esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Biotechnology for Sunflower Inbred Line Development: Immature Embryo Rescue
## 3 Results
### Survival Rate of Immature Embryos--Multiple Factors Influence
Comparable results were also observed for MS2, MS3, and MS4, with survival rates ranging between 41% and 52%. In contrast, the control medium (MSI), which lacked phytohormones, produced a low survival rate of only 17%, even for the hybrid genotype. These findings highlight that genotype plays a crucial role in rescuing immature embryos during this stage. Inbred lines (G1 and G2) achieved a maximum survival rate of 23%, indicating a lower response compared to hybrids. The interval of 264 h (11 days) after fertilization revealed an optimal period for rescuing immature sunflower embryos and advancing them into the next generation. The MS5 medium demonstrated a survival rate above 50% for most genotypes. During this period, the number of genotypes or plants progressing to the next generation increased significantly. However, it is essential to ensure that the chosen medium supports this transition effectively. The interval of 312 h (13 days) after fertilization recorded the highest survival rates for immature embryos. Nevertheless, inbred lines (G1 and G2) did not demonstrate a 50% survival rate potential in MS1 medium (0:0:0). On the other hand, when using MS5 medium (2.0:0:4:0.4), most results showed low variability in the embryo saving rate (%), indicating a stable and optimal period for embryo rescue using a single-stage in vitro procedure. According to the results, most genotypes achieved survival rates above 71%, with the highest rate observed for hybrids (81%). These results demonstrate that genotype is a critical factor to consider in immature embryo rescue. The heterosis effect (hybrid vigor) could influence the selection of embryos in the population, favoring the development of more vigorous embryos. Therefore, it is recommended to use optimal conditions that exhibit low variability in terms of the survival rate. Low variability, which was not dependent on the genotype, was observed when using the MS5 medium at the 312 h interval (13 days) after fertilization. Additionally, the time interval is a key factor in improving the survival rate of immature embryos. If a specific period is targeted for embryo rescue, it is necessary to consider the number of embryos harvested to ensure success. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Genotype**} & **Time** & **MS1** & **MS2** & **MS3** & **MS4** & **MS5** \\ \cline{2-7}  & **hours** & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline G2—line & 264 h  
The medium used also significantly influenced the stability of the survival rate. All tested media supplemented with phytohormones outperformed the MS1 control medium without hormones. However, the highest survival rate was recorded at the concentration of MS5--2.0:0.4:0.4 (BAP:NAA:GA3).
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 2 Theoretical Method for Determining Prestress Loss of BFRP Tendons
### Prestress Loss from the Friction between BFRP Tendons and the Surrounding Casing Pipe  
BFRP tendons deflect under their own weight, resulting in friction between the prestressed BFRP tendons and the casing wall during tensioning. The friction is related to the length of the prestressed BFRP tendons, given the fact that the longer the length of the prestressed BFRP tendons, the higher the friction, and therefore the higher the prestress loss caused due to the friction, which can be obtained through Equation (2).  
\[\sigma_{\text{\tiny{2}}}=\sigma_{\text{\tiny{con}}}\bigg{(}1-\frac{1}{e^{ \kappa\kappa}+\mu\theta}\bigg{)} \tag{2}\]  
where \(\sigma_{\text{\tiny{con}}}\) is the tension control stress of the prestressed BFRP tendon, \(\kappa\) is the coefficient of the casing pipe, \(\mu\) is the friction coefficient between the BFRP tendon and the casing wall, \(x\) is the distance between the calculation cross-section and the anchor end, and \(\theta\) is the tangent angle between the calculation cross-section and the anchor end.
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
## 2 Materials and Methods
### The Benchmark Problem  
The benchmark problem describes a series of experiments which have a single solitary wave propagating up a triangular shaped shelf with an island feature located at the offshore point of the shelf. The series of experiments are conducted in a large wave basin which is 48.8 m long, 26.5 m wide and 2.1 m deep [19]. The basin is equipped with a piston-type wave maker powered by an electric motor and a wave board that consists of 29 independently functioning paddles, which is able to produce linear and nonlinear waves up to 0.8 m in height [19]. The walls and the underlying bathymetry of the basin are made of concrete in order to reduce the boundary effects due to friction [19]. The complex bathymetry is constructed symmetrically along the centerline of the basin. The water depth is kept constant at 0.78 m and hence the still water level (SWL) intersects the land at \(X=25.75\) m [19]. A single solitary wave with a height of 0.39 m is generated for each trial [19]. In the experiments, resistance and sonic wave gages are used to record the free surface elevation time series, and the velocity time series are recorded via acoustic Doppler velocimeters (ADVs) [19]. The bathymetry provided with the benchmark problem is given in Figure 1.  
The free surface elevation time series are recorded at \(Y=0.0\) m at following \(X\)-locations: \(X=7.5\) m, 13.0 m, and 21.0 m; at \(Y=5.0\) m at following \(X\)-locations: \(X=7.5\) m, 13.0 m, and 21.0 m; and at \(X=25.0\) m at following \(Y\)-locations: \(Y=0.0\) m and 5.0 m. The velocity data are recorded at two locations: \(X=13.0\) m, \(Y=0.0\) m and \(z=0.75\) m and \(X=21.0\) m, \(Y=-5.0\) m, and \(z=0.77\) m.  
The input parameters that are necessary for numerical modeling are the \(XYZ\) dataset of the bathymetry and the free surface elevation time series of the incoming wave. The dimensions of these parameters are kept the same as the ones that are used in the experiments. Figure 2 shows the model bathymetry and the incoming wave, which is a single solitary wave with a height of 0.39 m. The free surface elevation time series of the incoming wave is inputted from the left border, i.e., \(X=0.0\) m along \(Y\)-axis. The model bathymetry is formed using structured grids. The spatial grid size, \(\Delta x\), is selected as 0.05 m to achieve more accurate results. The time step, \(\Delta t\), is selected as 0.001 s to ensure stability with 0.05 m grid size. The duration of each simulation is 20 s, which is sufficient to observe the maximum wave height and the effect of the island on the propagation of the solitary wave. It is observed that it takes approximately 1 h to complete each simulation using 12-Core CPUs.  
Figure 1: The bathymetry provided with the benchmark problem.
## 2 The Online Dynamic Balance System Design for Double-Face Correction
### Discussion on the Balance Accuracy Problem of the Balance Disk Mechanism
#### 2.3.2 The Fit Clearance Problem  
In order to ensure that the balance disk can be adjusted freely with respect to the spindle, a suitable clearance between the balance disk and spindle must be left. When the balance disk is driven to rotate synchronously with the spindle, the rotating center line of the balance disk will deviate from the center line of the spindle caused by centrifugal force, and the additional unbalance shown in Figure 6 will be formed as spindle rotary.  
Figure 5: The radial position error of the balance disk.  
As Figure 6 shows, if the mass of balance disk is supposed as \(m\), the additional unbalance can be expressed as Equation (4).  
\[\overset{\rightarrow}{U_{a}}=m\cdot\Delta r \tag{4}\]  
In Equation (4), \(\overset{\rightarrow}{U_{a}}\) is the additional unbalance. Because the mass of the balance disk is large, the additional unbalance may be large even though the offset distance is small. In order to reduce the offset distance, the structure of the balance disk is improved to be a combined mechanism shown in Figure 7.  
In Figure 7, the double-shoulder sleeve and installing ring are fixed on the spindle, the side covers are fixed in the balance disk, suitable fit clearance can be ensured by the double-shoulder sleeve and side covers. Figure 7b shows that the non-uniformity of the fit clearance is reduced by the outward elastic power of four elastic claws. So, the offset distance can be reduced between the balance disk and spindle.  
Based on the above analysis, the main characteristics of the balance device proposed in the paper are summarized as follows compared with the related mechanisms.  
Figure 6: The deriving principle of additional unbalance. Notes: 1 unbalance mass; 2 balance disk; 3 spindle; \(\Delta\)r offset distance.  
Figure 7: The compensation principle of the fit clearance. (**a**) The fit condition between balance disk and spindle; (**b**) The four claws structure of balance disk. Notes: 1 unbalance mass; 2 spindle; 3 double-shoulder sleeve; 4 balance disk body; 5 installing ring; 6 side covers.  
1. The axial size of the balance device is small, and the synthetic vector of the two closer balance disks can be taken as in a face. In practical application, the structure of the machine tool spindle is very compact, and the balance device is difficult to apply in the actual machine tool if its axial size is larger.
2. According to the analysis in Section 2.3, the proposed balance device in the paper has symmetrical radial clearance between the balance disk and spindle whether it is in a stopping or running state, and the additional unbalance caused by the balance device is small.
3. The proposed balance device is a pure mechanical structure, and there is no electromagnetic interference in the motorized spindle system.
4. Location error may exist in the process of the radical position of the balance disk adjusted, but the maximum error can be accepted in application.
## 5 Simulation
### Example 1  
We now explore the positioning performance of the proposed method and estimate the deviation through simulation. The simulation is performed in 3D space, where the target is assumed to be at position \(\mathbf{T}=\begin{bmatrix}30&40&50\end{bmatrix}^{\prime}\) m. The sensor collects \(N=100\) azimuth/elevation angle measurements, which ensure the target is observable. The initial sensor is located at \(\mathbf{S}_{1}=\begin{bmatrix}0&0&0\end{bmatrix}^{\prime}\) m, the motion velocity components of the sensor in \(x,y\) and \(z\) directions are \(0.5\) m/s, \(0.8\) m/s, \(1\) m/s, respectively. The bearing angle measurement noise standard deviations \(\sigma=\xi\) are set in between \(\sqrt{0.01}\) and \(\sqrt{0.08}\) radian with the variance difference of \(0.01\) radian2. One thousand independent Monte Carlo experiments are run. We use the mean square error (MSE) and bias norm as evaluation indicators. The MSE and bias norm are defined as:  
Footnote 2: The mean square error (MSE) is defined as:  
\[MSE =\frac{\sum_{i=1}^{1000}\sum_{j=1}^{3}(\mathbf{\hat{T}}_{1}(j)- \mathbf{T}(j))^{2}}{1000}, \tag{90}\] \[bias =\frac{\sum_{i=1}^{1000}\sum_{j=1}^{3}|\mathbf{\hat{T}}_{1}(j)- \mathbf{T}(j)|}{1000}, \tag{91}\]respectively. The first simulation is about the MSE and bias norm comparison between the proposed BC method and the original 3D localization method (PLE) in [15]. As shown in Figures 5 and 6, the BC method has lower MSE and bias norm compared to the PLE method. In addition, the MSE and bias norm increase as measurement noise level increases. The positioning accuracy of the two methods is significantly improved by using the weighted instrumental variable method. The PLE-WIV estimator, as expected, suffers from more serious bias than the BC-WIV estimator.  
The second simulation based on positioning methods is carried out with the increase of sensor number. The bearing angle measurement noise standard deviation is assumed to be \(\sqrt{0.001}\) radian. The number of known sensors is changed from 60 to 260 with a difference of 40. The position of sensors is set as \(x_{\mathbf{s}_{i}}\in(0\ 50)\) m, \(y_{\mathbf{s}_{i}}\in(0\ 50)\) m, \(z_{\mathbf{s}_{i}}\in(0\ 50)\) m \((i=1,\ldots,n)\).  
Figure 5: MSE with increasing noise standard deviation with a variance difference of 0.01 radian2 for each method.  
Figure 6: Bias norm MSE with increasing noise standard deviation with the variance difference of 0.01 radian2 for each method.  
Under the same conditions of each experiment, as shown in Figures 7 and 8, the BC and the BC-WIV methods still show the best positioning performance. The MSE and bias norm decrease when the number of sensors increases. However, there can be low positioning accuracy occasionally when the number of sensors is large. This is because in the process of the random generation of sensors, the position of some sensors makes the target unobservable, resulting in low positioning accuracy.
###### Abstract
and others have highlighted the broader potential for integrating marine waste into bio-based polymer systems to promote sustainability [16; 17; 18]. Additional research has demonstrated enhanced mechanical, thermal, and flame-retardant properties in ABS composites using seashell-derived bio-fillers [19], with comprehensive reviews further underscoring the environmental and economic benefits of incorporating seashell and eggshell waste into polymer systems [20]. Despite these advances, most existing research has focused on seashell waste generated by seafood processing industries, which typically undergoes chemical or thermal pre-treatment to ensure consistency and reduce organic contamination. By contrast, tidal seashell debris--naturally deposited along shorelines--remains largely unprocessed, mixed in origin, and variable in particle morphology and mineral composition. This unprocessed nature introduces challenges for preparation and bonding but offers a sustainable, low-cost, and underexplored resource. Although seashell powder is primarily composed of calcium carbonate, tidal debris retains a natural layered nacreous microstructure, variable porosity, and trace mineral content (e.g., Mg, Sr), which can influence dispersion, crack deflection, and adhesion within an epoxy matrix [4]. Understanding these characteristics is critical for developing optimized, sustainable composites with enhanced mechanical integrity. Additionally, employing eco-friendly binders such as modified castor oil (MCO) and applying predictive statistical tools like response surface methodology (RSM) can enable precise formulation design and performance optimization. Therefore, this study aims to:  
* Investigate the use of unprocessed tidal seashell debris as a sustainable bio-filler in epoxy composites. * Optimize filler loading and resin composition using RSM to balance mechanical, thermal, and physical properties. * Characterize the resulting composites in terms of tensile strength, impact resistance, hardness, density, thermal conductivity, and microstructural features. * Evaluate the effects of tidal seashell filler morphology and dispersion on interfacial bonding and composite integrity.
# CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks
## 5 Prototyping and Performances Exploration
### Power Consumption Evaluation  
To evaluate the power consumption of the CERP, the Arduino-based SN is interfacing with MATLAB using a free support package for the Arduino [46], as shown in Figure 10.  
A voltage sensor, a current sensor (ACS712), and a voltmeter serve as our tools of choice for taking voltage and current readings. The sensor node is based on an Arduino and is powered by a pair of Lithium batteries, which can provide a voltage of up to 7 v. The support package handles reading the voltage value (or current) across the sensor attached to Arduino's port A0 (or A3), allowing us to collect measurements and visualize them in real-time.  
The power used by the CH is found by multiplying its voltage (Volt) by its current (Amps) for energy measurement:  
\[P=V\times I(Watt). \tag{10}\]  
Then, the energy consumed by the CH is the product of power (Watt) and time (hour) as follows:  
\[E=P\times t(WattHour). \tag{11}\]  
Thus, the energy consumed by the CH can be the instantaneous power (we use a sampling of 1 s for readings), as illustrated in Figure 11.  
As depicted in Figure 11, the proposed protocol beats the RPL in terms of energy efficiency, and this is because, in the CERP mechanism, the CH chooses the ideal pathways that decrease the power transmission without compromising high-reliability levels. However, due to the extensive and ongoing nature of the RPL construction, the CH battery may be quickly depleted by the many exchanged DAO, DIS, and DIO messages.  
Figure 10: Experimental test.
## 4 Results and Discussions
### Analysis of Bending Behavior of RC Beams with Different Number of SMA Bars
Thus, this effect together with the load transfer to the stiff Fe-SMA bars resulted in the increasing bending strength of the RC beam with increasing the number of the activated Fe-SMA bars, as observed in both the FE simulations and experiments in Figure 12. The experimentally observed cracking patterns shown in Figure 16 match very well with the FE simulation results, where it can be clearly seen that the cracks developed more severely in more concentrated distribution when a lesser number of activated Fe-SMA bars were used. Figure 14: Evolutions of equivalent stress fields of concrete with bending predicted by FE simulations of RC beam with activated 4, 3 and 2 Fe-SMA bars.
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 4 Experimental Result  
In this section, we present the numerical results to illustrate the effectiveness of the proposed model and algorithm. In addition, we verify the algorithm through a comparative analysis of our proposed method and the SCR method. The proposed method and the SCR method implement the algorithm by applying the HSV (Hue, Saturation and Value) Retinex model to the color image. HSV Retinex is intended to reduce color changes by applying the Retinex algorithm only to the value channel of the HSV color space, and then convert it back to the RGB domain.  
We note that the reflectance image obtained from Retinex is usually an overenhanced image. Therefore, we add a Gamma correction operation after the decomposition. Suppose \(L=exp(l)\) is the illumination function obtained from Algorithm 1, and \(S=exp(s)\) is the initial image; then the reflection function will be given by \(R=S/L\). The Gamma correction of \(L\) with an adjusting parameter \(\gamma\) is defined as \(L^{\prime}=W(\frac{1}{R})^{\frac{1}{\gamma}}\). In this experiment, we set the commonly used parameter \(\gamma=2.2\). \(W\) is the white value (it is equal to 255 in an 8-bit image, and also equal to 255 in the value channel of an HSV image), and the final result is given as \(S^{\prime}=L^{\prime}\cdot R\).  
We use the following parameter values to compare the two methods in the test: \(\alpha=1\), \(\eta=0.1\), \(\beta=1\). For the stopping criteria, we set \(\epsilon_{l}=\epsilon_{r}=0.001\) in Algorithm 1. We verified the algorithm using MATLAB 2019(a) version. When the size of the dictionary to be learned in the algorithm is small, the calculation of the proposed method can be completed faster. However, if the algorithm has a larger dictionary size, the reflectance function can be more complex, and the computational complexity will increase; therefore, we need to choose an appropriate dictionary size. In this paper, we experimentally set the size of the dictionary to \(11\times 11\) and use 100 filters (\(11\times 11\times 100\)), as seen in Figure 3. To generate the general dictionary for large-scale image data, we use 2000 images from ImageNet [23]. As seen here, Figure 2(a) without SVD and Figure 2(b) with SVD are similar in appearance, but the dictionary with SVD includes more general features that yield better reconstruction results (Sections 4.1 and 4.2). It also looks simpler and closer to the basis by using SVD. We collected training and test images for our experiment from [24,25], which was publicly released.
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## 3 Results and Analysis
### Nickel Adsorption Behavior of Soil Column  
In the cases of the 0 HBL, 0.5 HBL, and 1.0 HBL specimens, the Ni\({}^{2+}\) adsorption behavior followed a similar trend to that when the Ni\({}^{2+}\) solution was continuously supplied to the specimen. Initially, nickel ions were quickly adsorbed owing to the large amount of free exchangeable ions of absorbents. Subsequently, the adsorption reached equilibrium as the free sites for Ni\({}^{2+}\) became less available due to the constant adsorption.  
Figure 7: Change in the hydraulic conductivity of soil at flow rates of 0.04 mL/min and 0.2 mL/min.  
In general, the pure sand column had the lowest total amount of cumulative nickel adsorbed. GG in HBL improved the amount of Ni\({}^{2+}\) removed from the Ni\({}^{2+}\)-contaminated solution. A significant amount of nickel ion can be adsorbed with an increase in the GG content due to the increase in the functional groups and potassium cation exchange in the specimens provided by GG. The nickel adsorption trends (Figures 8 and 9) showed the potential maximal Ni\({}^{2+}\) adsorption ability of specimens with different GG-treated sand at different flow rates. Total nickel adsorption varied proportionally with GG content.  
Figure 8: Effect of flow rate on removal efficiency of nickel adsorption.  
The flow rate was essential in controlling the contact time between Ni\({}^{2+}\) and the adsorbent materials, which was crucial to determine the amount of nickel adsorbed. Therefore, there was a gap in the total amount of adsorbed Ni\({}^{2+}\) obtained from the low and high flow rates. For instance, under a low flow rate (i.e., 0.04 mL/min), the 0 HBL specimen could adsorb 40 mg of Ni\({}^{2+}\) ion as 200 mL of the Ni\({}^{2+}\) solution flowed through the specimen (Figures 8a and 9). Conversely, 29.98 mg of Ni\({}^{2+}\) was removed from the 200 mL nickel solution supplied at a high flow rate (0.2 mL/min) (Figures 8b and 9). This phenomenon also occurred in GG-treated sand. The 2.0 HBL adsorbed 86.89 mg and 133.98 mg nickel as 600 mL of the Ni\({}^{2+}\) solution was provided at flow rates of 0.2 mL/min and 0.04 mL/min, respectively (Figures 8 and 9).