esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### WEC-Sim and Gazebo Descriptions  
The equations of motion for forces acting on a body as solved by both the _WEC-Sim_ and _Gazebo_ models can be expressed as:  
\[m\ddot{X}(t)=F(t) \tag{1}\]  
where \(m\) is the mass matrix, \(\tilde{X}(t)\) the 6-degree-of-freedom (DOF) acceleration vector, and \(F(t)\) are the 6-DOF total forces on the body. Both of these models represent hydrodynamic forces based on linear wave theory which assumes the fluid flow is inviscid, incompressible, and irrotational [8]. This theory also assumes that the wave height is considerably smaller than both the wave length and still water depth. It is important to note that linear wave theory for waves steeper than 0.01 has limitations, and potential inaccuracies may arise in simulation outputs; however, it is widely accepted in practice [9]. The potential flow on a floating body can then be described as the sum of the radiated, diffracted, and incident potential functions. These assumptions and linearization allow for \(F(t)\) to be represented as a superposition of forces on the body:  
\[\begin{split} F(t)&=F_{exc}(t)+F_{rad}(t)+\dots\\ &+F_{B}(t)+F_{m}(t)+F_{PTO}(t)+F_{Drag}(t)\end{split} \tag{2}\]  
The hydrodynamic and hydrostatic forces on the body are the initial three terms on the right-hand side of (2). The excitation force \(F_{exc}(t)\) combines the diffraction force (resulting from the disturbance of waves by the floating body) and the incident force, expressed as the Froude-Krylov force (resulting from the pressure field of undisturbed waves) [5]. The radiation force \(F_{rad}(t)\) is included as an added mass and wave damping term to describe the instantaneous impact of the bodies' own motion on the surrounding fluid. The buoyancy force \(F_{B}(t)\) arises from the body's attempt to return to equilibrium and is related to the hydrostatic stiffness and displacement of the body. The calculation of \(F_{exc}\), \(F_{rad}\), and \(F_{B}\) depends on frequency-hydrodynamic parameters determined by boundary element method (BEM) solvers. In this study, the outputs of the BEM solver _WAMIT_[10] were used in both the _WEC-Sim_ and _Gazebo_ models for calculating these parameters.  
This study incorporates supplementary forces acting on the body, as indicated by the final three terms on the right-hand side in (2). The mooring force \(F_{m}(t)\) in this study is represented as a linear quasi-static mooring stiffness matrix [5], and the PTO force \(F_{PTO}(t)\) results from the electro-hydraulic PTO affecting the body and is represented uniquely by each model. \(F_{Drag}\) is the viscous drag of the various bodies moving through the water, proportional to the square of the velocity.
## 3 Analysis of Calculation Results
### Wind Turbine Structural Characterization
#### 3.1.1 Blade Surface Deformation  
The deformation of the blades with different numbers of layers is shown in Figure 4, and \(R\) is the radius of the wind turbine. As can be seen from Figure 4, the deformation of the blade with different numbers of layers gradually increases along the leaf root to the leaf tip, and the deformation of the leaf tip part is the largest. The deformation in the leaf center and leaf root position is relatively small, and the volatility of the deformation data is also relatively small, among which, the deformation in the leaf root position is the smallest and almost negligible, which is consistent with literature [18]. With the increase in the number of lay-up layers, the growth trend of deformation along the blade spreading direction is gradually flattened, and the decrease in the maximum deformation at the tip of the leaf  
Figure 3: Comparison of deformations.  
is also gradually reduced. The deformation at the tip of the blade is 3.445 mm when the number of layers is 16, the deformation at the root of the blade is 0.058 mm at the minimum, and the deformation along the leaf spread increases by 3.387 mm. The deformation at the tip of the blade is only 1.403 mm when the number of layers is 44, the deformation at the root of the blade is 0.027 mm at the minimum, and it increases by 1.376 mm along the leaf spread. The blade with 16 layers is 2.45 times more deformed at the tip than the blade with 44 layers, and the deformation increase along the blade spread is 2.46 times more deformed than the blade spread. This indicates that when the wind turbine blade is subjected to aerodynamic force, the tip position is subjected to the greatest disturbance. With the increase in the number of the blade's spreading layers, the mass of the blade becomes larger, the gravity load of the blade increases, the stiffness becomes larger, and the blade deformation decreases.
## 3 Dataset and Methods
### Dataset
#### 3.1.2 Seismic Data  
The 3D seismic dataset consists of pre-stack near (5-15 degree), middle (15-25 degree), and far (25-40 degree) migrated angle stacks. The 3D seismic dataset was acquired and processed in 2002 and covered a 266 km\({}^{2}\). The record length of data is 5 s. The quality of seismic data ranges from good to fair. Most of the reflectors are not continuous in the deeper section. They are poorer in resolution due to the effects of gas clouds from the shallower reservoir. A summary of seismic processing flow is:  
1. Navigation merge;
2. Designature;
3. Geometrical spreading amplitude compensation;
4. Swell noise attenuation;
5. Q compensation;
6. Predictive deconvolution;
7. Radon demultiplex;
8. 3D interpolation and regularisation;
9. Velocity analysis;
10. Pre-stack time migration;
11. Sort to angle gathers.  
The parameters above were applied to optimize the image of the seismic section up to the zone of interest, which is the H Group. The left-over multiples in the deeper section did not affect our results.  
Figure 4a,c demonstrate the map of seismic geometry with well locations and migrated seismic section view, respectively.
# Stationary 3D Scanning System for IoT Applications
###### Abstract  
In various types of industrial applications, such as reverse engineering, machine operation, technical metrology, or modern factory maintenance, it is important to have systems that enable the quick and easy scanning of selected mechanical parts. This study presents the design process and analysis of a low-cost, 3D scanning system which can be used in industrial applications. The system collects point cloud data using an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. Communication with the system is enabled through a serial interface and a dedicated window application, allowing users to monitor and adjust scanning parameters. The output data in the form of a point cloud are saved in a text file in the scanner's controller memory and then sent wirelessly to an external device, e.g., cloud and/or a diagnostic controller. The electronic system is equipped with a radio module that can be used to communicate with other devices in line with the idea of the Internet of Things and the concept of Industry 4.0. The results of the study are based on the accuracy of the three-dimensional digitization of the tested object and on the determination of the average measurement uncertainty.  
3D scanning; 3D scanner; reverse engineering; point cloud; scanning system; IoT; Industry 4.0
# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 3 Methods  
For text-based CAPTCHA designers, it is necessary to balance both the resistance mechanisms against web attacks and the usability of website users [50]. To increase anti-attack ability without reducing the user's recognition time, CCT and deformation are used in the CAPTCHA design [51]. As a result, a few consecutive letters may be difficult to distinguish by the naked eye, such as two back-to-back letters V, as shown in Figure 2. To avoid confusion, designers try to reduce confusing characters when designing CAPTCHAs, resulting in Markov Transition Probabilities (MTPs) between adjacent characters. For this reason, some datasets present the character sequence dependence between two consecutive characters, such as the public dataset M-CAPTCHA [52]. For these CAPTCHAs, break models must consider how to process this correlation between adjacent characters.  
As analyzed previously, every CAPTCHA image contains noise, interference, deformations, overlapping, and CCT. To address these problems for text-based CAPTCHA breaking, a model called Adaptive CAPTCHA is raised that utilizes filters and CRNN, which borrow part of the structure from Deep CAPTCHA, as shown in Figure 3. Compared with Deep CAPTCHA, the proposed model has fewer model parameters, fewer convergence epochs, and higher recognition accuracy. Pos T, Pos 0, Pos 1, Pos 2, and Pos 3 represent different output positions of the model. These different positions can be short-circuited using residual connections, potentially improving the training speed [53].  
In Adaptive CAPTCHA, the parameter settings of the first three convolutional layers and FC layers are completely consistent with Deep CAPTCHA. Unlike Deep CAPTCHA, which uses fully connected layers with a huge number of parameters after conv3, our model introduces CRNN based on two convolutional layers and one LSTM layer to replace the FC layers. The subsequent experiments show that replacing the full connected layers can significantly reduce the number of parameters. Additionally, LSTM can model the correlation between CAPTCHA characters, thereby improving ASR.  
The verification code is subject to interference and noise., and the filter module is specially designed to address the lack of processing in the original Deep CAPTCHA. However, the inclusion of the filter layers must be adjusted according to the level of image noise. Otherwise, it may negatively impact the recognition of the characters.
## 4 Results and Discussion  
### Spatiotemporal Evolution of Land Subsidence  
The annual average land subsidence velocity in Dangshan County from 2018 to 2024 exhibits clear spatial heterogeneity (Figure 4). A pronounced subsidence funnel is centered near the county-level administrative center, with the maximum subsidence rate exceeding 80 mm/a. The subsidence intensity gradually weakens with increasing distance from the center, forming a concentric zonal pattern. The core subsidence area is mainly distributed within a 5 km radius, where the velocity exceeds 60 mm/a. The surrounding zones show moderate to mild subsidence, typically ranging from 10 to 50 mm/a. In contrast, the peripheral areas of the county are relatively stable, with annual velocities close to zero. This pattern suggests a strong correlation between land subsidence and human activities concentrated in urbanized zones, particularly groundwater over-extraction. The spatial distribution further indicates that subsidence impacts are localized but intense, warranting targeted mitigation efforts in the most affected regions.
## 4 Simulation Results
### Applications of RTC France PV
#### 4.1.1 Case 1: SD Model of RTC France PV
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \(\mathbf{I_{ph}}\) **(A)** & \(\mathbf{I_{sd}}\) **(\(\mathbf{\mu\)A})** & \(\mathbf{n}\) & \(\mathbf{R_{sh}}\) **(\(\mathbf{\Omega}\))** & \(\mathbf{R_{ss}}\) **(\(\mathbf{\Omega}\))** & **RMSE** \\ \hline MGA & 0.760776 & 3.23 \(\times\) 10\({}^{-1}\) & 1.481184 & 53.71852 & 0.036377 & 9.8602 \(\times\) 10\({}^{-4}\) \\ \hline RIME [49] & 0.760776 & 3.23021 \(\times\) 10\({}^{-1}\) & 1.481184 & 53.71865291 & 0.036377096 & 9.9755 \(\times\) 10\({}^{-4}\) \\ \hline PGJAYA [50] & 8.2167 & 0.002284 & 58.1742 & 773.8117 & 0.3435 & 1.5455 \(\times\) 10\({}^{-4}\) \\ \hline MPA [44] & 8.184927 & 7.94459 \(\times\) 10\({}^{-2}\) & 1.285180059 & 92.14823504 & 0.004537611 & 1.487 \(\times\) 10\({}^{-2}\) \\ \hline JFS [44] & 8.193182 & 4.72 \(\times\) 10\({}^{-2}\) & 1.250052 & 14.97462 & 0.004679 & 9.477 \(\times\) 10\({}^{-3}\) \\ \hline GO [56] & 8.192967 & 4.31808 \(\times\) 10\({}^{-2}\) & 1.244346 & 15.103921 & 0.004710 & 8.515347 \(\times\) 10\({}^{-3}\) \\ \hline HFAPS [57] & 8.1992 & 0.154161 & 74.5795 & 1448.2590 & 0.2396 & 4.9863 \(\times\) 10\({}^{-2}\) \\ \hline CPMPSO [47] & 8.21689146 & 0.00224195 & 1.07641028 & 763.535149 & 0.34381405 & 1.53903 \(\times\) 10\({}^{-3}\) \\ \hline EHHO [53] & 8.2224 & 0.000001 & 80.6915 & 1806.0252 & 0.1835 & 5.9507 \(\times\) 10\({}^{-2}\) \\ \hline FPSO [20] & 8.2186 & 0.001436 & 56.9854 & 130.2813 & 0.2409 & 2.8214 \(\times\) 10\({}^{-2}\) \\ \hline PSOGWO [48] & 8.2132 & 9.6768 & 1.7463 & 38.8968 & 0.0011 & 1.2700 \(\times\) 10\({}^{-1}\) \\ \hline EO [44] & 8.209153 & 2.85 \(\times\) 10\({}^{-2}\) & 1.218068 & 7.714703 & 0.004815 & 2.888 \(\times\) 10\({}^{-3}\) \\ \hline PSO [54] & 8.2027 & 2.8852 & 1.6052 & 33.8855 & 0.0019 & 1.0195 \(\times\) 10\({}^{-1}\) \\ \hline MVO [52] & 8.2527 & 0.063908 & 69.2388 & 134.4813 & 0.1341 & 8.3800 \(\times\) 10\({}^{-2}\) \\ \hline BMA [45] & 8.1950 & 3.1015 & 1.6130 & 100.0000 & 0.0019 & 1.0244 \(\times\) 10\({}^{-1}\) \\ \hline LAPO [46] & 8.2155 & 8.1491 & 1.7258 & 5.0000 & 0.001 & 1.3813 \(\times\) 10\({}^{-1}\) \\ \hline EMPA [44] & 8.21195 & 3.59 \(\times\) 10\({}^{-2}\) & 1.232551 & 7.560713 & 0.004742 & 3.847 \(\times\) 10\({}^{-3}\) \\ \hline NLBMA [51] & 8.1467 & 0.0022 & 1.0839 & 5.0000 & 0.0045 & 3.3610 \(\times\) 10\({}^{-2}\) \\ \hline HEAP [44] & 8.200974 & 4.49 \(\times\) 10\({}^{-2}\) & 1.246924 & 11.87468 & 0.004696 & 7.425 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Acquired parameters for the R.T.C. France PV module (SD model) using MGA compared to the reported results.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 3 Experiments and Results  
### Dataset  
To validate the proposed algorithm, a set of GF-4 sequential images has been used. The imaging area is in the East China Sea as shown in Figure 3, and detailed information of these data sets is given in Table 1. There are 5 frames, with a footprint of approximately 500 km \(\times\) 500 km. Each data has five spectral bands, and the size of each band is 10,240 pixels \(\times\) 10,240 pixels with a resolution of 50 m. The level of all images is level 1A, which is not corrected geometrically. As the imaging area is so large and there are hundreds of ships in a single image, it is hard to evaluate our algorithm in the whole region. In this paper, we select two regions of interest (ROI) bounded by red lines to validate the tracking method. The size of each ROI is 2000 pixels \(\times\) 2000 pixels (about 100 km \(\times\) 100 km). Meanwhile, AIS data (ground truth) used for position correction is mainly obtained from the shore-based AIS stations. The time range is from 2017-03-09 03:40:00 to 2017-03-09 04:10:00, and the refresh rate of data is up to 1 min.  
Figure 3: Coverage area of GF-4 satellite image with AIS data. The locations of AIS reports are marked by blue dots. The imaging area is bounded by green lines while two ROIs are bounded by red lines. ROI 1 and ROI 2 are labeled with 1 and 2, respectively.
## 4 Results and Discussion
### Mobile Application for Educational Similar
#### 4.1.2 System Architecture  
In Figure 2, the architecture of the proposed application is shown. The architecture takes as an input the following data:  
1. [leftmargin=*]
2. Meal parameters: these parameters are related to carbohydrate intake (in grams) and the day's carbohydrate intake schedule.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Processor** & **RAM** & **Android Version** \\ \hline Galaxy S2 & Dual-core 1.2 GHz Cortex-A9 & 1 GB & 4.1 \\ Galaxy S4 & Dual-core 1.7 GHz Krait 300 & 1.5 GB & 5.0.1 \\ Polaroid Tab & Dual-core 1.0 GHz Broadcom 21663 & 1 GB & 4.2.2 \\ Galaxy Tab 10.1 & Quad-core 2.3 GHz Krait 400 & 3 GB & 5.1.1 \\ LG G3 Stylus & Quad-core 1.3 GHz Cortex-A7 & 1 GB & 5.0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Devices utilized for testing the proposed application.  
Figure 1: The diabetic patient model utilized for the implementation of simulations reported in this work.  
2. Insulin dose parameters: these parameters are related to the type (fast or slow actions) and the dose of insulin (in units).
3. Exercise parameters: these parameters are related to the quantity and intensity of exercise.
4. Simulation configuration: these parameters are related to the simulation's duration and the type of plot to be generated.
5. The architecture contains the following blocks:
6. Simulation variables container: this block stores data required to be accessed and shared by all model blocks. It contains three profiles: meal dosing, insulin dosing, and exercise quantification.
7. Meal model block: this block takes as input the meal profile and generates as output the glucose obtained according to the carbohydrate intake grams.
8. Insulin model block: this block takes as input the insulin dosing profile and generates as output the quantity and duration of insulin in the blood flow.
9. Exercise model block: this block takes as input the exercise dosing profile and generates as output the blood redistribution volume according to the quantity of exercise.
10. Simulation loop block: this block updates each model block with required data from others and controls the simulation's start and end.
11. Plot generation module: this block generates data from the simulation to be used for generating a plot.
12. Plot visualization module: this block takes as input the vector generated by the plot generation module and displays the plot.  
Finally, the architecture generates as the output the plot with the information according to the user's input.  
Figure 2: The system architecture.
# Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications
## 3 Architectures of Crowdsourcing and Proposed Crowdsourcing Internet of Things
### Proposed Architecture of Crowd IoT
From our study, it is observed that the choice of crowdsourcing structure, components, and type of architecture depends on the type of application. Applications with a focus on data storage, task management and distribution, security, and privacy adopted the centralized architecture. On the other hand, applications with high concerns for energy consumption, network connectivity and coverage, high processing speed, load balancing, security, and privacy utilized decentralized architecture. Security is a major challenge for Crowd IoT implementations due to the lack of common standard and architecture for IoT security and the requirement to handle data in heterogeneous networks. Security, privacy, and trust components provide services that meet these requirements in the Crowd-IoT. The trustworthiness, privacy, and security for crowdsourcing in IoT will be further elaborated in the next section (Section 3).
## 3 Results and Discussion
### Variations in ARGs and LRGs After Remediation via Multiple Methods
#### 3.1.2 Antibiotic Resistance Variations  
The primary mechanisms of antibiotic resistance in Pb-contaminated soil were identified as efflux pumps and antibiotic deactivation (Figure 3b). A comparison of BES, Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), and BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) revealed changes in ARGs resistance mechanisms following synergistic remediation with composite agents and BES. The abundance of ARGs involved in antibiotic deactivation was notably lower in BES and BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) compared to Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), indicating that bioelectrochemical reactions suppress antibiotic deactivation (Figure 3c). In contrast, ARGs associated with cellular protection mechanisms were more prevalent in BES and BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\), suggesting that bioelectrochemical processes may enhance cellular protection ARGs proliferation. Further analysis of ARGs resistance mechanisms under varying initial Pb concentrations and pH levels post-synergistic remediation (Figure 3c) showed that the majority of ARGs in BES-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) were linked to cellular protection, while BES5-Imvite-MgO-Ca(H\({}_{2}\)PO\({}_{4}\))\({}_{2}\) displayed a higher relative abundance of ARGs related to antibiotic deactivation. Notably, an initial pH of 5 significantly increased the relative abundance of ARGs associated with antibiotic deactivation.
# Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges
## 2 Evolution and Enablers of Industrial Internet of Things
### Industry 4.0-Building Blocks
#### 2.5.7 Fog Computing  
Fog computing or \"fogging\" is an extended form of cloud computing, in respect of industrial revolution giving applications and services (low latency and high processing) to autonomous heterogeneous devices inside an industry [36]. The idea is to bring processing, storage, maintenanceand intelligence control to the proximity of data devices. Inside industry 4.0, there is critical requirement of real-time services with high data processing, maximum capacity and scalability. Fog computing gives the best solutions for such an environment because of its significant benefits over cloud computing. Extension of cloud computing, aims to minimize the burden on the cloud by introducing network edge computing concept.  
For industrial automation, real-time services and decision making processes require low latency and enhanced cache memory. Required performance parameters are mobility, real time applications, low-latency, location-awareness, number of nodes and cache-enabled edge devices on this basis of geographical distribution. Virtualized nodes frequently known as cloudlets or fog nodes are placed between clouds of internet and end user devices. Fog computing provides services and applications as a cloud does with better QoS parameters performance covering critical requirements of IIoT. Important advantages of fog computing that influence its use for IIoT are:  
* Data storage on network edge nodes eliminates the transmission delay by removing the need for accessing data from far-away clouds.
* Fog computing supports to process and analyze the data on faster speed for IIoT applications.
* Data storage on edge nodes will reduce the processing and computing delay.
* Cache enabled nodes will prevent transmission of irrelevant information over the network.
* Can give support to all IoT applications e.g., smart grids, smart cities, D2D, Vehicular Ad-hoc networks (VANETS) using edge networking concept.
* Provides filtered and required interaction between end devices and cloud service providers.  
Fog computing is the building stone to provide solutions for more efficient, effective and manageable communication way for the massive number of smart IoT devices in the near future. Fog computing with extra features as compared to cloud computing in terms of latency, security, location awareness, location, and number of server nodes, real-time connectivity and mobility is a promising enabler for industrial automation.