esa-sceva/satcom-chunk-collection huggingface dataset:
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
## 3 Results and Discussion
### Selectivity of the Proposed Method  
The HPLC-UV analysis provided by the Chinese government used two measurement wavelengths: 428 nm for Tartrazine, Auramine O, and Metanil yellow; 500 nm for Sunset yellow, Orange II, Amaranth, Erythrosine, Acid red 73, and Azorubine [6]. The developed HPLC method also employed identical wavelengths as the Chinese method for quantitative analysis of illegal synthetic dyes. Specificity was evaluated by comparing the chromatograms of the blank solution, standard solution, and sample solution under selected chromatographic conditions. The chromatogram results are shown in Figure 3A-D. The ten synthetic colorants were well-separated within the range of retention times between 6.5 and 37.5 min. Three synthetic dyes, such as Tartrazine, Auramine O, and Metanil yellow, were separated and detected at 428 nm, and their retention time was 6.5, 34.6, and, 37.5 min, respectively. In contrast, seven synthetic colorants were analyzed at 500 nm and were well-separated within 32 min. Hawthorn fruit, Cornus fruit, and Schisandra fruit did not express any peaks in the blank samples at either 428 or 500 nm. There were no coeluting peaks to interfere with peaks of analytes at the retention time of ten synthetic dyes in three herbal medicines. In other words, their matrices did not influence ten analytes in HPLC quantitative analysis. This result indicated that the analyte peaks were pure and successfully separated under these HPLC analysis conditions. That is, the specificity of the proposed HPLC method was achieved.  
Figure 3: The HPLC chromatograms of synthetic dyes in Hawthorn fruit. **(A)** The blank sample solution at 428 nm; **(B)** the blank sample solution at 500 nm; **(C)** the mixed standard solution and spiked sample at 428 nm; **(D)** the mixed standard solution and spiked sample at 500 nm.
# Analysis of the Propulsion Performance and Internal Flow Field of an Underwater Launcher
## 2 Experimental Research
### Experimental Results and Discussions  
To conduct the experiment, mixed charge is used. One of the propellants has a fast burning rate, which is utilized to swiftly create gas to form a gas-curtain. The other has a slow burning rate and is utilized to continuously burn to maintain the gas-curtain and promote the projectile. Figure 3 depicts the chamber pressure for the three launching conditions. Table 2 displays the interior ballistics results for the matching launching conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Type** & **Characteristics** & **Jet Structure and Size/mm** & **Mass _ml_/g** \\ \hline
1 & A & Central nozzle & \(\phi\)3 & 45 \\
2 & B & Central nozzle & \(\phi\)4 & 45 \\
3 & C & Sidewall groove & \(4\times\phi\)2 & 45 \\ \hline \end{tabular}
\end{table}
Table 1: Structure and dimension of gas-curtain projectiles.  
Figure 1: Schematic diagram of the experimental system.  
Figure 2: The gas-curtain projectiles. (**a**) Physical picture, (**b**) schematic diagram of projectile structure.  
According to Figure 3 and Table 2, the propulsion performance of projectile B is the best with maximum pressure and muzzle velocity of 41.8 MPa and 177.5 m/s, respectively. The propulsion performance of projectile A and projectile C deteriorates in turn. The results demonstrate that the central nozzle promotes the creation of the gas-curtain, lowering launching resistance and achieving greater propulsion performance than the sidewall grooves. Furthermore, increasing the diameter of the central nozzle improves the propulsion performance.  
Compared with the experimental results of a fully submerged launch in Reference [19] (116.0 MPa/172.8 m\(\cdot\)s\({}^{-1}\)), it can be found that the three jet structures suggested in this paper can provide a good drag reduction effect.
## 4 Experiments and Analysis
### Ablation Experiment  
To evaluate the performance of our modules--SEAM (A), FRFN (B), and SC-Conv (C)--we conducted extensive ablation experiments on two datasets with different perspectives: the side-view driving behavior dataset SfdDD and the front-view dataset YawDD-E. The results are summarized in Tables 9 and 10. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **GFLOPs** & **Parameters (M)** & **Speed (ms)** & **FPS** & **Weight Size (MB)** \\ \hline YOLO-SGC & 23.4 & 8.832 & 13.61 & 73.50 & 5.2 \\ YOLOv10n & 6.5 & 2.266 & 9.54 & 104.82 & 5.5 \\ YOLOv11n & 6.3 & 2.583 & 8.12 & 123.21 & 5.2 \\ YOLOv12n & 6.3 & 2.557 & 10.40 & 96.17 & 5.3 \\ YOLO-AFR & 5.7 & 2.421 & 9.56 & 104.59 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of model complexity, speed, and file size. Figure 7: Our YOLO-AFR is compared with other real-time detectors in terms of accuracy and speed on the SfdDD dataset. The radius of the circle represents GFLOPs. From the results on the SfdDD dataset, it is evident that incorporating any of the three modules individually leads to noticeable performance improvements across different behavior categories. Specifically, the SEAM module significantly boosts performance for the
# Stress Analysis in Damaged Pipeline with Composite Coating
## 3 Results and Analysis  
The following geometrical and mechanical characteristics of the structure were accepted during the calculations: the outer diameter of the pipe \(d=$1220\,\mathrm{mm}$\); the thickness of the pipe wall \(h_{0}=$15.2\,\mathrm{mm}$\); the thickness of the coating \(h=$4.2\,\mathrm{mm}$\); the crack depth \(l\)--varied; pipe steel module \(E_{0}=$2.1\times 10^{5}\,\mathrm{MPa}$\) and Poisson's ratio \(\
u_{0}=0.3\); and the average value of the coating modules \(E_{c}=$2\times 10^{3}\,\mathrm{MPa}$\) and \(\
u_{c}=0.4\).  
For the maximum operational load \(p=$10\,\mathrm{MPa}$\), three cases of repair pressure were considered: \(a\)--\(p_{repair}=p\) (the pipe is operated without a coating); \(b\)--\(p_{repair}=0.5p\); and  
Figure 3: Schemes of finite element splitting of a crack’s surroundings.  
\(c\)--\(p_{repair}=0\) (the coating is applied to the unloaded pipe). The repair pressure was taken into account according to the procedure described in Section 2.2.3 for a defect-free pipe.  
Figure 4 presents the distribution of the equivalent von Mises stresses for a two-component ring with a crack \(l=3\,\mathrm{mm}\) deep at \(p=10\,\mathrm{MPa}\). The plots show that the coating reduces the stress concentration around the crack tip and therefore has a reinforcing effect.  
The dependences of the J-integral and SIF values on the length of the crack at different values of the repair pressure are shown in Figures 5 and 6. From the obtained results, we can conclude that coating the unloaded structure with a surface crack of 3 mm in depth, for example, reduced the magnitude of the SIF by 25%; at a repair pressure \(p_{repair}=0.5p\), we observed a decrease of 12.3%.  
Figure 4: Equivalent stresses around the crack in the coated pipe: (**a**) \(p_{repair}=p=10\,\mathrm{MPa}\), (**b**) \(p_{repair}=0.5p=5\,\mathrm{MPa}\), (**c**) \(p_{repair}=0\).  
As the accepted model does not take into account the possibility of a violation of adhesion or loss of the integrity of the coating, the results obtained regarding the mechanical effect of the thin coating on the boundary condition of the defective pipe should be considered an exaggeration. In fact, due to the inelastic deformation of the coating and the boundary layer, the mechanical contribution of the insulation to the delay of crack development is not significant. It is important to block the corrosive medium's access to the crack-like defect by the coating for a certain period of time.
# A Review of Vehicle to Vehicle Communication Protocols for VANETs in the Urban Environment
## 4 Simulation and Analysis  
### Simulation Setup  
For implementation and performance analysis through simulation, we have chosen two dynamical junction selection routing protocols (i.e., TFOR, and E-GyTARD) and one static junction selection routing protocol (i.e., DGSR). The simulations are carried out in GLOMOSIM (Global Mobile System Simulator) [27]. In our simulation, VanetMobiSim [28; 29] is used to generate the vehicular mobility traces in an area of 2400 \(\times\) 2000 m\({}^{2}\). The area contains 20 intersections and 32 bidirectional multilane roads in which vehicular nodes mobility is simulated.  
The vehicular nodes are randomly distributed over the multilane roads and move in both directions. The movements of the vehicles on the roads are based on the car following model or intelligent driving model [28; 29]. The vehicles speed depends on the kinds of the vehicles (like car, bus, truck, etc.) and nature of the roads. The rest of the parameters are summarized in Table 1.
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
## 2 Methodology
### Principle of TDLAS
Thus, the Doppler broadening can be ignored in the absorption spectrum, and the width of the absorption spectrum can be described using the normalized linear function, which is expressed as:  
\[\varphi(v)=\frac{\Delta v_{L}/2\pi}{\left(v-v_{0}\right)^{2}+\left(\Delta v_{L} /2\right)^{2}} \tag{5}\]  
where \(\Delta v_{L}\) is the collision broadening (Hz), which represents the difference between two frequencies corresponding to half of the maximum intensity of a spectral curve. After \(S(T)\) and \(\varphi(v)\) are determined, the gas concentration is obtained as follows:  
\[C=\frac{\ln I_{0}(v)-\ln I(v)}{S(T)\varphi(v)I}\cdot 22.4 \tag{6}\]  
where \(C\) is the gas concentration (ppm).
# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 5 Usability Evaluation of the Font Auto-Recommendation System  
### Implementation Results  
Based on the research findings, an interface has been developed to make it accessible to real users. This interface is built on the Django framework and is designed to have two main sections: one for users to input content and the other for displaying the recommended fonts. In the current system, fonts are recommended based on facial expressions and sentence emotion analysis. Users can choose to input content by selecting a facial expression or a sentence from the available options. Figure 4 represents the implementation of a font recommendation system on the web. At the top, there is an instructional message displayed, and the two buttons below allow the selection between entering an expression or a sentence. This example involves inputting an image with an expression and matching the emotions in the image with the top five recommended fonts. Clicking on the button to the right of the font enables redirection to a webpage featuring that font.  
The input photos and sentences undergo emotion extraction through deep-learning emotion analysis models. Subsequently, a mapping model applying Pearson correlation coefficients transforms them into font keyword values. As a result, the transformed values are based on the same criteria as font keywords, enabling similarity evaluation. The transformed values are compared to the font keyword values using cosine similarity, and fonts with the highest similarity are displayed at the top.  
Figure 3: Illustration of an example depicting the emotion ’JOY’ generated from the input sentence, showcasing the corresponding emotions of associated fonts.
## 3 Results
### The After-Growth Evaluation of Seedings Produced in Biochar-Anended Media
#### 3.3.2 Basil Plant Growth  
Basil seedlings from different biochar-amended mixes (except 50% HB) all had significantly lower GI at transplanting compared to those from the commercial propagation mix (Figure 7a). However, after growing in CS1 for four weeks, all plants from SBB- and HB-amended mixes (except 10% SBB, 30% SBB and 50% SBB) had similar GI and SPAD (except 30% SBB, 70% SBB, 100% SBB, 30% HB, 50% HB and 70% HB) to the control (Figure 7a,b). In addition, basil plants from all biochar-amended mixes had similar LDW (except 10% SBB, 30% SBB and 50% SBB) and FDW (except 10% SBB, 30% SBB, 50%  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Mixes \({}^{\text{z}}\)** & **Stalk DW(g)** & **Leaf DW(g)** & **Fruit DW(g)** & **Total DW (g)** \\ \hline SBB:PM:P(10:60:30) & 1.7 \(\pm\) 0.1** & 6.5 \(\pm\) 0.1 ** & 0.3 \(\pm\) 0. 0** & 8.5 \(\pm\) 0.2** \\ SBB:PM:P(30:40:30) & 2.0 \(\pm\) 0.2 ** & 7.6 \(\pm\) 0.4 & 1.0 \(\pm\) 0.3** & 10.6 \(\pm\) 0.9** \\ SBB:PM:P(50:20:30) & 2.7 \(\pm\) 0.2 & 8.8 \(\pm\) 0.5 & 0.9 \(\pm\) 0.1** & 12.5 \(\pm\) 0.6** \\ SBB:PM:P(70:0:30) & 3.2 \(\pm\) 0.3 & 8.7 \(\pm\) 0.4 & 1.3 \(\pm\) 0.2** & 13.2 \(\pm\) 0.9** \\ SBB:PM:P(100:0) & 3.5\(\pm\) 0.2 & 8.6 \(\pm\) 0.2 & 2.4 \(\pm\) 0.2** & 14.5 \(\pm\) 0.4** \\ HB:PM(10:90) & 2.4 \(\pm\) 0.3* & 8.9 \(\pm\) 0.5 & 1.3 \(\pm\) 0.2** & 12.6 \(\pm\) 0.8*** \\ HB:PM(30:70) & 2.9 \(\pm\) 0.3 & 7.8 \(\pm\) 0.4 & 3.2 \(\pm\) 0.7** & 13.9 \(\pm\) 1.0** \\ HB:PM(50:50) & 3.2 \(\pm\) 0.2 & 8.1 \(\pm\) 0.2 & 4.0 \(\pm\) 0.5* & 15.4 \(\pm\) 0.5* \\ HB:PM(70:30) & 2.6 \(\pm\) 0.2 & 8.4 \(\pm\) 0.2 & 2.0 \(\pm\) 0.3** & 12.9 \(\pm\) 0.6*** \\ Control & 3.4 \(\pm\) 0.1 & 8.1 \(\pm\) 0.5 & 7.7 \(\pm\) 2.3 & 19.2 \(\pm\) 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stalk, leaf, and fruit dry weight (g) of tomato seedlings from biochar-amended mixes after four weeks in the commercial substrate. (*, ** indicate a significant difference from the control using [PERSON]’s test at \(p\leq 0.05\) and \(p\leq 0.01\), respectively).  
Figure 6: The growth index **(a)** and SPAD **(b)** of tomato seedlings from biochar-amended mixes after four weeks in commercial substrate. SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar; CS1 = commercial growing substrate. SBB (10%, 30%, 50%, 70% and 100%; by vol.) incorporated with 30% perlite with the rest being peat moss; HB (10%, 30%, 50% and 70%; by vol.) incorporated with peat moss. (*, ** indicate a significant difference from the control using [PERSON]’s test at \(p\leq 0.05\) and \(p\leq 0.01\), respectively).  
SBB and 10% HB) in comparison to the control (Table 5). Plants from SBB-amended mixes all had significantly lower SDW and TDW compared to the control; however, plants from HB-amended mixes all had similar SDW (except 10% HB) and TDW (except 10% HB and 70% HB) to the control.
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 4 The Proposed Game-Based Pricing Method
### Constructing the Game Tree
#### 4.2.1 Step One: Finding the Candidate Virtual Machines  
In the process of constructing the game tree, the proposed method checks each physical machine to see whether it is possible to create the requested virtual machine of the user on it. This checking is necessary to constitute the action set of the first player (user). Creation of the requested virtual machine might not be possible on all physical machines due to the lack of available resources or the adverse effect of other virtual machines on the performance. Running multiple virtual machines at the same time on a single physical machine can have a negative impact on their performance, as they share resources [31]. It would be advantageous to consider these effects in order to achieve optimal resource allocation, enhance system efficiency, and ensure a fair price.  
In cases where multiple virtual machines placed on the same physical machine are responsible for executing programs with high consumption of a particular resource, such as CPU and memory, these effects would be more serious [32]. In such cases, the over-utilized resource could become a bottleneck or a breakpoint for the system and eventually lead to service degradation. In such instances, the resource that is being excessively used could potentially become a hindrance or a stopping point for the system, ultimately resulting in a decline in service quality. According to research conducted and documented in [33], the time it takes to complete a task that requires a significant amount of processing power or memory can be twice as long or even worse if another task that also demands a lot of processing power or memory is using the same physical resource at the same time. Building  
Figure 3: The tree of the proposed game.  
upon the findings of [33], the proposed approach takes these conflicts into consideration and excludes the physical machines that are unable to promptly execute the user's task from the process of constructing the tree. Additionally, physical machines that do not have enough available resources are also eliminated from the tree construction process.
## 2 Review of Existing Studies
### Regional Studies
Results from these calibration-based approaches to area correction suggest that while calibration seems to work well for homogeneous areas, for heterogeneous areas, where irrigated fields are scattered against a predominantly non-vegetated background, coarse resolution vegetation index values become rather low and consequently the regression relations are less reliable. However, comparison of multiple data sources improves confidence in the classification and identifies areas requiring more intensive fieldwork [49]. Another approach directly from the remotely sensed observations is to use a regression algorithm to derive the fraction of irrigated area within each pixel that had been identified as having irrigation [20]. However, both approaches establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of time and resources for ground-truthing.
# Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
## 3 Results
### Noise Measurement of the Underwater Glider
#### 3.1.2 Glider's Operation Noise Measurement at Sea  
The underwater glider dived to a depth of 10-200 m and repeated six yo missions during about 6 h. It took about one hour for each yo. Figure 5 shows the command values of the driving part according to the water depth when the underwater glider is operated at sea. The driving-unit command value is \(-1\) (low) to \(+1\) (high).  
Figure 4: Measurement noise level when the underwater glider operates in the laboratory tank: (**a**) Piston, (**b**) Pitch, (**c**) Air Bladder, and (**d**) Altimeter.  
Figure 5: Command values when the glider is operating at sea.  
When the underwater glider changes from descent to ascent, the values of the buoyancy control (Vol Piston) and the Pitch control (Pitch) change from \(-1\) to \(+1\); these values are negative during descent and positive during ascent. The Fin controller operated once every 3 to 4 s and the command value changed fast, regardless of descent or ascent. It can be seen that the Air Bladder command value is 0 except near the end when it is 1 as the bladder inflates raising the glider to the surface thereby allowing communication with the operator via satellite.  
The Fin and pumped CTD noise levels were calculated from recorded data at sea. The same hydrophone was mounted on the same position of the underwater glider body as at tank experiment. Due to the ambient noise contained in the recordings at sea, the reference sound level of Fin and pumped CTD is greater than that in the laboratory tank. The Fin noise level rose 4-5 dB at frequencies of 800-950 Hz (Figure 6a). The pumped CTD noise level rose by more than 10 dB at discrete frequencies of 357, 410, 710, and 1450 Hz (Figure 6b).
## 3 Evaluation Methods of the Performance of GSP
### Common Laboratory Testing Methods
#### 3.1.3 Moisture Resistance  
GSP is considered as a promising anti-moisture pavement as it prevents water permeation by its high density and low residual void. This property can be assessed by freeze-thaw test and Immersion Marshall test. The freeze-thaw test is divided into the freeze-thaw splitting test (ST) and freeze-thaw indirect tensile strength (ITS) test. Thus, the results of retained Marshall stability (RMS) and tensile strength ratios (TSR) are summarized and illustrated in Figure 3[6; 38; 49; 70; 77; 103; 104; 110; 111].  
Moisture resistance of GSP is significantly enhanced with high void rate OMA. The reason is that a high void rate can lead to void interconnection and saturated grouting [112]. From the figure, results of Immersion Marshall testing are commonly higher and easier to reach design requirement than those in freeze-thaw tests, because of the more rigorous experimental environment of the latter. There is another interesting phenomenon where some RMS results exceed 100%. This phenomenon may be triggered by further hydration of cement in GSP. Therefore, from this aspect, the freeze-thaw test may be more accurate than the RMS test in the evaluation of moisture resistance for GSP.
## 5 Conclusions  
This study comprises an analysis of four different algorithms (SVM, ANN, BNN, and RF) to predict the pressure drop in Herschel-Bulkley fluids flowing through eccentric and concentric horizontal annuli. There are six different input parameters such as Di/Do, e, n, K, \(\tau_{y}\), Q, and one output for prediction pressure loss (\(\Delta\)P). RMSE, MAE, and R\({}^{2}\) are used to evaluate the performance of the proposed algorithms. SVM's overall performance is found to be the lowest with an RMSE of 21.96 kPa, MAE of 18.13%, and R\({}^{2}\) of 0.875. The performance of ANN is slightly better than SVM with an RMSE of 19.58 kPa, MAE of 11.52%, and R\({}^{2}\) of 0.90. Alternatively, BNN is used which employs the basic architecture of ANN but additionally considers SMBO to optimize and determine optimum number of hidden layers, number of neurons, learning rate, dropout ratio, and best activation function for the hidden layer. The RMSE, MAE, and R\({}^{2}\) for train data are 5.3 kPa, 2.85%, and 0.99, whereas, and test data exhibit 8.38 kPa, 3.7%, and 0.989, respectively. The results clearly indicate that BNN performance is very good on training and test dataset and the model can predict \(\Delta\)P with very high accuracy. In comparison to BNN, RF reports an RMSE of 4.7 and 9.09 kPa, MAE of 1.74% and 3.4%, and R\({}^{2}\) of 0.99 and 0.986 for train and test dataset. The performance of BNN and RF are almost similar, and both can generalize the pressure loss calculation in eccentric and concentric annulus for Herschel-Bulkley fluids, but the performance of RF is better than BNN at low flow rate. The comparison of RF predictions with the theoretical result shows much better agreement than BNN. However, feature analysis concludes that eccentricity (e) has no impact on \(\Delta\)P prediction. Herschel-Bulkley fluid parameters, such as flow behavior index (n),  
Figure 19: Feature importance of input parameters based on RF algorithm.  
Figure 18: Comparative analysis of the theoretical solution by [PERSON] et al. [19] and the Random Forest.  
consistency index (K), and shear stress (\(\tau_{y}\)) have less than 5% effect on \(\Delta\)P prediction, while diameter ratio (Di/Do) and flow rate (Q) accounts for more than 95% on \(\Delta\)P.  
Conceptualization, A.K. and S.R.; Methodology, A.K., S.R., S.U.I., and P.V.; Software, A.K.; Validation, A.K.; Formal Analysis, A.K.; Investigation, A.K., S.R., S.U.I., and P.V.; Resources, A.K. and S.R.; Data Curation, A.K.; Writing- Original Draft Preparation, A.K.; Writing- Review and Editing, S.R., S.U.I., and T.G.; Visualization, A.K. and S.R.; Supervision, S.R., P.V., and T.G.; Project Administration, A.K., S.U.I., and T.G.; Funding Acquisition, S.R. All authors have read and agreed to the published version of the manuscript. This research is funded by YUTP, grant number 0153 AA-E87.  
This work is supported by Petroleum Engineering Department and Institute of Hydrocarbon Recovery at Universiti Teknologi PETRONAS, Malaysia.  
The authors declare no conflict of interest.