esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation
### Evaluation of Numerical Performance (in Terms of DOFs Reduction)  
This section describes the numerical performance of the proposed method in terms of DOFs reduction. For numerical models such as FE model or FE-BE coupling model, DOFs is the number of unknown variables of the matrix equation for calculating the dynamic behavior of a physical system. The higher the DOFs, the greater the computational effort. Therefore, from the reduction rate of DOFs, we evaluated how high numerical performance the method proposed in this study has compared to the conventional full FEM. We modeled cylindrical arrays as shown Figure 4a and configured four different sets of row and stave numbers, \(N_{C}\) and \(N_{R}\) to consider various sizes of problems: \(N_{C}=24\) and \(N_{R}=4\); \(N_{C}=36\) and \(N_{R}=6\); \(N_{C}=48\) and \(N_{R}=8\); and \(N_{C}=60\) and \(N_{R}=10\). We also considered four different analysis frequencies represented by _ka_: _ka_ = 2, 3, 4, and 5. The following are basic schemes for generating the FE-BE coupling model for the proposed method and the FE model for the conventional method:  
1. The size of one side of the acoustic element is set to about \(\lambda/8\)[37]. 2. The FE of the structure should be generated so that the mode shapes that are within 2 times the maximum analysis frequency can be well represented. 3. The shape of the acoustic window is assumed to be an oblate spheroid, and its size is set to increase in proportion to the array size. Here, the sizes of the half-length of the principal axes are assumed to be \(a_{window}=1.5R_{Array}\) and \(c_{window}=1.7N_{R}(a+d)/2\). \(R_{Array}\) is the radius of the array. 4. For the conventional full FEM, an adequate amount of space is required between the array and the outer surface in order to apply an open boundary condition to the surface of the acoustic region. The free space of the open boundary is assumed to be set to 300% of the size of the structure. Thus, the radius of the open boundary is three times the half-length of the principal axes. The DOFs for the two models (FE-BE coupling model and the conventional FE model) were obtained using the general-purpose software's DOF calculation function. The DOFs required for a reliable solution are summarized in Table 3. The DOFs of the proposed method are denoted by \(DOF_{FE-BE}\), and that of the conventional full FEM are denoted by \(DOF_{FullFE}\). \(DOF_{FullFE}/DOF_{FE-BE}\) is an indicator of how many times the DOFs have been reduced when using the proposed hybrid method. Figure 16 shows the ratio of DOFs of the proposed method to the conventional method. The dashed-dotted line, the dotted line, the broken line, and the solid line represent the cases of the transducer arrays corresponding to \"\(N_{C}=24\) and \(N_{R}=4\)\", \"\(N_{C}=36\) and \(N_{R}=6\)\", \"\(N_{C}=48\) and \(N_{R}=8\)\", and \"\(N_{C}=60\) and \(N_{R}=10\)\",
## 4 Brief Literature Survey on ANNs Applied to Time Series Modeling
### Combining Feedforward Neural Networks and Particle Swarm Optimization for Time Series Forecasting
#### 4.1.1 Particle Swarm Optimization for Artificial Neural Networks
[PERSON] [93] conducted a couple of experiments to compare ISSO against five other ANN training methods: BP, GA [118], basic PSO, a PSO variant called cooperative random learning PSO [119], and regular SSO. * Experiment number 1 tests all six training algorithms on a special ANN architecture called _single multiplicative neuron_ (SMN), which is similar to an FFNN but consists of an input layer and an output layer with a single processing unit (this single neuron has a logistic activation function but multiplies its inputs instead of adding them; additionally, there is a bias for each input node, in contrast to FFNNs, which contain just one bias in the input layer). * Experiment number 2 also tests all six training algorithms, but this time on a regular FFNN with one to six hidden neurons. Time series employed in this experiment were as follows: Mackey-Glass chaotic time series [120], Box-Jenkins gas furnace [121], EEG data [122], laser-generated data, and computer-generated data [123]. All time series values were transformed to be in the interval \([0.1,0.9]\) in order to avoid saturation of neural activations and improve convergence of training algorithms. In both experiments, each model was trained 50 times for each time series; 30 particles/chromosomes were employed in each training session. The training algorithms were allowed to run for 1000 generations. The measures used to compare the results were the mean square error (MSE), standard deviation of MSE test errors, and CPU processing time. According to the author, the results from both experiments showed that ISSO outperformed the other five training algorithms, with the exception of BP, which performed better in experiment 1 when forecasting the laser-generated data. Additionally, the FFNN models produced forecasts that were more accurate than the ones generated by the SMN model.
Common starch modification methods employed in the industry include heat-moisture treatment, annealing, retrogradation, freezing, ultra-high-pressure treatment, glow discharge plasma treatment, genetic modifications, enzymatic reactions, osmotic-pressure treatment, thermal inhibition, gelatinization, etherification, esterification, crosslinking, acid treatment, oxidation, and dual modification. However, those aforementioned starch modification methods can be categorized into four main processes: Genetic, enzymatic, physical, and chemical [26]. Several articles have recently been published on environmentally friendly or green starch modification techniques. For example, [PERSON] and [PERSON] [28] reported the newly emerging green solvent for the modification of starch, whereas [PERSON] et al. [29] studied the green techniques available for starch modification to stabilize Pickering emulsions. Furthermore, [PERSON] and coworkers [30] reviewed \"clean\" or \"green\" alternatives for starch modification and their possible applications. Moreover, [PERSON] et al. [31] presented an overview, including the current investigations on the properties of starch produced via esterification/crosslinking with citric acid. The current review focuses on the emerging starch modification techniques, including enzymatic, physical, and chemical for developing starch-based materials reported in the literature over the past few years. This review mainly discusses the chemical modification techniques of starch and their effect on the thermomechanical properties. In addition, a brief overview of genetic, enzymatic, and physical modification of starch and their main applications is also provided. Finally, the potential applications of the chemically modified starch in packaging, adhesives, pharmaceutical, agriculture, super-absorbents and wastewater treatment are also discussed in this review. Figure 3: Modified starch market share by the application (Reproduced from: Modified starch market size, by product 2014–2025 (USD billion) Grand View Research, Inc. [27]).
## 3 Wave Tank and Field Experiments
### Data Processing  
The radar modulation transfer function \(m^{radar,VV}\), which we denote further for brevity as \(m(\Omega)\), was analyzed in the experiments as follows (see [36]):  
\[m(\Omega)=-\frac{C}{\overline{P}}\cdot\frac{\overline{P(\Omega)U^{*}(\Omega)} }{\left|U(\Omega)\right|^{2}}=\frac{1}{\overline{P}}\frac{\overline{P(\Omega)Z ^{*}(\Omega)}}{\left|Z(\Omega)\right|^{2}}\cdot\frac{sh(kh)}{\kappa}=\left|m \right|\cdot e^{i\varphi_{m}} \tag{20}\]  
In Equation (20), \(P(\Omega)\), \(U(\Omega)\) and \(\left|Z(\Omega)\right|\) denote Fourier components of the received radar signal power, IW orbital velocity, and pycnocline elevation due to IWs, respectively; the horizontal bar denotes averaging over the IW period. \(\left|m\right|\) is the RMTF magnitude, and \(\varphi_{m}\) is the RMTF phase, which is the difference between the phase of radar backscatter variations and the IW pycnocline elevations. Note that \(\varphi_{m}=0\) if the radar return maxima are located over the minima of the IW orbital surface velocity, or equivalently, over the crests of the pycnocline elevations. The duration of time records in the experiment were about 10 min, and the sampling rate was 1250 Hz. The radar intensity over a time interval of 1-2 s was calculated when integrating the radar Doppler spectrum in the frequency band of 50-150 Hz. The obtained time series of the radar intensity and pycnocline displacement oscillations were divided into 3 min realizations; for each realization, the Fourier spectra were calculated, and then RMTF was calculated according to Equation (20).  
To estimate the errors of MTF values, the coherence \(\Upsilon^{2}(\Omega)\) was calculated as (see [36]):  
\[\gamma^{2}(\Omega)=\frac{\overline{\left|P(\Omega)U^{*}(\Omega)\right|^{2}}}{ \left|P(\Omega)\right|^{2}\left|U(\Omega)\right|^{2}}, \tag{21}\]  
Figure 6: Distributions of surfactant concentration over IW profile in the first experimental set; the black curve at the bottom of the figure is the IW profile. The arrow indicates the IW propagation direction, and the curves/dots of different colors correspond to different average surfactant concentrations: 0.8 mg/m\({}^{2}\) (black and green), and 0.6 mg/m\({}^{2}\) (violet). Period, wavelength, and amplitude are 9 s, 200 cm, and 5 mm, respectively.  
The coherence characterizes a ratio between the variance of the radar return fluctuations, correlated with IW and the total variance of the radar return fluctuations [25]. The error of MTF increases with a decrease in the coherence, and estimates of the magnitude and phase of the MPF are unreliable if the coherence is small.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Odometry Estimation  
In the realm of deep learning methods for odometry estimation, particularly from point cloud and image representations, a central challenge revolves around identifying pertinent features and effectively tracking them across successive frames. Should these relevant features be successfully identified and consistently tracked, a differentiable point matching mechanism like the Kabsch algorithm [108] can be employed to determine the transformation matrix \(T\in SE(3)\) between the frames. Yet, the crux of the matter remains in the ability to pinpoint these features and establish accurate point associations, constituting the core challenge in odometry estimation derived from sensor data. The capacity of models to extract keypoints from both point clouds and images has undergone thorough investigation, particularly in the context of VSLAM and lidar-based SLAM methods [8; 28; 29]. Notably, when compared to other sensors, radar encounters a distinct challenge due to the prevalence of sparse and unreliable data. A common method for determining transformation parameters involves a scan-matching approach, typically assuming that \(T\in SE(2)\). The optimal ego-motion parameters are estimated by learning a mask that maximizes the overlap of the noise-free representation, and trained with a loss of the form [8]:  
\[\mathcal{L}=||\hat{t}-t||_{2}-\alpha||\hat{R}R^{T}-\mathbf{I}||_{2} \tag{4}\]
## 3 Simulation Results
### The Simulation Setup  
The performance of the proposed algorithms is evaluated via 5000 episodes. We define two error metrics: the EDM error and the localization error. The EDM error quantifies the deviation between the denoised EDM and the original EDM. Specifically, it is computed by first taking the Frobenius norm of their difference and then normalizing it by the Frobenius norm of the original EDM, with the final result averaged over all experimental trials. This metric provides a measure of the accuracy of the denoised pairwise distances. Meanwhile, the localization error represents the average localization error across all experimental trials, capturing the overall accuracy of the proposed method in reconstructing spatial coordinates.  
In the simulations, \(N\) is set to 30, and the entire nodes are randomly distributed over a \(100\times 100\)-sized space. Furthermore, the resolution of the distance measurements is assumed to be 10. Next, we set the variables of the noisy measurements, which are defined in Section 2 A, as follows: \(B_{N}\)\(\sim\)\(\mathcal{N}(0,5)\), \(B_{U}\)\(\sim\)\(\mathcal{U}(0,10)\), \(B_{NLoS}\)\(\sim\)Ber(0.5) (except for Section 3C), and \(R_{NLoS}=25\). In addition, \(\delta_{ij}\) in (3) follows Ber(0.9) for all \(i,j\), i.e., 10% of the distance measurements are missed on average. To clearly confirm the performance in EDM denoising, all of the EDM measurements follow the above assumptions in terms of the statistical parameters for error modeling and the resolution of the distance measurements, irrespective of whether the pair measuring their distance contains the reference/target node. Additionally, except for Table 1a, the number of reference nodes (\(P\)) is 18. The classical MDS [23] is utilized as the localization method to estimate the position of the target nodes.  
Throughout all cases for NN optimization, the squared error and the scaled conjugate gradient are applied as the loss function and the optimization method, respectively. We consider a fully connected NN model. Furthermore, the depth of the hidden layer and the number of perceptrons per hidden layer are 2 and 450, respectively. With the exception of Figure 2, the size of the training dataset (\(M\)) was 10,000 for all experiments. We tested on NVIDIA RTX A2000 GPU machines (Santa Clara, CA, USA).
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 3 Experiment and Analysis
### Angle Verification and Analysis  
#### 3.4.1 Experimental Environment  
In order to verify the correctness of the measurement of the pole from different observation directions at a fixed inclination angle, a pole model for simulation was designed and built in the laboratory. The depth camera Percipio DS460 is used to obtain the depth map of the pole.  
Figure 17: Pole angle measurement. (**a**) RGB image; (**b**) Pole point cloud segmentation results; (**c**) Measurement results in the camera coordinate system.  
The pole is fixed on the inclined platform, and the camera is adjusted to the horizontal position. For simplified calculation, the camera coordinate system and the world coordinate system are set to the same coordinate system. The inclination angle is adjusted by the inclined platform every 2\({}^{\circ}\) from 0-10\({}^{\circ}\) and the observation direction is adjusted by rotating the platform every 45\({}^{\circ}\) in a clockwise direction from 0-360\({}^{\circ}\). The average value of five measurements is taken as the final result of this inclination angle and observation direction. The experimental environment is shown in Figure 18. Where, 1. pole model, 2. depth camera, 3. image processing system, 4. rotating platform, 5. inclined platform, and 6. angle measuring instrument.
# Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
## 4 Results
### Number of Parameters  
Both formulas make use of parameters that can be linked to the critical sections of the data, such as storage modulus transition regions or the peak values of the loss modulus and loss tangent. The LBEM method requires 10 parameters to be defined, whereas the polynomial method needs a minimum of 16 parameters for the storage and loss modulus to be evaluated.  
An important quality of the LBEM approach is that the parameters are directly linked to typifying quantities of the data, such as the loss tangent frequency peak location, the curve's growth rate and the loss tangent peak value.
## 3 Validation of the Proposed Numerical Methods
### Experimental Results of the Flow around a Vertical Plane  
In the experimental study of the submarine flow around a vertical plane, data sampling was performed at a frequency of 20 Hz. Considering the asymmetry of the submarine's hull in the _yoz_ plane, the main forces acting on the submarine during vertical plane movement are the longitudinal force (\(F_{x}\)), vertical force (\(F_{z}\)), and pitching moment (\(M_{y}\)). When comparing numerical calculations with experimental results, the average values of these three directions were utilized for the sake of a comparison study. Due to the symmetry of the submarine hull in the _xoz_ plane, the effect of the lateral force (\(F_{y}\)) and the rolling moment (\(M_{x}\)) is generally neglected during vertical plane motion. However, the asymmetric random vortices generated by the vertical plane motion may be the main cause of large yawing and swaying motions, which require real-time data monitoring. Therefore, forces in these three directions were compared using time-history curves.  
Figure 11 illustrates the schematic grid diagrams of the compartment (with sail rudder) and the cross-section of the submarine at different time instants during the flow around the vertical plane with a speed of \(w=1.0\) m/s. The demonstrated results indicate that the implementation of adaptive meshing allows for mesh refinement based on the vortex intensity, leading to a more reasonable mesh distribution and more accurate simulation results. Each case took approximately 50 h to compute on the server, utilizing three blades, each equipped with 128 GB of memory and 20 cores.  
Figure 10: Adaptive mesh for flow around a cylinder at various time instants.
# Image Processing and QR Code Application Method for Construction Safety Management
## 3 Image Processing-Based Worker Location Estimation System
### Image Processing Learning Model
#### 3.2.3 Construction Management DB  
The system recognizes three classes of people, workers and QR codes. At the construction site, the object containing the QR code is the person included in the system DB, which means the project's people. If the QR code does not recognize, it can be divided into two cases. The one is wearing work clothes, and another is not wearing work clothes. If the target is wearing work clothes, the QR code does not recognize or that the target did not include the DB. In the case of another, it is regarded as neither the worker nor the project's people.
# Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures
## 2 Research Methodology
### Theoretical Considerations
#### 2.1.3 Infrared Thermography  
Objects with an absolute temperature above 0 K emit infrared light from their surfaces. The amount of emission is known to have a high correlation with the object's temperature. The temperatures of these objects can be determined from the amount of infrared light emitted from them.  
Infrared light has a wavelength in the range 0.8-1000 \(\upmu\)m. It is an electromagnetic wave that occurs between microwaves and visible light. Infrared light cameras, with elements that can detect electromagnetic waves in the infrared region, can be used in the same manner as normal optical video cameras to obtain the temperature distributions of their targets. This method is known as infrared thermography (IRT). Compared to ultraviolet or visible light, infrared light has long wavelengths and experiences minor reflection or absorption owing to the particles. Therefore, it penetrates the atmosphere to a large extent and can obtain observations at long distances to a certain extent. Because of this, it is used for satellite ground observations, and a variety of research on the applications of IRT is now being actively pursued.
## 3 Results and Discussion
The possible future application of the proposed HR is monitoring glucose levels of human blood. There are many other substances in human blood (e.g., fat, cholesterol) and they all ineluctably have an impact on resonant frequency. However, glucose concentration changes greatly just before and after meals. The glucose concentration in human blood varies significantly over the short term, so it can be modeled as the aqueous solution mentioned above. This means that sensor calibration is needed regularly (e.g., once a week). In this paper, underlying theory, principle and architecture based on a modified HR were proposed for application as a biosensor. As future work, miniaturization, integration and further optimization will be done for practical applications.
## 5 Results and Discussion  
### Influence of the Spacing between the Adjacent Pile and Existing Pile  
To investigate the influence of the distance between the existing and adjacent piles on the existing single-pile response, Figures 14 and 15 show the deformation and bending moment of the existing pile at four different distances (i.e., 1.5 m, 2 m, 2.5 m and 3 m) adopted for calculation at 10 m and 20 m of new pile penetration.  
Figure 14: Influence of the distance between the used and new piles on lateral displacement of used pile.  
Figure 13: Comparison of calculation results between [PERSON] foundation model and [PERSON] foundation model.  
It can be seen that the lateral displacement generated by an existing pile increases significantly as the distance decreases. Furthermore, when the distance is short, the existing pile's lateral displacement grows dramatically with increasing distance. At 10 m pile penetration, when the distance was lowered from 2 m to 1.5 m, the maximum value of lateral displacement of existing piles increased by 5.65 mm, from 12.62 mm to 18.27 mm; meanwhile, when the distance was reduced from 3 m to 2.5 m, the maximum value of lateral displacement increased by only 2.16 mm, from 7.11 to 9.27 mm. At 20 m pile penetration, the highest lateral displacement of the existing pile rose from 15.17 mm to 20.61 mm, with a 5.47 mm rise when the distance is lowered from 2 m to 1.5 m; when the distance was lowered from 3 m to 2.5 m, the highest lateral displacement rose by just 2.06 mm, from 9.78 mm to 11.84 mm (as shown in Table 2). This indicates that increasing the spacing between the existing pile and the new pile has a major impact on minimizing the squeezing effect. Too small a distance will affect the bearing capacity of existing piles.