crucial support for the development of advanced coreference resolution systems.  
In the future, work will be conducted on the resolution of event conflicts, which often involves entities such as individuals and organizations as participants. The integration of entity coreference resolution with event coreference can result in knowledge graphs or databases that are more comprehensive and coherent. By leveraging shared context, joint entity and event coreference resolution can also improve accuracy.  
Conceptualization, M.A. and M.K.; methodology, M.A.; software, M.A. and O.S.D.O.; validation, M.A., M.K. and O.S.D.O.; formal Analysis, M.A., M.K. and O.S.D.O.; investigation, M.A., M.K. and O.S.D.O.; resources, M.A., M.K. and O.S.D.O.; data curation, M.A., M.K. and O.S.D.O.; writing--original draft preparation, M.A. and M.K.; writing--review and editing, M.A.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IF2/PSAU/2022/01/21848).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Data and Methods
### Hybrid Coordinate Ocean Model (HYCOM)  
HYCOM is an assimilated model which, through the three-dimensional variational Navy Coupled Ocean Data Assimilation (NCODA) system, incorporates satellite observations as well as Argo float, CTD and Expendable Bathythermograph (XBT) measurements to forecast ocean variables such as salinity, currents and temperature among others [33]. The U.S. Navy uses HYCOM on a daily basis as part of their Global Ocean Forecasting System (GOFS) as does the National Oceanic and Atmospheric Administration (NOAA) at the National Centers for Environmental Prediction (NCEP) [33]. The HYCOM data used in this study is from GOFS 3.1 and experiment 93.0, which has a 0.08\({}^{\circ}\) spatial resolution and a 3 h temporal resolution. In GOFS 3.1, SSS is initialised using climatology from the Generalized Digital Environmental Model (GDEM4). In-model relaxation to GDEM4 climatology occurs at locations where the model salinity values have a less than 0.5 psu difference from climatology salinity values. This is intended to improve model SSS calculations at river outflow areas [34].  
NCODA runs on a regular basis at the Fleet Numerical Meteorology and Oceanography Center (FNMOC) as well as at the Naval Oceanographic Office (NAVOCEANO) and it supplies HYCOM with input parameters for SSS, SST and currents [19]. To do this, ocean observations are compiled and put through specific quality control methods. The quality-controlled data are then ingested by NCODA, which is run with data from the previous HYCOM forecast. The updated NCODA output is then used to initialize the final HYCOM simulation. HYCOM's fine resolution accurately resolves mesoscale ocean behavior and fast flowing western boundary currents [18]. These elements have made the model one of the favored choices for researchers to examine freshwater fluxes and salinity budgets [35] and to validate satellite salinity retrievals [14; 21]. Due to the inability of Argo floats (one of the model's main sources of in situ data acquisition) to record measurements at areas close to land, HYCOM may have an issue recognizing freshwater discharge in these locations [21]. Overestimations of salinity by HYCOM in certain areas may also be due to deficiencies in the climatological forcing of the model, specifically in its ability to represent freshwater fluxes in its output [35; 36].