esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Coagulation Process of Bagsase as Natural Coagulant  
A jar test was carried out to identify the suitability of extracted bagasse for turbidity removal. Figures 6 and 7 shows jar test with different coagulant dosage and pH, respectively. Based on Figure 6, the highest turbidity removal was recorded at 58.5% by using a 50 mg/L coagulant dosage. However, all recorded turbidity removal with pH 7 at initial turbidity of 200 NTU is less than 60%. A study by [PERSON] et al. [15] and [PERSON] et al. [16] also shows bagasse requires a lower coagulant dosage compared to other sources to obtain optimum turbidity removal. [PERSON] et al. [20] used banana stem as a source of natural coagulant for turbidity removal in coolant wastewater. Based on the study, the banana stem requires a high coagulant dosage for optimum turbidity removal. The difference in optimum coagulant dosage required is probably related to the active coagulant agent content in bagasse and banana stem. polysaccharide content in bagasse is more than 40% while polysaccharide content in the banana stem is less than 30% [52; 53]. In a comparison of the polysaccharide content and coagulation performance, higher polysaccharide content or active coagulant agent in natural coagulant will result in lower coagulant dosage required for optimum turbidity removal [54]. A higher coagulant dosage is undesirable as it will not only increase the cost but also can produce more sludge [55].  
Jar test was further carried out to determine the optimum pH from the obtained optimum coagulant dosage. Figure 7 shows the turbidity removal recorded by using extracted bagasse on different pH. Based on Figure 7, the optimum pH of extracted bagasse was pH 4 with 95.9% turbidity removal while the lowest turbidity removal was 81.1% which was recorded at pH 12. Bagasse is more suitable to be used in acidic conditions. Generally, the pH of wastewater ranged from pH 4 to 10 [56].  
Based on this finding, the possible coagulation mechanism of bagasse is interparticle bridging. Charge neutralization is not the possible coagulation mechanism of bagasse. The difference in maximum and minimum turbidity removal on different pH by using bagasse is small as it only differs by 14%. According to [PERSON] et al. [9], if there is no pH effect on turbidity removal, the coagulation mechanism is not due to charge neutralization. [PERSON] et al. [57] reported that the coagulation mechanism of _Jatropha curcas_ is charge neutralization as the difference between maximum removal and minimum removal by using different pH is around 50%. Therefore, the possible coagulation mechanism of bagasse (_Saccharum officinarum_) is interparticle bridging because unlike charge neutral  
Figure 5: SEM images of (**a**) raw bagasse; (**b**) extracted bagasse at 100 \(\upmu\)m magnification.  
ization, interparticle bridging is least affected by pH but at lower pH, polymeric chains that are responsible for interparticle bridging will be extended and can attach to more pollutants [58; 59].
## 2 Literature Review
### Similar Anchored EB-FRP Shear-Strengthening Applications  
At the time of design for this retrofit project, studies on the use of FRP anchors for shear-strengthening FRP applications on flanged beams were somewhat limited [14; 19; 25; 26; 27; 28]. Some of the strengthening concepts and design recommendations for the FRP laminates and FRP anchors in these studies were used by the authors as a reference for design. For example, both [PERSON] et al. [25] and [PERSON] et al. [28] studied fully closed wrap schemes achieved using FRP splice anchors passing through either the flanges or the web-slab interface, respectively, and demonstrated the outstanding performance of these hooplike shear-strengthening concepts when compared to more traditional U-wrap solutions. Also, many of the design recommendations for FRP-embedded anchors provided in the Austin, Texas, report by [PERSON] et al. [19] were incorporated in the retrofit design, as later discussed in Section 5.2. However, most of these studies (in fact, all except for [PERSON] et al. [25]) focused on the strengthening of gravity beams tested under monotonic loading, making their results not extractable to moment frame beams. To the author's knowledge, tests of FRP-shear-strengthened RC MRF T-beams under large-amplitude, fully reversed cyclic loading had only been performed at the time by [PERSON] et al. [25], [PERSON] [29; 30], and [PERSON] and [PERSON] [31]. Among these cyclic studies, only [PERSON] et al. [25] used embedded FRP anchors, and in all cases, the specimens were relatively small (specimens in [25] had depths of 400 mm [15.8 in], 500 mm [19.7 in], and 600 mm [23.6 in]; cross-sectional dimensions (depth \(\times\) width) were 360 mm \(\times\) 120 mm [14.2 in \(\times\) 4.7 in] in [29] and [30], and 350 mm \(\times\) 150 mm [13.8 in \(\times\) 5.9 in] in [31]).
## 3 Results
### Time-Domain Results of Vibration Velocity Response  
Figure 5 depicts the diagram of the variation of the vibration velocity of the road surface with time at various driving speeds of medium-sized buses. Scenario one and scenario 2 show similar shapes in the time-domain curve of vibration velocity. The vibration velocity drops a little as the axle approaches. Then it climbs to its maximum, drops to its minimum, and returns close to zero. It generates an N-shaped curve [26; 27; 28]. The timing of the wave peak's appearance coincides with the arrival of the axle. When the medium-sized bus passes through the section at a speed of 20 km/h, the curve is double N-shaped. With the acceleration of driving speed, the double N-shaped gradually transformed into a single N-shaped. It is because the front and rear axles of the driving vehicle have two excitation effects on the road that cause this phenomenon. At a slower driving speed, the excitation interval is longer and can act independently on the test section, producing two N-shaped. At a faster driving speed, the two excitation moments are comparably close. Hence, a superposition effect of the two excitation effects of the traveling load on the test section. The vibration velocity is expressed as an N-shaped in the time-domain curve [29; 30].  
Figure 5: Time-domain curve of vibration velocity.  
In addition, the period of the pavement vibration velocity generated by the random excitation of the travel load is less affected by the vehicle's travel speed. For schemes 1 and 2, the response period of traveling wave load vibration velocity is around 1.5 s. Nevertheless, scheme 2 has a little longer response time than scheme 1. In the experiment, polypropylene resin material is utilized to create a geocell. A geocell has a high modulus of elasticity, which results in a high circumferential stress for aeolian sand provided by the geocells. The geocell-reinforced aeolian sand layer is a structurally interconnected layer. Allowing the upper roadbed and superstructure layer to be excited for a longer duration [31; 32]. In contrast, in gravelly soils, the connection between soil particles and gravel is rather loose. When subject to loads from above, relative displacement will occur. The upper roadbed becomes less sensitive to excitation, hence scheme 2 has a longer response period than scheme 1.
# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 3 The Proposed Approach
### Loss Function
#### 3.2.1 2D Spatial Loss  
Let \(p_{t}\) be a pixel in the RGB image \(I_{t}\), and \(P_{t}\) be the point in the 3D space corresponding to \(p_{t}\). According to the pinhole camera model, the relationship between them can be described as  
\[P_{t}=d_{t}K^{-1}p_{t} \tag{1}\]where \(d_{t}\) is the corresponding depth value of the pixel point \(p_{t}\) in the depth image \(D_{t}\) and \(K\) is the camera intrinsic matrix.  
Then, based on the transformation relationship of the 3D space coordinate system, the transformation matrix \(\hat{T}_{t,t+1}\) obtained by the RCNN network is used to predict point \(\hat{P}_{t+1}\) at time \(t+1\). \(\hat{P}_{t+1}\) can be calculated by  
\[\hat{P}_{t+1}=\hat{R}_{t,t+1}d_{t}K^{-1}p_{t}+\hat{t}_{t,t+1} \tag{2}\]  
where \(\hat{R}_{t,t+1}\) and \(\hat{t}_{t,t+1}\) are rotation matrix and translation vector, respectively, which can be derived from the transformation matrix \(\hat{T}_{t,t+1}\).  
Next, according to the pinhole camera model, the predicted point \(\hat{P}_{t+1}\) is converted back into a predicted RGB image \(\hat{I}_{t+1}\) by  
\[\hat{p}_{t+1}=\frac{1}{\hat{d}_{t+1}}K(\hat{R}_{t,t+1}d_{t}K^{-1}p_{t}+\hat{t }_{t,t+1}) \tag{3}\]  
where \(\hat{d}_{t+1}\) is the z-axis coordinate value of \(\hat{P}_{t+1}\), which is also the depth value of the pixel point \(\hat{p}_{t+1}\).  
We perform the above calculation on each pixel in RGB image \(I_{t}\) to obtain the predicted RGB image \(\hat{I}_{t+1}\). Regarding the pixels with null values, we use bilinear interpolation to estimate them.  
Using the same method, we can get \(\hat{I}_{t}\) by  
\[\hat{p}_{t}=\frac{1}{\hat{d}_{t}}K(\hat{R}_{t+1,t}d_{t+1}K^{-1}p_{t+1}+\hat{t }_{t+1,t}) \tag{4}\]  
where \(\hat{p}_{t}\) is a pixel in \(\hat{I}_{t}\), \(\hat{R}_{t+1,t}\), \(\hat{t}_{t+1,t}\) are the rotation matrix and translation vector, which can be obtained by the transformation matrix \(\hat{T}_{t+1,t}\), the relationship between \(\hat{T}_{t+1,t}\) and \(\hat{T}_{t,t+1}\) can be described as  
\[\hat{T}_{t+1,t}=\hat{T}_{t,t+1}^{-1} \tag{5}\]  
Finally, the 2D spatial loss is derived by  
\[L_{2}=\sum_{t=1}^{N-1}\left(\overline{|I_{t}-\hat{I}_{t}|}+\overline{|I_{t+1 }-\hat{I}_{t+1}|}\right) \tag{6}\]
## 4 Results and Discussion
### Single Particle Sizing Measurement
The minimal available particle size discrimination by this system was around a half wavelength (\(\sim\)300 nm), and a considerable detection size range from 200 nm to 10 \(\upmu\)m was approached, which can be qualified in clinical red blood cell detection.
## 2 The Design of the Proposed Antenna
### Simulation Results of the Proposed Antenna  
Figure 5 shows the S11, boresight gain, and total efficiency based on the existence of a harmonic suppression filter in the proposed antenna. The simulated S11 (\(\leq-10\) dB) bandwidth was 2.96-3.67 (21.5%) and 3.05-3.46 GHz (12.4%), respectively. This shows that the reflection coefficient is completely satisfied in the operating band (3.25-3.35 GHz), where the antenna has a wide HPBW. In the simulation result of the DRA with the filter, the boresight gain of the operating band with wide HPBW is 2.41-1.74 dB. The reason why the gain seems relatively high at a lower frequency (<3.25 GHz) is that the matching characteristics are poor, but the directivity is high because it operates in the fundamental mode, which does not have a wide HPBW characteristic. Although the gain in operating frequency seems low, as can be seen in Figure 5c, the antenna efficiency has no problem. The antenna gain was comparable in the operating band, regardless of the existence of a filter; nonetheless, radiation in a higher frequency was sufficiently suppressed. This indicated that harmonic suppression had been properly performed, and unwanted radiation was sufficiently suppressed.  
According to the simulation, the resulting changes in the radiation pattern of the antenna (based on the presence of a filter) are shown in Figure 6. In the case of the antenna integrated with a filter, the antenna gain in the operating band was reduced slightly due to the loss that occurred in the feeding line, but the pattern was very similar to one without a filter. However, at 6.5 GHz, the harmonic band was sufficiently suppressed, as shown. In addition, based on the results of the simulation, the 3-dB beamwidths of the antenna were 184.6\({}^{\circ}\) and 189.3\({}^{\circ}\) in the \(x\)-\(y\) and \(y\)-\(z\) planes, respectively, at 3.25 GHz, and 217.1\({}^{\circ}\) and 198.2\({}^{\circ}\), respectively, at 3.3 GHz.  
Figure 4: Proposed filter and the S-parameter. (**a**) A microstrip line model of the proposed filter optimized for the antenna base and (**b**) the simulated S-parameter.  
Figure 3: S-parameter of the proposed seventh-order Chebyshev LPF and step-impedance of the same low-pass filter.  
Figure 5: Simulation results of the proposed antenna with and without the filter: (**a**) the S11, (**b**) the boresight gain, and (**c**) total efficiency of the antenna.  
Figure 6: Simulated far-field radiation patterns of the proposed antenna with and without a filter: (**a**) 3.25, (**b**) 3.3, and (**c**) 6.5 GHz.
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 6 Conclusions  
In this paper, ANSYS was used to construct the tire-road coupling model of an airport runway, simulate the impact process of aircraft landing, and investigate the influence of tire pressure, landing pitch angle, and sinking speed on the dynamic response of an airport pavement. According to the simulation results, with the increase in the tire pressure and sinking speed, the maximum values of equivalent stress, effective strain, and vertical displacement of different structures increased, and the effect of sinking speed was more significant. With the increase in the landing angle, there was a slight decrease in the response parameters, but the overall change was not significant. In the process of calculating, it was found that the flexural tensile stress at the bottom of the surface, the strain at the top of the soil foundation, and the vertical displacement at the top of the surface are the indicators that should be paid attention in the pavement structure to ensure safety. In addition, the time for each structure of the pavement to reach the maximum stress and strain was constant, while the displacement time was slightly delayed, and the farther the distance between the structure and the top of the surface, the longer the time required.  
In summary, the influence of sinking speed on the dynamic response of the airport pavement was the most significant, followed by tire pressure, and the landing pitch angle was negligible. During the operation of an airport, attention should be paid to indexes such as the stress at the bottom of the surface, the strain at the top of the soil foundation, and the vertical displacement at the top of the surface. Sensors can be used to monitor changes in the above indexes in real time to determine the best time for pavement maintenance and ensure the operational safety of the airport.  
In this paper, only an asphalt mixture pavement was studied, and cement concrete pavements were not included. Meanwhile, the influence of environmental factors on the pavement during aircraft landing was not taken into account, and a coupled tire-road surface-environment model can be constructed in the future to obtain a better simulation of the impact effect on airport pavements during aircraft landing.  
Q.M.: formal analysis, writing--original draft; K.Z.: funding acquisition, methodology; M.S.: methodology. All authors have read and agreed to the published version of the manuscript.  
The research described herein was funded by the National Key R&D Program of China (Grant No. 2021 YFB2601200, sponsored by the Ministry of Science and Technology of the People's Republic of China). This financial support is gratefully acknowledged.  
Not applicable.  
Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 2 Experimental Study
### Working Conditions and Experimental Procedures  
Table 3 shows the experimental working conditions. Four factors were considered in this study: the digging distance \(L_{d}\), the distance from the duct outlet to the closed end \(L_{0}\), the slope of the uphill section \(\theta\), and air supply speed \(u_{0}\). In addition, the slope of the downhill section was set to 2% in the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instruments** & **Range** & **Accuracy** \\ \hline Hot-wire anemometer & 0.3–30 m/s & 1\% \\ Micro differential pressure transmitters & \(-25\) to 25 Pa & 0.4\% \\ CO flow meter & 0.1 to 1 L/min & 4\% \\ CO detector & 0 to 1000 ppm & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranges and accuracy of experimental measuring instruments.  
Figure 4: Details of experiment: (**a**) experimental fan, (**b**) hot-wire anemometer, (**c**) fixed-wind-speed-measuring Piot tube and micro-differential pressure transmitter, and (**d**) CO concentration detector.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & \begin{tabular}{c} **Slope of the Uphill** \\ **Section** \\ **\(\theta\) (\%)** \\ \end{tabular} & **Tunneling Length** & \
\begin{tabular}{c} **Distance from the Duct Outlet** \\ **to the Closed End** \\ **\(L_{d}\) (m)** \\ \end{tabular} &
\begin{tabular}{c} **Air Velocity at the Duct** \\ **to the Closed End** \\ **\(u_{0}\) (m/s)** \\ \end{tabular} \\ \hline
1–45 & 2 & 12, 16, 20 & 1, 2, 3 & 2, 4, 6, 8, 10 \\
46–90 & 4 & 12, 16, 20 & 1, 2, 3 & 2, 4, 6, 8, 10 \\
91–135 & 6 & 12, 16, 20 & 1, 2, 3 & 2, 4, 6, 8, 10 \\
136–180 & 8 & 12, 16, 20 & 1, 2, 3 & 2, 4, 6, 8, 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detail of test conditions.  
The experimental steps can be performed as follows: (1) Start the fan, calibrate the wind speed at the duct outlet, and carry out the next steps after the value has stabilized. (2) Open the software for acquiring wind speed and CO concentration detectors. Then turn on the fan to a specific working condition. (3) Open the CO cylinder valve and regulate the gas flow meters after the wind flow field has stabilized. Release at 600 mL/min for 25 s of quantitative CO. (4) Save the data after all CO detectors show a concentration of 0. (5) Turn off the fan. For other conditions, repeat steps (1) to (5) after adjusting the tunnel model. Additionally, the moment when the release of CO starts has been defined as moment 0 s (at this moment, \"ventilation time\" = 0 s). Repeat the experiment 3 times for each working condition.
# Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model
## 5 Conclusions  
This study has proposed a machine learning approach for predicting the machining precision of the turning process based on the values assigned to the machining depth, rotational speed, feed rate, and cantilever cutting length, respectively. Based on the experimental results obtained from the Taguchi factorial design experiment, the contributions of this study are summarized as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & \(R^{2}\) \\ \hline Random Forest & 0.0107 & 0.012336 & \(-\)0.092 \\ Decision Tree & 0.0068 & 0.008034 & 0.537 \\ XGBoost & 0.0067 & 0.007887 & 0.554 \\ SMOGN-GA-XGB & 0.0064 & 0.007411 & 0.606 \\ SMOGN-GWO-XGB & 0.0061 & 0.007081 & 0.64 \\ SMOGN-PSO-XGB & 0.0062 & 0.007213 & 0.627 \\ SMOGN-CPSO-XGB & 0.0059 & 0.006878 & 0.661 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics of various models.  
Figure 23: Prediction performance of different models.  
1. The XGBoost model yields a better prediction performance than the random forest model or decision tree model. It has further been shown that the prediction performance of the XGBoost model can be enhanced through the use of a synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) and a center particle swarm optimization (CPSO) algorithm.
2. According to the index of R\({}^{2}\), it can be observed that the values of R\({}^{2}\) for the SMOGN-CPSO-XGB and SMOGN-GWO-XGB models are 0.661 and 0.64, respectively. It reveals that the SMOGN-CPSO-XGB and SMOGN-GWO-XGB models have better prediction effects than the others and perform well.
3. For the comparison of performance for the SMOGN-CPSO-XGB and SMOGN-GWO-XGB models, it is evaluated by MAE, RMSE, and R\({}^{2}\) to show that the performance of the SMOGN-CPSO-XGB model can achieve a MAE of 0.0059, and an R\({}^{2}\) score of 0.661 is significantly better than the SMOGN-GWO-XGB model. Accordingly, the SMOGN-CPSO-XGB model has the best predictive ability and effect for turning process.
4. This study provides a rapid and reliable means of predicting the turning quality, optimizing the turning parameters or machining error in industrial settings without the need for time-consuming and expensive trial-and-error experiments.  
Conceptualization, C.-C.W.; methodology, C.-C.W.; software, P.-H.K.; validation, C.-C.W., P.-H.K. and G.-Y.C.; formal analysis, P.-H.K. and G.-Y.C.; investigation, C.-C.W.; writing--original draft preparation, C.-C.W., P.-H.K. and G.-Y.C.; writing--review and editing, C.-C.W.; visualization, C.-C.W.; supervision, C.-C.W.; project administration, C.-C.W.; funding acquisition, C.-C.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministry of Science and Technology in Taiwan, grant number MOST 110-2221-E-167-019. The authors declare no conflict of interest.
## 4 Experiments
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
Commonly, evaluation metrics based on a pedestrian's single ground-truth trajectory are used. These metrics include [6]:  
1. The Average Displacement Error (ADE): The mean Euclidean distance between the predicted and ground-truth trajectories across all time steps.
2. The Final Displacement Error (FDE): The distance between the last predicted point and the corresponding actual point at the final time step.  
To assess the performance of the multimodal predictions, metrics that consider multiple possible trajectories were adopted, including:  
1. The Minimum Average Displacement Error (minADE): The minimum ADE of the predicted trajectories between all possible real trajectories.
2. The Minimum Final Displacement Error (minFDE): The minimum FDE of the predicted endpoint over all possible real endpoints.
## 3 Results and Discussion
### EE2 Biodegradation in Domestic Secondary Effluents  
Since _R. zopfi_ presented superior EE2 biodegradation results in both tested media, it was further selected for an additional experiment with secondary effluents from a sanitary wastewater treatment plant. The effluent water quality parameters are presented in Table 1. Figure 2C presents the EE2 biodegradation rate by _R. zopfi_ SBP capsules in the secondary effluents. Unexpectedly, and contrary to the results obtained with the other media, most of the EE2 reduction occurred during the first hours of incubation, from 2.2 mg/L to 0.786 mg/L (64.24% reduction). After 24 h of incubation, the EE2 concentration was 0.33 mg/L (85% reduction) and after 96 h of incubation, the EE2 concentration decreased to 0.18 mg/L (91.8% reduction). Control without capsules did not change the EE2 concentration during the experimental interval. These results indicate that _R. zopfi_ SBP capsules can be used as an EE2 removal treatment in secondary effluents, since most of the EE2 biodegradation occurred during the first hours of incubation (64.24% reduction). Moreover, _R. zopfi_ is considerably more adapted to the secondary effluent medium, since it presents superior EE2 consumption during the first hour of incubation compared to MSM and MSM + 2%LB media (31.2% and 11.7% reduction, respectively). Thus, encapsulated _R. zopfi_ does not need an adaptation stage (lag phase) in a secondary effluent environment and its response to the presence of EE2 in the medium is almost instantaneous. This may be explained by the fact that the purchased _R. zopfi_ isolate used in this study originates in an aerated wastewater bioreactor [28].  
Table 4 summarizes the EE2 biodegradation efficiency in the various types of media examined in this study. It is important to note that other EE2 biodegradation studies used suspended cultures (Tables 2 and 3). Our results present one of the best EE2 biodegradation rates, thus suggesting that a confined environment, such as the SBP capsule, might provide superior environmental conditions for the _R. zopfi_ culture.