esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Simultaneous Dual-Resolution Digital Holography System
## 2 Principle and Method
Equation (11) is transformed into the following form:  
Figure 2:3  
Here \(\otimes\) is the convolution operation, \(U_{r1}=\mathrm{FT}(u_{r1})\), \(U_{r2}=\mathrm{FT}(u_{r2})\), \(U_{o1}=\mathrm{FT}(u_{o1})\), and \(U_{o2}=\mathrm{FT}(u_{o2})\). Figure 3 shows a sketch of the Fourier spectrum of the intensity images. Five spectra are distributed on the Fourier domain, corresponding to the eight terms in Equation (12). Sections A contains \({U_{r1}}\otimes{U_{r1}}^{*}+{U_{r2}}\otimes{U_{r2}}^{*}+{U_{o1}}\otimes{U_{o1} }^{*}+{U_{o2}}\otimes{U_{o2}}^{*}\), which are low-frequency components that represent the background in the spatial domain. Sections B and C represent the \({U_{r2}}\otimes{U_{o2}}^{*}\) and \({U_{o2}}\otimes{U_{r2}}^{*}\) parts, respectively, which contain information of the hologram for the large field of view. Sections D and E represent the \({U_{r1}}\otimes{U_{o1}}^{*}\) and \({U_{o1}}\otimes{U_{r1}}^{*}\) parts, respectively. These two parts contain information of the hologram for the small field of view. \({u_{o1}}{u_{r1}}^{*}\) can be obtained by employing an inverse Fourier transform to window the spectrum of \({U_{o1}}\otimes{U_{r1}}^{*}\). Similarly, \({u_{o2}}{u_{r2}}^{*}\) can be obtained using the same method. These results are shown in the following equations:  
Figure 2:4  
Figure 2:5Using the same method, after the object deforms, the distribution of an additional two phases can be obtained, as described in the following equations:  
Figure 2:6  
Figure 2:7  
The relative phase difference in the large and small fields of view due to a deformation resulting from an applied load can then be calculated as follows:  
Figure 2:8  
Figure 2:9  
Through the operation described above, our last goal, i.e., to separate the phase information from the two holographic setups recorded in a single image by a Fourier transform and inverse Fourier transform, is attained, and the deformations of the large and small fields of view resulting from the applied load can then be calculated by the determined phase distribution. It should be emphasized that the direction of the carrier frequency in the holographic system determines the orientation of the spectrum of the hologram after the Fourier transform. A special advantage of holography is that a direction change in the carrier frequency can easily be implemented by adjusting the incident angle of the reference beams; therefore, the spectrum aliasing reported in the shearographic system [20] can be solved easily in the dual-channel holographic system.
## 5 Simulation Debugging
### Simulation Waveforms of Star Chart Generation  
The simulation waveform of target subzone search is shown in Figure 19. In the figure, pos_qin_en is the data input enable signal, zone_no is the current target subzone number, zone_done is the current target subzone search completion signal, and all_done is the whole target subzone search completion signal. It can be observed in the figure that the time from the rising edge of pos_qin_en to the rising edge of all_done is 75.26 us, which is all the time consumed by the generation of a star map.  
In the three parts of optical axis pointing calculation, optical axis pointing coordinate transformation calculation, and star zone search range calculation, a set of attitude quaternions only needs to be calculated once, which takes up a relatively small amount of calculation time. In the three parts of star point coordinate transformation calculation, star point effective judgment, and star point coordinate transformation, every star point data in the subarea range needs to be calculated, and the calculation time takes up a larger proportion. The greater the parallelism of the star point data calculation, the less time consumed by the calculation and the less time consumed by the final star map generation.  
Figure 18: Simulation of star point spatial coordinate transformation waveforms.  
Figure 20 shows the search results of the pixel coordinates and grayscale of the 0 th to 44 th star points. Moreover, pix_cnt is the effective star points pixel counter, pix_x and pix_y are the horizontal and vertical coordinates of the effective star points pixels, and pix_gray is the grayscale of the effective star points pixels. Based on the results in Figure 20, it can be seen that the computation of the star point pixel coordinates and grayscale values is consistent with the MATLAB calculations in Figure 8 above. Because the number of star points within the search area varies, the star map calculation time varies. The higher the number of star points in the target sub-area, the longer the calculation takes.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 4 Poly(3,4-ethylenedioxythiophene)-Based Electrodes  
Poly(3,4-ethylenedioxythiophene) (PEDOT), which is a polythiophene (PT) derivative, is an outstanding conducting polymer for practical applications because of its small bandgap, good optical properties, and high electrical conductivity compared to other conducting polymers [115]. PEDOT exhibits excellent electroactivity in phosphate buffers, making it ideal for biosensor applications, particularly in enzyme matrices for immobilization processes. Most importantly, its mode of action features the direct transfer of electrons during detection [116]. Given these findings, numerous studies have attempted to synthesize PEDOT nanostructures using different electrochemical and chemical oxidative polymerization methods.
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations with Single-Sized Non-Spherical Particles  
#### 3.2.1 Experimental Materials and Methods  
The experimental system for studies with non-spherical particles is shown in Figure 11. To ensure a better balance between the operation of experiments and the reliability of experimental observations, the tank thickness, \(W_{\text{tank}}\), was fixed at 60 mm, while \(H_{\text{w}}\) and \(d_{\text{n}}\) varied from 60 to 80 cm and from 30 to 40 cm, respectively. In total, 46 experimental sets were conducted by employing non-spherical particles with different types (Al, Al\({}_{2}\)O\({}_{3}\), ZrO\({}_{2}\), SS, and Cu), sizes (\(d_{\text{ev}}\) from 0.50 to 5.62 mm), and shapes (dish, triangular prism, cylinder, and irregular shape) [67; 68]. To evaluate the particle-shaped characteristics, the particle sphericities (\(\phi\)) of non-spherical particles were also approximated in accordance with [PERSON]'s equation (see Equation (2)).  
\begin{table}
\begin{tabular}{c c} \hline
**Empirical Constant** & **Value** \\ \hline \(a\) & 1.20 \\ \hline \(b\) & 0.60 \\ \hline \(c\) & 0.90 \\ \hline \(d\) & 1.05 \\ \hline \(k_{\text{B}}\) & 1.01 \(\times\) 10\({}^{-8}\) \\ \hline \(s_{1}\) & 65.5 \\ \hline \(s_{2}\) & 0.263 \\ \hline \(s_{3}\) & 0.441 \\ \hline \(p_{1}\) & 122.451 \\ \hline \(p_{2}\) & 0.555 \\ \hline \(p_{3}\) & 0.625 \\ \hline \(r_{1}\) & 0.016 \\ \hline \(r_{2}\) & 1.295 \\ \hline \(r_{3}\) & 0.012 \\ \hline \end{tabular}
\end{table}
Table 3: Values of the empirical constants in the predictive model for flow regimes and bed shapes.  
Figure 15: Development of regime maps in accordance with the empirical model focusing on flow regime characteristics. (**a**) Single-sized spherical particles [67]; (**b**) single-sized and non-spherical particles with the extension scheme coupled [68]; (**c**) single-sized and mixed-sized spherical particles with the base model using \(d_{\mathrm{a}}\)[69]; (**d**) single-sized and mixed-sized spherical particles with the extended model [69]; and (**e**) boiling condition with releasing heated particles [72].
# Statistical Analysis of the Weight and Center-of-Gravity Position of an Empty Container Ship
###### Abstract  
For the correct execution of the preliminary design of a transport ship, among other things, approximate formulas enabling the calculation of the weight of the unladen ship and the location of the center of gravity are necessary. The aim of the conducted research was to develop approximate formulas for calculating the weight and center of gravity of an empty container ship with a size ranging from 270 _TEU_ to 3100 _TEU_, depending on the basic design parameters: ship speed \(V\), deadweight _DWT_, and number of _TEU_ containers. Since the weight of an unladen container ship has a very large impact on the ship's operating parameters, an additional aim was to obtain regression formulas with greater accuracy than similar formulas published in the literature. Simple and multiple regression methods were used to develop regression formulas. The obtained results were verified on the basis of experimentally measured parameters obtained from built ships. The regression formulas presented in this article are characterized by high accuracy, greater than that of similar formulas published in the literature, and were developed for container ships currently under construction. A novelty of this study is the development of regression formulas for weight classes, which make up the total weight of an unladen ship.  
weight classes; light container ship weight; engine room weight; light container ship center of gravity; ship design Article
## 5 Experimental Validation and Benchmarks
### Quality of Service (QoS) Management
#### 5.4.1 Network Congestion Management  
AI-based models are increasingly being used to predict and manage network congestion, ensuring that traffic flows are optimized to minimize its impact on critical services. In modern communication networks, congestion can arise due to high traffic volume, network failures, or inefficient resource allocation. During periods of congestion, AI algorithms can dynamically reroute traffic, adjust bandwidth allocations, and implement priority rules to ensure that essential services, such as emergency communication, real-time video conferencing, and VoIP, experience minimal disruption [127].  
AI-driven congestion management systems work by analyzing network traffic patterns in real-time, identifying potential bottlenecks, and forecasting when congestion may occur. Machine learning models are trained to detect anomalies in traffic, such as sudden surges in demand, which might lead to congestion. Once these patterns are detected, AI algorithms can take corrective actions, such as dynamically adjusting Quality of Service (QoS) policies, redirecting traffic to underutilized network paths, or prioritizing time-sensitive packets over less urgent data. This proactive approach ensures that critical applications continue to function smoothly, even during high-demand periods [129].  
Furthermore, AI models can continuously learn from network data, improving their prediction accuracy and response strategies over time. For instance, reinforcement learning algorithms can adjust routing and traffic management strategies based on real-world feedback, gradually optimizing the flow of traffic and minimizing congestion-related delays. These adaptive models are particularly useful in complex, high-traffic networks where traditional, static traffic management systems may struggle to keep up with changing conditions.  
AI also enables the integration of congestion management strategies across different layers of the network, from the core to the edge. By analyzing both local and global traffic patterns, AI can coordinate actions across different network segments, ensuring end-to-end traffic optimization. This is especially critical in large-scale networks such as 5G, where seamless management of diverse traffic types (e.g., IoT devices, mobile users, video streaming) is essential for maintaining overall network performance.  
Table 9 summarizes the performance improvements in QoS management applications using AI models.
# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 3 Method  
In this paper, we provide clear implementation procedures to verify the anti-occlusion performance of the three models for single target detection. Specific data sets are generated to facilitate the algorithm evaluation. The format of the data set is Visual Object Class (VOC) type. The images for training are imported to \"labeling\"--a deep learning annotation tool embedded in Anaconda 3. The ground truth anchors, including the classes and coordinates, are drawn manually. All images are collected from the different distances and angles of the target robot. Some images are obtained under unclear lighting and noisy conditions.  
A computer program is designed to read the information of the ground truth anchors from \"XML\" files and generate two \"txt\" files for training and validation. An example can be seen in Figure 1: Manual annotation of the object in Labeling. The graphical user interface contains three key areas which are tool bar, labeling and file list. A toolbar is located at the edge of the screen, and the image waiting to be labelled is displayed in the center of the interface. There is a file list at the low right corner edge. It illustrates the file information of the selected images.  
The main parameters for the performance evaluation in the three deep learning methods include confidence, nms_iou and anchors_size. The confidence is set to 50% to balance the tradeoff and the nms_iou is set to 0.7. The setting of the anchor size is based on the traditional average value in similar cases.  
Three categories of occlusion simulations are tested. One is a simulation based on data augmentation. In the second simulation, test data set two was created. The image in test data set two does not belong to the images in the target robot data set and test data set one. The targets in these images come from different angles and distances. At the same time, some targets are out of range. The occlusion is about 10% to 30% of the target. In the third simulation, we test whether these models can adapt to the change of occlusion.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 4 Estimation Model for Solar Radiation in Fujian
### Estimation Model for Daily Global Solar Radiation
#### 4.1.2 Influence of Sunshine Duration on the Empirical Coefficients  
Aside from the time scale, the actual sunshine duration would also affect the empirical coefficients in the Angstrom-Page equation. The measured average sunshine duration (averaged over one month) since 1951 for four cities in Fujian can be obtained from National Meteorological Information Center, China Meteorological Administration [58]. Hua'an was chosen as an example to illustrate the influence of sunshine duration on the empirical coefficients. The results listed in Table 2 show that the coefficient of correlation obtained using the sunshine duration are largest for Xiamen, which is nearest to Hua'an. Moreover, the root-mean-square error obtained by using the sunshine duration of Xiamen is the smallest. Consequently, the empirical coefficients for cities in the Fujian that did not have measured sunshine duration data can be predicted using the available sunshine duration for the nearest city (Nanping, Fuzhou, Yong'an, or Xiamen).
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
## 2 Materials and Methods
### Viscous Gravity Number (\(N_{gy}\))  
Since the density of the gas used in the WAG was less than that of the reservoir fluid, the difference between the density and gravity causes an override in which the injected gas moves to the upper layer, which reduces the vertical sweep efficiency. The vertical sweep efficiency was affected by the relationship between the viscosity and gravity. The change in the vertical sweep efficiency with the addition of DME was quantitatively investigated using the viscous gravitational number (\(N_{gy}\)), which can be expressed by [38]:  
\[N_{gy}=\frac{\text{time for horizontal flow}}{\text{time for vertical flow}}=\frac{k_{v}k_{rs}\Delta gg\cos\alpha AL}{q_{s}\mu_{s}h} \tag{17}\]  
where \(k_{v}\) is vertical permeability, \(k_{rs}\) is relative permeability, \(\Delta\rho\) is density difference between injected and reservoir fluids, \(g\) is gravitational acceleration, \(A\) is cross sectional area, \(L\) is length of the reservoir, \(q_{s}\) is flow rate of the solvent, \(\mu_{s}\) is solvent viscosity and \(h\) is height of the reservoir.  
If \(N_{gy}=10\), horizontal flow takes ten times longer than vertical flow. The guidelines for determining which gravity or viscous force dominates are as follows:  
\[N_{gy}<0.1:\text{ Viscous forces dominate};\] \[0.1<N_{gy}<10:\text{ Intermediate};\] \[N_{gy}>10:\text{ Gravity dominate}.\]
## 4 Experimental Results and Analysis
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Steganalysis Method**} & \multicolumn{5}{c}{**Embedding Rate**} \\ \cline{2-6}  & **20\%** & **40\%** & **60\%** & **80\%** & **100\%** \\ \hline DMSM & 0.6094 & 0.6856 & 0.7524 & 0.8059 & 0.8548 \\ FSMDP & 0.6103 & 0.6891 & 0.7553 & 0.8082 & 0.8590 \\ Stegformer & 0.6122 & 0.6928 & 0.7568 & 0.8120 & 0.8604 \\ Our method & **0.6208** & **0.7037** & **0.7665** & **0.8226** & **0.8692** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection accuracy for the ACL steganography method.
## 1 Introduction  
Air acts as an insulator and in excess can lead to heat stress in certain applications, such as in structural firefighting turnout suits [1]. Air gaps, which can be defined as the volume of air occupied between the body and clothing, or between clothing layers, are essential for thermal protection [2]. Air gaps within a multi-layer firefighting turnout suit work to slow heat transfer from the fireground to the skin. However, overly large air gaps can exacerbate the onset of heat stress as they reduce the body's ability to lose excess metabolic heat produced during intense physical activities such as firefighting [2]. Coupled with hot and humid conditions, an optimized balance in air gap thickness must be struck when donning a full systems firefighting ensemble, including personal protective equipment (PPE) such as the self-contained breathing apparatus (SCBA) [1].  
While essential for air way protection, the SCBA often has a negative impact on firefighters' physiological strain [3]. Current designs may significantly hinder firefighter mobility and increase the difficulty of performing job-related tasks as the SCBA places an additional weight-bearing load on the body, especially in the shoulders and hips [4]. This cumbersome load leads to quicker onset of heat stress which, over an extended period, can result in increased cardiovascular strain, the leading cause of acute firefighter fatalities [5, 6].  
The SCBA may also interfere with personal protective clothing (PPC) fit in certain areas where it is synched. [PERSON] (2020) studied the effects of the SCBA, and other ensemble elements, on static range of motion and found that mobility progressively worsened as additional PPE was donned. In the same study, [PERSON] (2020) also assessed how PPC fit impacted ease measurements between the base layer and turnout suit ([PERSON] et al., 2020). Due to three-dimensional body scanning limitations, the previous study could not quantify the same impact when donning a SCBA. To the authors' knowledge, no previous work has specifically quantified the direct effect of donning an SCBA on fit, ease measurements, or air gaps. Given air gaps provide essential protection but may also exacerbate the leading cause of firefighter fatalities, it is critical for researchers to better understand the impact of SCBA design on air gaps within the full systems firefighting ensemble to optimize fit, physiological comfort, and protection.  
The above tradeoffs between protection and comfort underscore the importance of conducting research within a functional design framework, such as the portable clothing environment by [PERSON] (2017). The functional design process involves considering how changing one design or material component impacts all others. Employing the functional design matrix as part of this process is essential (2017). While design and material recommendations are outside the direct scope of this work, as findings lend themselves to these potential improvements, such suggestions will be included.
## 2 Methods
### Mono-Colored Spherical Object Detection Algorithm
To prevent this collision, the radius value is determined as the maximum value between the radius value obtained from HSV filter and the value calculated in the previous time step. * Scenario 3. If the CHT fails to find a spherical object and the MAV is yawing in search of the spherical object, the position and radius predicted via HSV filtering are used. Before the initial spherical object detection occurs, image cropping for CHT and radius comparison are unavailable. Thus, in this scenario, the HSV filtering result is used, even though it is inaccurate. Notably, the error in this scenario can be rapidly reduced because the CHT on the cropped image starts to operate when the MAV vision system initially detects the spherical object. Figure 6 shows the flowchart of the new hybrid detection algorithm with the above-mentioned scenarios. Figure 5: Sequence of the newly developed algorithm. (**a**) Original image captured by the MAV vision system; (**b**) cropped image using the HSV filtering result; (**c**) final spherical object detection using the detection algorithm. The red and green circles represent the detected result of the HSV filter and CHT, respectively. This hybrid detection algorithm (shown in Figure 6) was used to detect the spherical object and not the align marker. During detection of the sphere installed at the landing platform, the distance from the MAV to the sphere is considerable, and other objects can be captured in the image. To reduce the possibility of misdetection, the pass range of the HSV filter must be narrow. However, only a portion of the spherical object would be detected because of this narrow band (for the HSV filter). Thus, cropping, the scenario-based method and CHT were combined to detect the sphere. Meanwhile, during detection of the align marker, the MAV is very close to the station. Thus, the align marker occupies most of the image area, and interference by other objects in the background is very small. A broad HSV range can thus be used for the align marker, hence the hybrid detection algorithm was not necessary.
## 3 Experimental Results and Discussion
### Toughness Index
The enhancement effect of the fiber on the concrete toughness is more obvious with the increase of fiber content. However, at a high strain rate (i.e., \(10^{-2}\) s\({}^{-1}\)), the toughness index of FRC (C30) is increased by 23.3, 61.3, and 88.2%, respectively, and the values increased 57.3, 44.9, and 95.6%, respectively, for C40. The improvement of the strain rate will lead to a decrease in the toughness index to a certain extent. However, the toughness index will be less reduced compared to the decrease in plain concrete, which shows that the toughness of the concrete with PVA decreases more smoothly with increases of the strain rate compared to plain concrete. These results illustrate that the toughness of FRC is better than that of plain concrete under the strain rate range of seismic load [20]. This is of great significance to the application of FRC in seismic design. Considering the effect of the fiber on the compressive strength, splitting strength, elastic modulus, and peak strain of concrete, as well as the cost factors and dispersion technology of the fiber, the concrete with a 0.2% PVA content is recommended for actual applications, as it can meet the engineering requirements.