esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
## 3 Results and Discussion
### Model Training Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Scanning Speed** & \multicolumn{3}{l}{**Laser Power (W)**} & & & & & & & & & & \\ \cline{2-11}
**(mm/s)** & **100** & **110** & **120** & **130** & **140** & **150** & **160** & **170** & **180** & **190** & **200** \\ \hline
400 & **S0** & - & S9 & - & - & - & - & - & - & - & - \\
500 & S1 & - & S10 & - & S18 & - & S27 & - & - & - & - \\
600 & S2 & - & S11 & - & S19 & - & S28 & - & S36 & - & S45 \\
700 & S3 & S6 * & S12 & S15 * & S20 & S24 * & S29 & S33 * & S37 & S42 * & S46 \\
800 & S4 & S7 * & S13 & S16 * & S21 & S25 * & S30 & S34 * & S38 & S43 * & S47 \\
900 & S5 & S8 * & S14 & S17 * & S22 & S26 * & S31 & S35 * & S39 & S44 * & S48 \\
1000 & - & - & - & - & S23 & - & S32 & - & S40 & - & S49 \\
1100 & - & - & - & - & - & - & - & - & - & S41 & - & S50 \\ \hline \hline \end{tabular} Where bold is the deleted sample, those with symbols ‘*’ are simulated data, and the rest are experimental data.  
\end{table} 
Table 6: Correspondence between process parameters and sample numbers. After data augmentation, all models showed marked improvement. The MLP model achieved the highest accuracy gains, with increases of up to 23.5% in R\({}^{2}\), and reductions of 27.1% in MAE and 17.8% in RMSE. These results confirm the efficacy of the proposed augmentation strategy. By incorporating physically consistent synthetic data, the framework not only enhances predictive accuracy but also improves the physical interpretability of the model. A detailed comparison is presented in Figure 12.  
Figure 11: Prediction of melt pool depth by MLP model after data enhancement. Figure 10: Prediction of melt pool width by MLP model after data enhancement.
## 4 Results and Discussion
### Test Systems
#### 4.2.6 Test System 6  
The time-varying model is selected for testing, and the model's difference equation is displayed as follows. The mean value and standard deviation for 10 runs are displayed in Table 10. The mapping range of the activation function is set to 0 to 30. Figure 14 shows the step response, while Figure 15 displays the comparison results. The results demonstrate that the ALGWO-BPNN method shows the optimal control effect in slow time-varying systems.  
\[w=1-0.8\times e^{-0.1k} \tag{25}\]  
\[y(k)=0.9969\times w\times y(k-1)+0.0053\times u(k-1) \tag{26}\]  
First, as shown in Table 10, using the ALGWO method produces lower variances and standard deviations compared to using the GWO algorithm. For example, a PID control system using \(t(x)\) BPNN reduced the standard deviation from 8.8126 to 0.6698, and also reduced the average value by 5.5. Therefore, it improves elasticity and average accuracy. Then, the final average fitness is reduced by roughly 10, making the ALGWO-BPNN-PID controllers significantly superior to conventional PID controllers. Finally, the \(g(x)\) activation function has a better impact when used as an activation function, with a fitness of 40.2174.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \(g(x)\) & \(t(x)\) & \(h(x)\) & **PID** \\ \hline \multirow{3}{*}{GWO} & AVE & 40.8749 & 46.6955 & 41.2529 & 56.4116 \\  & STD & 0.61855 & 8.8126 & 1.2789 & 0 \\ \cline{1-1}  & AVE & 40.2174 & 41.1174 & 40.5169 & 49.2536 \\ \cline{1-1}  & STD & 0.5555 & 0.6698 & 0.6763 & 6.6099 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparison of final fitness of algorithms on test system 6.  
Figure 13: The comparison results of algorithm optimization search on test system 5.  
Figure 14: The step response results on test system 6. (**a**) The simulation results of BPNN-PID using \(g(x)\) and (**b**) simulation results of BPNN-PID using \(t(x)\) and (**c**) simulation results of BPNN-PID using \(h(x)\) and (**d**) simulation results of traditional PID.  
Figure 15: The comparison results of algorithm optimization search on test system 6.  
Figure 14 shows that the controller employing BPNN-PID outperforms the PID controller in terms of reaction speed, shortening the time by around ten seconds, when tracking the unit step response. As seen in Figure 15, the ALGWO algorithm is employed to regulate the BPNN-PID control scheme in comparison to GWO, and by iteration 100, it comes close to an optimal solution, demonstrating ALGWO's superior global search capabilities. The optimization values for the ALGWO method are 49.2536 and 40.5169, respectively, in terms of optimization accuracy. This demonstrates that it outperforms GWO. In conclusion, GWO-BPNN-PID controllers have better performance when applied to slow time-varying systems.
## 4 Deployment Test
### Modeling of Friction and Trajectory Error  
For friction compensation, a rotational friction model considering the Coulomb friction torque was employed as given below [27,28]:  
\[EFT\approx T_{\mathrm{C}}\cdot\tanh\biggl{(}\frac{\omega}{\omega_{Coul}}\biggr{)} \tag{13}\]  
where \(T_{\mathrm{C}}\), \(\omega\) and \(\omega_{Coul}\) are the Coulomb friction torque, relative angular velocity, and Coulomb threshold velocity, respectively; \(\omega_{Coul}\) is a parameter used to alleviate numerical instability caused by Coulomb friction. In this work, 0.01 rad/s was used as the reference value [28]. As seen in Figure 10, EFT1, EFT2, and EFT3 are the equivalent friction torques at joints O, I, and F; \(\phi_{1}\) and \(\phi_{2}\) indicate the rotation angles of joint O and I; trajectory error was defined and computed using Equation (14) as follows:  
\[Trajectory\ error(\phi^{a})=\frac{1}{n}\sum_{t=0}^{n}\biggl{(}\frac{\phi^{e}(t_ {i})-\phi^{a}(t_{i})}{\phi^{e}(t_{i})}\times 100\biggr{)} \tag{14}\]  
where \(\phi^{a}=\phi^{a}(EFT1,EFT2,EFT3,t)\) in degrees are the rotation angles according to the time evolution; the initial time was \(t_{0}=0\) s and the time step \(\Delta t=0.05\) s; \(n\), \(a\), \(e\) denote the number of time data used, analysis, and experiment, respectively. The data were compared for four seconds until all panels were fully deployed in the test. We added the test frames in the MBD model to consider the effect of test fixture. Contact stiffness of 37,000 N/mm\({}^{1.5}\) and contact damping of 3.7 N\(\cdot\)s/mm\({}^{1.25}\) were applied based on Hertz contact theory [29] between panel to panel and between truss-links to panels to allow for movement within a limited area in case of contact.  
Figure 9: Fully deployed configuration (case 2 and case 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test-ID** & **Joint O** & **Joint I** & **Truss-Links** \\ \hline Case 1 & 2.17 N\(\cdot\)m & 2.38 N\(\cdot\)m & \(\times\) \\  & (2 ea) & (2 ea) & \\ Case 2 & 2.17 N\(\cdot\)m & 2.38 N\(\cdot\)m & O \\  & (2 ea) & (2 ea) & \\ Case 3 & 2.17 N\(\cdot\)m & 3.57 N\(\cdot\)m & O \\  & (2 ea) & (3 ea) & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Test cases.
###### Abstract  
In this paper, a conceptual investigation of the interface between wireless power devices and a retina complementary metal oxide semiconductor (CMOS) neuron integrated circuit (IC) have been presented. The proposed investigation consists of three designs: design-I, design-II, and design-III. Design-I involves a slotted loop monopole antenna as per American National Standards Institute (ANSI) guidelines, which achieve an ultra-wide band ranging from 3.1 GHz to 10.6 GHz. The biocompatible antenna is made on silicon-nitride substrate using on-wafer packaging technology and it is used as a receiver device. The performance of antenna provides a wideband, sufficient power to receive, and low losses due to the avoidance of printed circuit board (PCB) fabrication. A CMOS based multi-stack power harvesting circuit achieves the output power ranging from 4 mW to 2.7 W and corresponds from the selected Radio Frequency (RF) bands of loop antenna is exhibited in design-II. The power efficiency of 40% to 82%, with respect to output powers of 4 mW to 2.7 W, is achieved. Design-III includes a CMOS based retina neuron circuit that employs a dynamic feedback technique and support to achieve the number of read-out spikes. At the end of the interface between wireless power devices and a CMOS retina neuron IC, 50 mV read-out spikes are achieved, with varying light intensity, from 0 mW/cm\({}^{2}\) to 2 mW/cm\({}^{2}\). The proposed design-II and design-III are implemented and fabricated using commercial CMOS 0.065 \(\upmu\)m, Samsung process. The antenna and RF power harvesting IC could be placed on a contact lens platform while retina neuron IC can be implanted after ganglions cells inside the eye. The antenna and harvesting IC are physically connected to the retina circuit in the form of light. This conceptual investigation could support medical professionals in achieving an interfacing approach to restore the image visualization.  
integrated circuit (IC); CMOS (complementary metal oxide semiconductor); image acquisition; retina neuron; wireless power devices; on-wafer antenna; RF power harvesting
## 3 Using ML for Water Activities
### Advanced Techniques and Sustainability
#### 3.3.2 Advancements in Water Management and Quality Assessment  
* **Intelligent water management system**[267]: This study proposed an intelligent system for optimizing water collection and distribution, including water consumption prediction, without specific performance metrics.
* **Water quality index prediction**[268]: This study developed water quality index (WQI) prediction models using water samples from North Pakistan. Hybrid algorithms showed superior performance with high accuracy and low error metrics.
* **Dynamic time scan forecasting (DTSF)**[269]: This study introduced the DTSF method for water demand forecasting in urban supply systems, demonstrating comparable or better predictions with fewer computational resources.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 4 Automatic Navigation System of a ship in a Fairway
### Control System
#### 4.4.3 Heading Stabilizing Controller  
Because the rudder at low speed is ineffective, the bow thruster is used to bring the ship's heading onto the desired angle. To automatically control the ship's heading by the bow thruster, a second fuzzy controller is also proposed. This controller includes two inputs and one output. The inputs consist of heading error and yaw rate while the output is the pushing force by the bow thruster. Each variable of the controller has seven membership function sets with variables: \(-\mathrm{B}\), \(-\mathrm{M}\), \(-\mathrm{S}\), Zero, \(+\mathrm{S}\), \(+\mathrm{M}\), and \(+\mathrm{B}\). The membership functions for these parameters are shown in Table 4. The membership functions of the controller are presented in Figure 8.  
Figure 7: The scheme of ship’s speed control using proportional-integral-derivative (PID) algorithm.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fuzzy Logic Controller** & **Variable Name (Unit)** &
\begin{tabular}{c} **Zones of Membership Functions** \\ **and Their Parameters** \\ \end{tabular} \\ \hline Input set 1 & Heading error (Degrees) & +B (\(-\)B): (trapmf [180 180 130 110]) +M (\(-\)M): (trimf [130 85 42]) +S (\(-\)S): (trimfrimf [53 27 0]) \\  & & Zero: (trimf [\(-\)1.5 0 1.5]) \\ \hline Input set 2 & Yaw rate (Degrees/sec) & +B (\(-\)B): (trapmf [1 0.8 0.5]) +M (\(-\)M): (trimf [0.6 0.4 0.22]) +S (\(-\)S): (trimf [0.4 0.2 0]) \\  & & Zero: (trimf [\(-\)0.1 0 0.1]) \\ \hline Output set & Bow Thruster (N) & +B (\(-\)B): (trimf [10,000 10,000 9500]) +M (\(-\)M): (trimf [8500 8000 7500]) +S (\(-\)S): (trimf [4500 4000 3500]) \\  & & Zero: (trimf [\(-\)500 0 500]) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of heading controller.  
Input set 1: +B (\(-\)B) signifies a big heading error to the starboard (port) side; +M (\(-\)M): is medium heading error; +S (\(-\)S): is small heading error; Zero: is very small heading error, see Figure 8a.  
Input set 2: +B (\(-\)B) signifies a fast yaw rate to the starboard (port); +M (\(-\)M): is slow yaw rate; +S (\(-\)S): is very slow yaw rate; Zero: is small error, see Figure 8b.  
Input set 3: +B (\(-\)B) signifies a big force by bow thruster to push the ship's bow from the port (starboard) to opposite side; +M (\(-\)M): is medium pushing force; +S (\(-\)S): is small pushing force; Zero: is pushing force set to zero, see Figure 8c.  
Output set: Heading error range is limited from \(-\)180 to +180 degrees. The yaw rate is bounded from \(-\)1.0 degree/s\({}^{2}\) to 1.0 degree/s\({}^{2}\), and the output is determined from \(-\)10,000 N to 10,000N. In the membership functions for the pushing force of the bow thruster, there are seven triangle zones which are distributed symmetrically across a \"0\" value. Three zones at the negative axis are used to push the ship's bow from starboard to port. To complete the design of the controller, 49 inference rules were built as Table 5.
## 1 Introduction
Using only one module to extract semantic-consistent features is often not enough; the supervision of the corresponding semantic information is also missing in the disentangling module. Therefore, it will lead to the problematic domain shifts. To solve the above problems, we improve the SDGZSL method and propose an disentanglement generalized zero-shot classification method involving enhanced semantic-consistent and discriminative feature transformation (ESTD-GZSL). A semantic decoder is first added to decode the seen class instances and the synthesized instances of unseen classes back into the semantic space, by which we can make the synthesized instances better represent their semantics, enhance the alignment of the two modalities, and reduce the offset between visual and semantic features. Then, we feed the output of this decoder as a supervised signal into the disentanglement auto-encoder to reconstruct visual features. Finally, since the semantic decoder is trained with cycle-consistent loss, we consider that the output obtained by the visual features via the semantic decoder is also semantic-consistent. Therefore, we further improved the classification module of the method. That is, generalized zero-shot classification is performed by enhanced semantic-consistent discriminant features. Our source code is available at [[https://github.com/HanAccount/ESTD-GZSL](https://github.com/HanAccount/ESTD-GZSL)]([https://github.com/HanAccount/ESTD-GZSL](https://github.com/HanAccount/ESTD-GZSL)) (accessed on 6 December 2022). The proposed approach is based on SDGZSL [10] with the following contributions:  
* In order to alleviate the domain shift problem. An additional semantic decoder structure is added to enable the generator synthesized instances to better represent their semantics, strengthen the semantic and visual alignment, and reduce the bais between visual and semantic. * To enhance the ability of the disentanglement module to break down semantic consistency, we input the semantic decoder output as a supervised signal into the feature reconstruction of the disentanglement auto-encoder. * We improve the generalized zero-shot classifier by introducing transformation discriminative features in the classification stage and splicing semantic consistency features output from the semantic decoder and semantic consistency features from the disentanglement module decomposition to enhance semantic consistency and reduce the ambiguity between different categories. * The proposed method achieves more competitive results on four datasets in GZSL than baseline.
## Materials and Methods  
### Scheme Descriptions  
Five irrigation schemes in Africa were selected--the Wonji (Ethiopia), the Metehara (Ethiopia), the Koga (Ethiopia), Zankalon (Egypt), and ODN (Mali). The Wonji and Metehara are located in the Awash Basin, the Koga scheme is located in the Upper Blue Nile, the Zankalon scheme is located in the Nile Delta Basin, and the ODN scheme is located in the Niger Basin (Figure 1). The description of each irrigation scheme, standard deviation (SD) of plot sizes used in the evaluation, the major crops cultivated, the scheme area used in the evaluation, and the minimum and maximum elevation of each scheme are given in Table 1. The minimum and maximum elevation was taken from the digital elevation model (DEM) (90 m) of the Shuttle Radar Topography Mission (SRTM).  
Figure 1: Location of irrigation schemes: (1) Wonji, (2) Metehara, (3) ODN, (4) Koga, (5) Zankalon.  
In the Wonji and the Metehara, plot boundaries were provided by the irrigation scheme managers in AutoCAD [33] and converted to shapefile. The Koga, Zankalon, and ODN plots were digitally delineated using google Earth. Statistics were derived using the R software [34], and visualizations were done in ArcMap Thematic Mapping [35].
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## Materials and Methods
### Spectral Extraction and Preprocessing  
Each of wheat seed sample was considered as a region of interest (ROI). The spectra of all ROIs were extracted from the corrected hyperspectral images. An image segmentation procedure was conducted to isolate the wheat seeds from the black background. With the largest difference between wheat seeds and background, the image at 974 nm was selected to create a binary mask. Later, this mask was applied to the images of other wavebands. Besides, some morphological operations were also applied to avoid any overlapping among wheat seeds.  
There was no doubt that the existence of random noise would influence samples' spectra and further affect the accurate construction of the discrimination model [16; 18]. Thus, standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were employed to preprocess the spectral data in this study. For WT, the optimal parameters, such as wavelet function db6 and decomposition scale 3, were determined through the experiment. These preprocessing methods were analyzed and compared to improve the model performance.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
###### Abstract  
Recently, the handling of waste industrial resources has become an issue, and the importance of sustainable resources has increased. Among these waste industrial materials are glass wool and mineral wool, which are fibrous materials used as insulation materials with characteristics such as sound absorption, insulation, and non-flamamability. However, after their service life, glass wool and mineral wool used for insulation are generally buried or incirated, causing problems such as air and soil contamination. This research was conducted to examine the applicability of waste glass wool and mineral wool obtained from expired insulation as fiber reinforcement in cement concrete. The research aimed to evaluate the fresh concrete properties, strength properties, and durability properties by adding waste glass wool and waste mineral wool up to 0.5-2.0% of the cement weight. Regarding the slump and air content of fresh concrete, the results showed that the addition of waste fibers within this range did not significantly affect the air content. However, the slump decreased as the addition amount increased due to the high absorption, which is a characteristic of the fibers. In addition, the evaluation of strength revealed that the incorporation of fibers decreased the compressive strength compared to the reference concrete. However, the tensile strength increased due to the load-supporting function of the waste fibers. In the evaluation of freezing-thawing resistance and chloride ion penetration resistance, it was confirmed that the freezing-thawing resistance improved in all cases where waste glass wool was added. The chloride ion penetration resistance was found to be similar to that of the reference concrete. However, in the case of waste mineral wool, it was observed that an addition rate of more than 2.0% of fibers was required to ensure freezing-thawing resistance. As the addition rate increased, the total charge passed (permeability) increased significantly, leading to a decrease in chloride ion penetration resistance.  
waste glass wool; waste mineral wool; fiber-reinforced concrete; sustainable resource; compressive strength; flexural strength; freezing-thawing resistance; chloride penetration resistance
## 3 Results  
### Oligomeric State of HoFucl  
Commonly, native L-FucIs exist as oligomers [2; 3; 23]. According to crystal structure analysis, L-FucIs form homohexameric structures to be functional for substrate interactions [2; 23]. However, the oligomeric states of L-FucIs in a solution can vary, existing as trimers or tetramers, depending on the biochemical conditions [3; 6; 17]. In this study, size-exclusion chromatography, which is commonly used to examine the enzyme's molecular mass at the native state (i.e. under the non-denaturing condition), was performed to determine the oligomeric state of HoFucl (Figure 2). The chromatogram showed a single peak corresponding to HoFucl (Figure 2a). By comparing its elution volume with that of standard proteins, the molecular mass of the enzyme was predicted to be approximately 204 kDa, corresponding to a trimer (Figure 2b).