esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Affecting Factors
### Effect of Wave Period  
The effect of wave period can be studied by comparing the test results recorded in Test 2 (T = 2.0 s) and in Test 3 (T = 3.0 s). Distribution of the maximum wave impact on the surface of the revetment breakwater under regular wave loading in Test 2 is shown in Figure 17. It can be seen in Figures 10 and 17 that the distribution and values of the maximum wave impact on revetment breakwater recorded in Test 2 and Test 3 are basically the same. Comparison of wave impact force on structure A and B measured in Test 2 and Test 3 is shown in Figure 18. Comparison of pore pressure recorded at P1, P4, P8 and P12 between Test 2 and Test 3 is shown in Figure 19. It is found that the wave-induced impact force on the revetment breakwater and the pore pressure in the reclaimed sand foundation recorded in Test 2 are smaller than that in Test 3. Table 4 demonstrates that the vertical displacement of structure B is 3 mm and the overtopping is 169.46 kg per sectional meter in Test 2, which are about 1/2 of that in Test 3. The above results indicate that the regular wave with a longer period will cause greater adverse effects on the stability of breakwater.  
Figure 16: Comparison of pore pressure recorded at P1, P4, P8 and P12 between Test 3 (\(d\) = 0.71 m) and Test 1 (\(d\) = 0.48 m).  
Figure 17: Distribution of the maximum wave impact on the surface of the revetment breakwater under regular wave loading in Test 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Test No.** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** & **Test 6** & **Test 7** \\ \hline Displacement-x (mm) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 & 0.5 \\ Displacement-z (mm) & \(-\)0.2 & \(-\)3.0 & \(-\)6.0 & \(-\)0.4 & \(-\)1.1 & \(-\)0.3 & 0.3 \\ Overtopping (m\({}^{3}\)/(m\(\cdot\)h)) & 0 & 0.1695 & 0.3578 & 0.9136 & 0.9921 & 0.8990 & 0.3219 \\ \hline \end{tabular}
\end{table}
Table 4: Displacement of structure B and overtopping in Test 1 to Test 6.  
Figure 18: Comparison of wave impact force on structure A and B measured in Test 3 (\(T\) = 3.0 s) and Test 2 (\(T\) = 2.2 s).
## 6 The Discrepancy Analysis between the Model Calculation and the Experiment Measurement  
To make it clear whether the discrepancy between the modified model's prediction result and the experiment's measurement is larger or the margin of the measure equipment's error is bigger, it is inevitable to analyze the error from this measuring progress. The experiment platform's elements are presented in Figure 2, and to get the whole platform's instrument error, every element's error band is necessary, as listed in Table 6.  
According to Equations (52) and (53), the tangential stiffness along the \(x\) and \(y\)-axis's measurement is the deformation divided by the force increment of their direction, respectively, which means the error propagation analysis of A/B is inevitable. With the symmetry character of the tangential stiffness model along the \(x\) and \(y\)-axis, only the stiffness along the \(x\)-axis is discussed in this section. According to [PERSON] [32], the error of the tangential stiffness along the \(x\)-axis can be expressed as Equation (55):  
\[U_{x}=\sqrt{\frac{U_{\Delta F_{x}}^{2}}{\left(\Delta x\right)^{2}}+\Delta F_{x }^{2}\frac{U_{\Delta x}^{2}}{\left(\Delta x\right)^{4}}} \tag{55}\]  
According to this expression and Table 6, it is not hard to get the foundation of this error band: \(U_{F_{x}}\), \(U_{\Delta x}\), \(\Delta x\) and \(\Delta F_{x}\). To get the range of \(U_{x}\), the range of each parameter in \(U_{x}\) is essential, which means the maximum and minimum value of each parameter is necessary. Based on Table 6, it is convenient to see that \(U_{\Delta F_{x}}\) is larger than \(U_{\Delta x}\) by at least 16 orders of magnitude; therefore, Equation (55) can be simplified as:  
\[U_{x}=\frac{U_{\Delta F_{x}}}{\Delta x}+o\left(\frac{U_{\Delta F_{x}}}{\Delta x }\right)\approx\frac{U_{\Delta F_{x}}}{\Delta x} \tag{56}\]  
where \(o\left(\frac{U_{\Delta F_{x}}}{\Delta x}\right)\) is the infinitely small term of \(\frac{U_{\Delta F_{x}}}{\Delta x}\) and then the simplification of Equation (56) could be justified. The data listed in Table 6 manifests that \(U_{x}\) is at least one magnitude smaller than the discrepancy between the model value and the experimental data. According to the symmetry character of the tangential stiffness model along the \(x\) and \(y\)-axis, the error of the tangential stiffness along the \(y\)-axis will have a same magnitude character as \(U_{x}\). And that means the change from the modified model is not from the discrepancy between the experiment and the model's calculation but an effective improvement of the original one.
# Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
###### Abstract  
At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman's correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.  
fault diagnosis; graph attention network; feature fusion; marine engines; machine learning +
Footnote †: journal: selected topics
## 4 Implementation and Comparison
### Performance Comparison for the Hyperbolic Function
In this case, in addition to optimizing the hardware structure, we spend a little bit of area to optimize the timing, and finally the OML-PLAC method improves all performance indicators of the circuit. The final implementation results show that our method reduces area, delay and power by 8.32%, 7.14%, and 6.28% separately. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Method** & **x** & **No.S** & **IO.F** & **NO.M** & **NO.A** & **qw** & **kw** & **Node** & **Pre** & **Area** & **Delay** & **Power** & **Energy** & **MAE\({}_{\text{\emph{def}}}\)** & **MAE** \\ \hline This paper & [0,1] & 4 & \(8/8\) & 1 & 1 & 8 & 1 & 90 & -1.92 & -102 & -0.52 & -0.019 & -0.010 & -5.0 \(\times 10^{-3}\) & -4.95 \(\times 10^{-3}\) \\ ML-PLAC & [0,1] & 4 & \(8/8\) & 1 & 1 & 8 & 2 & 90 & -1.92 & -102 & -0.52 & -0.019 & -0.010 & -5.0 \(\times 10^{-3}\) & -4.95 \(\times 10^{-3}\) \\ \hline This paper & (\(-\)1,1) & 22 & \(13/13\) & 4 & 3 & 13 & 3 & 40 & 1.54 & 563.77 & 0.65 & 0.179 & 0.116 & 3.79 \(\times 10^{-4}\) & 3.79 \(\times 10^{-4}\) \\ ML-PLAC & (\(-\)1,1) & 29 & \(13/13\) & 4 & 3 & 13 & 5 & 40 & 1.43 & 614.93 & 0.7 & 0.191 & 0.134 & 3.79 \(\times 10^{-4}\) & 3.79 \(\times 10^{-4}\) \\ \hline This paper & (\(-\)8,8) & 4 & 6/6 & 1 & 1 & 6 & 1 & 65 & 1.2 & 119.88 & 0.83 & 0.009 & 0.007 & \(2\times 10^{-2}\) & 1.89 \(\times 10^{-2}\) \\ \hline
[0] & (\(-\)8,8) & - & 6/6 & - & - & - & - & 65 & 0.44 & 126.53 & 2.25 & - & - & \(2\times 10^{-2}\) & 1.98 \(\times 10^{-2}\) \\  & & & & & & & +172.7\% & \(-\)52.9\% & \(-\)63.11\% & - & - & - & -4.55\% \\ \hline This paper & (\(-\)8,8) & 7 & 8/8 & 2 & 1 & 16 & 1 & 90 & 1.111 & 0.90 & 1.116 & 0.104 & 7.6 \(\times 10^{-3}\) & 7.6 \(\times 10^{-3}\) \\ \hline Scheme I in & (\(-\)8,8) & 12 & 8/8 & - & - & 16 & - & 90 & 1 & 1684.27 & 0.98 & 0.520 & 0.510 & - & 1.14 \(\times 10^{-2}\) \\  & & & & & & & +11\% & \(-\)49.90\% & \(-\)8.16\% & \(-\)77.69\% & \(-\)79.61\% & - & \(-\)33.33\% \\ \hline Scheme II in & (\(-\)8,8) & 12 & 8/8 & - & - & 16 & - & 90 & 1 & 2024.37 & 0.98 & 0.667 & 0.654 & - & 7.6 \(\times 10^{-3}\) \\  & & & & & & & +11\% & \(-\)58.31\% & \(-\)8.16\% & \(-\)82.61\% & \(-\)84.10\% & - & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison for sigmoid(\(\infty\)). The third and fourth experiments compare our method with other specialized methods with good performance, and the results of the comparison are shown in the table. In these two sets of experiments, we take advantage of the symmetry of the function and only consider the case where the independent variable x is positive. Since the range of the dependent variable is (0,1), we only consider the fractional bits of the result. When x is negative, the corresponding positive number is used as input to get the intermediate result, and the final result can be obtained by inverting the fractional bits and adding 1.
# A Novel Adaptive Transient Model of Gas Invasion Risk Management While Drilling
###### Abstract
[PERSON] et al. developed a fuzzy controller, which satisfies physical/operational limitations [34]. The PID controller has the advantages of a simple structure, independent control parameters, good adaptability, and strong robustness. It can exert its control ability to the maximum effect on complex models and is widely used in the field of the petroleum industry. Nevertheless, traditional PID controllers require offline tuning and fixed parameters, which limit their adaptability in dynamically changing downhole environments. Most of the existing studies are based on the gas-liquid two-phase flow model to control BHP [35; 36], while studies considering gas-liquid-solid three-phase flow in the wellbore annulus are limited. The multiphase flow model is a hyperbolic partial differential equation (PDE) equation, and the solution of the gas-liquid-solid three-phase flow model is more complex. To reduce the control complexity while maintaining accuracy, PID control is still a suitable choice. However, traditional PID controllers require predefined parameters, do not adapt to changing targets, and exhibit poor responsiveness and significant overshoot in BHP regulation [25]. In recent years, machine learning has been widely applied in various industrial scenarios [37]. Among them, artificial neural networks have strong self-learning and nonlinear approximation capabilities, enabling the real-time adjustment of PID parameters. Fuzzy control provides reasoning capability based on human knowledge. However, traditional fuzzy controllers often rely on fixed fuzzy rule tables defined offline, which lack adaptability in dynamic downhole conditions. To overcome these limitations, this paper proposes a fuzzy neural network PID controller that integrates neural network adaptability, fuzzy logic reasoning, and PID simplicity. This controller can learn and optimize fuzzy rules online according to real-time system feedback, providing improved adaptability and robustness. Compared to NMPC and L1 adaptive control, the proposed fuzzy neural network PID controller reduces the dependence on accurate physical models, lowers the computational load, and improves the numerical stability during real-time operation in complex multiphase environments. Based on the three-phase flow mechanism in the wellbore, the controller is developed to regulate BHP, and its performance is compared with that of the conventional PID controller and fuzzy PID controller during gas kick scenarios. Simulation results demonstrate that the proposed controller achieves higher accuracy, faster response, and better control stability in BHP regulation. Compared to previous studies that mostly relied on simplified two-phase or isothermal models, this work presents a novel integration of a fuzzy neural network PID controller with a full-scale non-isothermal gas-liquid-solid three-phase model. This integration allows for online rule optimization, improved adaptability, and real-time robustness for BHP control during gas invasion scenarios, which constitutes the core scientific contribution of this study.
# Microbial Precipitation of Calcium Carbonate for Crack Healing and Stabilization of Sandy Soils
###### Abstract  
Microbially induced calcium carbonate (CaCO\({}_{3}\)) precipitation (MICP) can improve the shear strength of soil via biocementation while reducing its porosity and hydraulic conductivity. The purpose of this study was to evaluate the effect of the addition of bacterial metabolites and montmorillonite on the crack healing and biocementation of sandy soil during the MICP process. Cracks were generated by drying wet soil samples in Petri dishes, after which they were sprayed with one of four treatments: deionized water, a cementation solution, bacteria mixed with the cementation solution, and bacterial metabolites mixed with the cementation solution. After five cycles of this spray treatment, the surface crack ratio was observed to decrease by about 71% when living cells were used and by about 80% when microbial metabolites were added. However, the crack reduction ratio was relatively low when treated with water (28%) and the cementation solution alone (48%). To investigate the effect of adding a phylosilicate to improve the strength of sandy soil, MICP was induced in sand mixed with 0-30% montmorillonite (MMT). As a result, the soil strength increased with higher levels of MMT, indicating that MMT contributed to soil stabilization as a colloid for CaCO\({}_{3}\) precipitation and via adhesion between sand grains. Therefore, for the crack healing and stabilization of sandy soil, the addition of bacterial metabolites and montmorillonite may enhance the effectiveness of the MICP process.  
MICP; soil stabilization; microbial metabolites; _Sporosarcina pasteurii_; montmorillonite
###### Abstract  
A deployable reflector antenna (DR-A) is a structure that can be stored in a large-diameter Synthetic Aperture Radar (SAR) antenna and be mounted onto a launch vehicle. Considering the performance of the launch vehicle, it is necessary to develop a lightweight, high-performance antenna structure. The solid-type deployable reflector antenna is composed of a number of unit main reflectors. To reduce the weight of the antenna, a lightweight main reflector must be developed. In this paper, following \"Development of Deployable Reflector Antenna for the SAR Satellite (Part 1)\", the manufacturing and qualification of the main reflector using honeycomb sandwich composites are described. Four types of composite main reflectors were manufactured with variables in the manufacturing process. The manufacturing variables include the curing process of the structure, the application of an adhesive film between the sheet and the core, and the venting path inside of the sandwich core. After manufacturing the main reflector, we performed weight measurements, non-destructive testing (NDT), surface error measurement using a Coordinate Measurement Machine (CMM), and modal testing for each type of composite main reflector. Through the research and development process, we found that a perforated hole is necessary when excluding the adhesive film during bonding of an aramid core and a CFRP sheet, and a lightweight composite reflector could be developed through this process. We selected the main reflector with the best performance and developed a composite main reflector that can be applied to satellites.  
deployable reflector antenna (DR-A); Synthetic Aperture Radar (SAR); honeycomb sandwich composite; main reflector; non-destructive testing (NDT); Coordinate Measurement Machine (CMM) +
Footnote †: journal: Applied sciences  
doi:10.3390/
## 4 Conclusions  
A high-speed PIV set up for optical spark-ignited engine has been modified and optimized to allow not only for the measurement of flow-field characteristics but also simultaneous flame front detection and imaging. Both properties of the flow-field such as flow velocity, turbulence intensity, and flame properties such as flame area, flame speed, and wrinkleness can be derived from the same image to explore their relationship in a true simultaneous fashion. Solutions and approach to avoid and overcome interferences and challenges in applying and developing this diagnostic technique are detailed in this paper. This include the process to minimize interferences from flame luminosity and laser reflection. The processing procedures are discussed and a few validation checks were performed to ensure the technique will yield good accuracy, especially near the flame front region. After the accuracy is confirmed, the technique was then applied during firing cycles of a stoichiometric operating optical engine as well as motoring cycles of the same engine. While prior to ignition the flow-field is similar between firing and motoring case, significant differences were observed after ignition timing. The flame propagates with the global tumble direction and help maintain this flow across the pent-roof area. It also enhances flow and turbulence intensity in flow region near the flame front as the combustion enters later stages and the flame occupies a larger portion of the pent-roof. In terms of cyclic variations, there seems to be no correlation between flame shape and propagation path with burning speed. However, there is a correlation between the flow across the spark plug with burning durations as well as flame propagation path. Additionally, this relatively simple high-speed PIV setup also has a lot of potential in combining with other diagnostics to capture other combustion species,  
Figure 12: **(a)** In-cylinder pressure and burnt duration of 19 tested cycles with combustion PIV, **(b)** the flame velocity of the four selected cycles derived from the extracted flame front and **(c)** flow-field vectors at the ignition timing of the four selected cycles and the flame development at 0\({}^{\circ}\) and 10\({}^{\circ}\) CA aIT as well as the movement of the corresponding flame centroids over the whole inspected period.  
mixture fractions as well as other important and influential processes which will help expanding the knowledge base for advance engine design and development.  
Conceptualization, Y.I.; Data curation, T.F.; Writing--original draft, M.K.L.; Writing--review & editing, A.N.  
This research received no external funding.  
The authors declare no conflict of interest.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.6 Matheuristics  
Two studies [30; 31] employ matheuristics, a hybrid approach blending mathematical precision with heuristic ingenuity, to solve optimisation problems, while both studies align with key aspects of Industry 4.0, such as transportation optimisation, automation, efficiency, and scalability, they differ in their scopes and methodologies.  
The study conducted by [PERSON] et al. [30] takes a comprehensive approach, addressing a wide range of packing challenges, encompassing the Container Loading Problem (CLP), Pallet Loading Problem (PLP), and the 3D Bin Packing Problem (3D-BPP). This study leverages mathematical programming models to handle the Vehicle Routing Problem with Simultaneous Delivery and Pickup and Time Windows (VRPSDPTW) in addition to the 3D-BPP.  
In contrast, the research conducted by [PERSON] et al. [31] is more specialised, concentrating specifically on the intricacies of the 3D-BPP. Here, the problem is formulated as a mixed-integer non-linear programming (MINLP) challenge, highlighting the study's precision in addressing this specific packing problem.
## 4 Proposed Framework  
We introduce an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 QAM-CAP modulated UVLC systems dependent on complex-valued neural networks and a constellation partitioning algorithm to overwhelm the linear and nonlinear imperfections in UVLC. The system's efficiency is improved by utilizing the Enhanced chaotic sparrow search optimization algorithm (ECSSOA). The audio signal features via a training data set can provide precise classification boundaries. I/Q components, which stand for in-phase and quadrature, are often concatenated. The kernel function can transform information from a \"lower-dimensional feature space to a higher-dimensional feature space\", allowing for linear separation of the training data set if it cannot be linearly separated [15].
# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 3 Width Measurement Using Image Processing
### Overview of Indoor Measurement Experiments
#### 3.1.3 Image Equipment and Optical Equipment (Lens)  
The most important thing in the use of imaging equipment and the crack measurement system using image processing techniques is selecting a high-definition digital camera. Once the crack measurement limit range has been set, the imaging equipment must be selected in consideration of the camera resolution, sensor size, and shutter speed. The imaging equipment used in this study was Sony's mirrorless A7 RIV and Canon's EOS 5D Mark III, which were installed with image sensors in full-frame 35.7 \(\times\) 23.8 mm\({}^{2}\) and 36 \(\times\) 24 mm\({}^{2}\) sizes with resolutions of 61 million (9504 \(\times\) 6336) and 22.1 million (5760 \(\times\) 3840) pixels, respectively.  
The lens collects light and sends it to the image sensor, and the working distance is secured according to the specifications of the lens. The types of lenses can be divided into wide-angle, telephoto, standard, and prime. Wide-angle lenses enable shooting wider background ranges compared to standard lenses and shooting large pictures of subjects that cannot be approached from distant locations. Prime lenses are only suitable for either long distances or short distances, thereby minimizing the problems above. The focal length of the lens necessary to satisfy accuracy in the determined working distance range can be obtained by using Equation (5). Figure 4 below shows the focal lengths according to the working distances necessary to acquire the size of the subject clearly and accurately. In this study, lenses that can optimize the performance of digital high-definition cameras were selected. The applied lenses consisted of 50, 100, and 135 mm single-vision lenses selected according to manufacturer, and the specifications of the lenses are shown in Table 2. As for the range of the final working distances, the distances to the points where the values of the size of one pixel corresponding to individual line widths become the same were set as the measurement limit distances.  
Figure 4: Focal length satisfying width measurement.