esa-sceva/satcom-chunk-collection huggingface dataset:
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 3 Results and Discussion
### Adsorption Study
#### 3.3.2 Isotherms
That is, RCS has a very high As(V) removal performance compared to existing adsorbents made from wastewater sludge. However, since RCS is structurally unstable below pH 5 (Figure S1), it has the weakness that it must be used at pH 5.5 or higher. Since As(V) exists as an anion, the adsorption efficiency decreases when the pH is high. Generally, the standard for wastewater discharge is pH 5.8 to 8.6. Therefore, the optimal operating condition for the As(V) removal process using RCS will be pH 5.5. The other metals, Cd(II) and Mn(VII), were well described by the Freundlich equation based on \(R^{2}\). This difference can be explained by the existence of operating mechanisms other than basic ion exchange, such as specific adsorption-complexation reactions that occur during the adsorption process [34]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metals** & **Sorbent Type** & **Uptake (mg/g)** & **Experimental Condition** & **Reference** \\ \hline \hline \multirow{8}{*}{As(V)} & Pyroized sludge (Industry) & 0.07 & pH 3.0–3.5, 48 h & [30] \\  & Biochar sewage sludge (Domestic) & 13.42 & pH 6.7–7, 24 h & [31] \\  & Acid mine drainage sludge (Industry) & 21.50 & pH 7, 24 h & [32] \\  & Acid mine drainage sludge alginate bead (Industry) & 21.79 & pH 5, 96 h & [33] \\  & Magnetic sludge composite (Domestic) & 21.3 & pH 2.6, 5 h & [34] \\  & Sludge–chitosan (Domestic) & 42.25 & pH 5.5, 6 h & This study \\ \hline \multirow{3}{*}{Cd(II)} & Activated sludge (Domestic) & 28.10 & pH 5, 2 h & [35] \\  & Sewage sludge (Domestic) & 28.41 & pH 5, 24 h & [36] \\ \cline{1-1}  & Sludge–chitosan (Domestic) & 28.69 & pH 5.5, 6 h & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum uptakes of metals (As(V) and Cd(II)) by various biosorbents manufactured from sludge.
## 3 Results and Analysis  
### Water-Reducing Additives  
In this research paper, three types of additives (SP62, PC180, and RC897) were used to enhance the performance of the concrete. The additives content ranged from 0 to 1.5%. Adding the additives reduced the water in the mixture, and the w/c ratio gradually decreased, thus keeping the slump value in the range of 200-220 mm. Regarding the concrete mixture, which contains 300 kg of cement, an addition of 0.5% of SP62 reduced the mixture's water content by 12.6%, while it was reduced by 17.6% and 9.1% when modified with 0.5% of PC180 and RC 897, respectively. Compared with 300 kg and 400 kg cement content in the mixture, the percentage of water-content reduction was higher for the mixture containing 350 kg of cement for the three types of additives, as shown in Figure 2. By increasing the content of the additive, the water-content reduction gradually increased (Figure 2). Modified the concrete with the SP62, PC180, and RC897 decreased the water content required to achieve the desired workability by 9.1% to 46.7%, based on the types and content of additives and based on the cement content, as shown in Figure 2.  
Figure 2: Percentage of water reduction caused by the addition of 3 types of additives in concrete mixes with (**a**) 300 kg, (**b**) 350 kg, and (**c**) 400 kg of cement.
## 3 Results and Discussion
### Cost Analysis  
Our cost analysis revealed that while the plastic injection method using ABS granules offers a lower per-unit cost, it requires substantial initial investment in machinery and holds. In contrast, 3D printing provides a significant cost advantage due to the lower machine cost and the absence of mold expenses. However, the plastic injection method boasts a substantially higher production capacity and faster rate.  
The break-even points in this study, which represent the production quantity where total cumulative manufacturing cost equals initial investment (including equipment and  
\begin{table}
\begin{tabular}{c c c c} \hline  & **ABS INJ.** & **ABS 3D** & **PLA 3D** \\  & **(\(\mu\)m)** & **(\(\mu\)m)** & **(\(\mu\)m)** \\ \hline
1. Point & 0.13 & 0.73 & 2.31 \\ \hline
2. Point & 0.18 & 1.25 & 3.55 \\ \hline
3. Point & 0.15 & 0.96 & 3.34 \\ \hline
4. Point & 0.12 & 1.94 & 2.57 \\ \hline
5. Point & 0.24 & 1.64 & 3.56 \\ \hline
6. Point & 0.19 & 2.08 & 2.22 \\ \hline
7. Point & 0.14 & 1.94 & 2.67 \\ \hline
8. Point & 0.14 & 1.29 & 3.32 \\ \hline
9. Point & 0.13 & 1.05 & 3.91 \\ \hline Avg & 0.16 & 1.43 & 3.05 \\ \hline \end{tabular}
\end{table}
Table 4: Surface roughness results of samples.  
tooling), differed significantly. For injection molding, this point is reached at approximately 17,680 units, whereas for 3D printing, it occurs at around 5820 units. These figures indicate the minimum quantity required for the initial capital expenditure to be amortized by the per-part production cost, without reflecting revenue or profit margins.  
A recent study indicated that producing at least 735 parts via the injection method results in a comparable production cost when compared with 3D printing. This suggests that once this specific number of toy figures is produced, the mold cost is recovered, leading to profit in subsequent productions. In summary, for a 4 g piece of this size, when demand is less than 735 units and production time is not a critical factor, 3D printing emerges as the more cost-effective option.
# Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges
## 3 Parameters That Affect Thermal Conductivity
### 3.2. Suspension
#### 3.2.2 Concentration  
The increase of the particle concentration enhances the thermal conductivity due to the greater interfacial area between the base fluid and the NPs and increasing agglomeration [41, 42]. [PERSON] et al. [43] and [PERSON] et al. [44] reported an increase in thermal conductivity by increasing the nanoparticle concentration. [PERSON] et al. [45] also observed a linear relation between concentration and thermal conductivity using water-based copper NFs. [PERSON] et al. [46], using NPs of Al\({}_{2}\)O\({}_{3}\) and CuO, measured experimentally the thermal conductivity of the NFs at low volume fraction range (lower than 0.05%) and found that the thermal conductivity ratios increase almost linearly with volume fraction, but with different rates of increase for each group of NPs tested. However, some reports noted a reduction of the thermal conductivity for high concentration values. The stability of the NF is likely to play a major role in the determination of the ideal concentration [5, 26].
## 4 Results and Discussion
### Simulation of Control System
#### 4.1.3 Verification of Simulation  
The simulation results of the control performance of the actuator was verified through laboratory experiment using the optimal PID coefficients selected as a result of the simulation above. It was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Actuator**} & \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Max. Overshoot (\%)**} & \multicolumn{1}{c}{**Settling Time (s)**} & \multicolumn{1}{c}{**Steady-State Error (\%)**} \\ \hline \multirow{3}{*}{Shuttle shifting} & K\({}_{\rm P}\)=6.30, K\({}_{\rm I}\)=4.80, K\({}_{\rm D}\)=0.155 & 8.50 & 1.18 & 0.00 \\ \cline{2-6}  & K\({}_{\rm P}\)=6.50, K\({}_{\rm I}\)=5.70, K\({}_{\rm D}\)=0.240 & 3.00 & 0.44 & 0.00 \\ \cline{2-6}  & K\({}_{\rm P}\)=6.70, K\({}_{\rm I}\)=6.05, K\({}_{\rm D}\)=0.330 & 1.50 & 3.74 & 4.50 \\ \hline \multirow{3}{*}{Clutch} & K\({}_{\rm P}\)=1.25, K\({}_{\rm I}\)=2.85, K\({}_{\rm D}\)=0.065 & 0.00 & 2.50 & 0.00 \\ \cline{2-6}  & K\({}_{\rm P}\)=1.45, K\({}_{\rm I}\)=3.45, K\({}_{\rm D}\)=0.190 & 0.00 & 1.06 & 0.00 \\ \cline{1-1} \cline{2-6}  & K\({}_{\rm P}\)=1.65, K\({}_{\rm I}\)=4.15, K\({}_{\rm D}\)=0.325 & 0.00 & 1.68 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Response performance of shuttle shifting actuator and clutch actuator according to PID coefficients.  
Figure 10: Comparison result of position control of (**a**) shuttle shifting actuator and (**b**) clutch actuator according to PID coefficients.  
performed for shuttle shifting actuator and clutch actuator, respectively, and the results are shown in Figure 11. In both actuator cases, the response performances in the simulation and the experiment were similar. Table 3 shows the results of the comparison of response performances by simulation and experimental results of the shuttle shifting actuator and the clutch actuator. The response performance showed less than 5% difference between simulation and experiment under all conditions. Thus, it was confirmed that the developed simulation program reflected the characteristics of the actuator well, such that it can control the shuttle shifting actuator and clutch actuator.
## 4 Conclusions  
The performance and exhaust emission characteristics of Moringa oleifera biodiesel blends were analyzed in this study. The results of the experimental investigation show that the MOB10 blend is the best blend ratio based on the following criteria:  
* At optimum speed, the BTE for MOB10 and MOB20 was 2.54% higher and 3.45% lower, respectively, than that of pure diesel.
* MOB10 and MOB20 blends had a higher average BSFC than diesel by 7.03% and 12.75%, respectively, due to the higher density and lower calorific values of biodiesel blends.
* MOB10 produced slightly lower BP when compared to diesel, by 0.26 kW. The MOB20 blend was the worst performer, producing less usable power than diesel by 0.36 kW.
* The average HC emission for MOB10 and MOB20 were lower than diesel, with a difference of 8 ppm.
* The average NOx emission for blended fuels was significantly higher than the neat diesel, and the MOB20 blend produces more NOx emissions due to increased oxygen content in fuel blends.
* MOB10 produced lower smoke opacity than those of neat diesel and MOB20 due to good combustion.
* Therefore, MOB10 is suitable to use in conventional compression-ignition diesel engines.  
Future Recommendation: The NOx emissions slightly increased in the combustion of biodiesel blends compared to conventional diesel. The researchers could pursue this work using different fuel additives such as nanoparticles or alcohols to reduce NOx emissions.  
Conceptualization, M.A.M. and M.E.M.S.; methodology, M.A.M., T.A. and M.E.M.S.; software, M.A.M., W.A., H.M.K., L.R. and M.E.M.S.; validation, A.H., M.S.G. and T.M.Y.K.; formal analysis, investigation, resources, data curation, writing--original draft preparation, M.A.M., L.R., A.A., M.S.G., U.I., T.M.I. and M.E.M.S.; review, editing and support, A.E., K.S., M.A.A.B. and M.F.; supervision, M.A.M.; project administration, M.S.G. and M.R.S.; funding acquisition, T.M.Y.K., A.E. and M.R.S. All authors have read and agreed to the published version of the manuscript.  
The Deanship of Scientific Research at King Khalid University, Grant Number R.G.P. 2/147/42. Taif University Researchers Supporting Project number (TURSP-2020/40), Taif University, Taif, Saudi Arabia.  
Not Applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Grant Number R.G.P. 2/147/42. This work was supported by Taif University Researchers Supporting Project number (TURSP-2020/40), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.
# Biomass Refined: 99% of Organic Carbon in Soils
## 3 Results and Discussion
### Total Living Biomass
#### 3.7.1 Forest Biome as Part of Total Vegetation Biomass and SOC  
Forests occupy about a third of total land area but supposedly support 80% of above-ground biomass and 40% of soil organic carbon bio-/necromass [141]. Tropical forests constitute 12.2% of the Earth's land area, have high NPP, and their soils, long thought to be SOM-deficient, are now known with high SOC content of 692 Gt C to 3 m depth [142]. Doubled for terrain, this tropical soil component alone is about 1284 Gt SOC! Earlier estimates of live forest biomass, both above- and below-ground, are thus shown to be quite inadequate compared current evaluation refinements.  
Relevant to earlier log/driftwood questions, ref. [126] estimated carbon stocks in the world's forests as 861 Gt C, with 383 Gt C (44%) in soil (to 1 m depth), and 363 Gt C (42%) in live biomass (above- and below-ground, although their root components may be underestimated). Their remaining \"_litter_\" categories added up to \(\sim\)116 Gt C for forests. The soil component may be doubled for depth, then all values doubled again for terrain to give a total forest inventory of \(\sim\)2250 Gt C. These authors provided estimates of regional production in various components, albeit many forests now being clear-felled.  
Figures 6 and 7 in ref. [141] also show carbon allocation in boreal forests in USA compared to global differences in tropical, temperate, and boreal forests where below-ground allocations differ markedly. The importance of often neglected below-ground allocation of NPP is shown by [143] summarizing that, on average, soil respiration is three times the above-ground litterfall-C, which further implies total below-ground carbon allocation (to roots, exudates, etc.) is roughly twice annual above-ground litterfall-C (often erroneously equated to global NPP). Recently, ref. [10] found up to 50% of NPP allocated to soil fungi (discussed later under f-SOC).  
Indeed, ref. [143] said allocation of carbon to below-ground plant structures often equals or exceeds above-ground litterfall and above-ground respiration in forest ecosystems, making soil the single most important recipient of gross primary productivity. Despite its importance, total forest carbon allocation remains poorly understood due to difficulties of quantifying root and mycorrhizal processes by any single method. These authors describe soil as the largest biochemically active carbon flux in forest ecosystems, thus surely the largest carbon exchange system in the whole Biosphere. A large component of this massive soil carbon exchange is found in the \"Fungisphere\".
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
### Novelty of the Proposed Smart Deployable Scissor Lift Brace System  
A literature review demonstrated that there has been no attempt to integrate EEW and mechatronic systems to tackle the long-standing problem of the soft-story mechanism. The majority of retrofitting solutions involve the permanent addition of braces that obstruct the original architectural design of the open space at ground level. This article describes the conceptual framework behind and the experimental assessment of a smart deployable scissor lift brace system to mitigate earthquake loading in soft-story buildings. The scissor lift brace system is activated and deployed when an EEW signal is received. Compared to the traditional retrofitting methods, the proposed system has the following advantages.  
1. It is designed to remain undeployed during regular use, preserving the open space design of the first floor.
2. It instantly deploys upon receiving the earthquake signal from the EEW system, thereby enhancing the building's structural integrity without obstructing its intended use.
3. It can be retracted automatically after each seismic event, making the system reusable and ensuring minimum impact on the building's functionality post-earthquake.  
This article presents first the conceptual design, followed by a proof-of-concept experimental investigation. Discussion on the experimental results, requirements for practical implementation, and limitations of the proposed system are also presented.
# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
## 7 Conclusions  
In conclusion, the proposed method has produced the best bone age prediction with the lowest \(MAE\) and \(MSE\) of 8.200 and 121.902, respectively. Therefore, the prediction error of the bone age is well below the 12 months threshold, where prediction tolerance under a one-year gap is still considered acceptable. The algorithm utilizes a novel image registration technique using separable convolution to automatically segment the hand and locate the keypoints for image rotation correction. Moreover, the regressor network, which is used to predict the bone age has utilized three-layer residual separable convolution units to produce a deep network, but maintain an acceptable model size, which is around 20,000,000 parameters. The network has also been trained using variable learning rates where its value is linearly decreasing with respect to the training epoch. However, the proposed method does not consider gender role in making the prediction, where this factor will surely affect the prediction accuracy. Another limitation that has not been considered in this work is the relative size of the captured X-ray image, where some samples are small in size when compared to the others. For future work, the proposed network can be further improved by adding a wider set of convolution filters instead of just a single network flow in the main branch. Atrous convolution can also be considered to capture larger feature maps while using a small convolution kernel size.  
Conceptualization, M.A.Z., S.R.A. and N.H.Z.; methodology, M.A.Z., S.R.A. and N.H.Z; software, M.A.Z., S.R.A. and N.H.Z; validation, M.A.Z., S.R.A. and N.H.Z; formal analysis, M.A.Z., S.R.A. and N.H.Z; investigation, X.X.; resources, M.A.Z., S.R.A. and N.H.Z; data curation, M.A.Z., S.R.A. and N.H.Z; writing--original draft preparation, M.A.Z., S.R.A. and N.H.Z; writing--review and editing, M.A.Z., S.R.A. and N.H.Z; visualization, M.A.Z., S.R.A. and N.H.Z; supervision, M.A.Z., S.R.A. and N.H.Z; project administration, M.A.Z., S.R.A. and N.H.Z; funding acquisition, M.A.Z., S.R.A. and N.H.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Universiti Kebangsaan Malaysia through Research University Grant Scheme under Grant GUP-2019-008, and in part by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme under Grant FRGS/1/2019/ICT02/UKM/02/1.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
**Ethical Statement:** All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of National Institutes of Health, USA (U24 CA180927).
## 3 Principle of the AE Source Location Technique
### P-wave Velocity Measurement for a Metal Specim  
For P-wave velocity measurement, the PLB test was conducted three times above AE sensor No. S3 along the line of No. S3 and No. S7, as shown in Figure 2. The arrival time was also obtained from the above-mentioned AR-AIC method. Then, the velocity of the metal specimen could be computed by Equation (4),  
\[v=\frac{z_{3}-z_{7}}{t_{7}-t_{3}} \tag{4}\]  
where, \(z_{3}\) and \(z_{7}\) are the \(z\) coordinates of AE sensors No. S3 and No. S7, \(t_{3}\) and \(t_{7}\) are the detected arrival times at AE sensors No. S3 and No. S7, and \(v\) is the P-wave velocity of sound in metal specimen.  
Finally, the average velocity of sound in the metal specimen was computed to be 5882 m/s, which is consistent with that the velocity of sound in steel metal, which is around 5556 m/s [16].
## 3 Proposed Method
### Smoothing  
The RUL prediction by the regression model usually has local fluctuations, but the actual bearing RUL has a linear relationship with the running time. In order to reduce the measurement noise and other uncertainties to obtain more accurate results, the KF algorithm [27] is adopted. The KF algorithm consists of two stages: prediction and measurement update. The equations for the prediction stage are  
\[\begin{split} x_{t|t-1}=x_{t-1|t-1}\\ P_{t|t-1}=P_{t-1|t-1}+Q\\ K_{t}=P_{t|t-1}\Big{(}P_{t|t-1}+R_{t}\Big{)}^{-1}\\ x_{t|t}=x_{t|t-1}+K_{t}\Big{(}\hat{y}_{t}-x_{t|t-1}\Big{)}\end{split} \tag{7}\]  
where \(x_{t|t-1}\) is the RUL prediction using the previous RUL, \(x_{t-1|t-1}\) is the best RUL prediction at time \(t-1\), \(P_{t|t-1}\) is the variance of \(x_{t|t-1}\), \(Q\) is the variance of system process, \(K_{t}\) is Kalman gain, \(R_{t}\) is the variance of measurement noise. \(\hat{y}_{t}\) represents the bearing RUL at time \(t\) obtained by the P-ResNet model. \(x_{t|t}\) is the optimized predicted value obtained by KF at time \(t\). The variance update process is  
\[P_{t|t}=(1-K_{t})P_{t|t-1} \tag{8}\]  
where \(P_{t|t}\) is the variance of \(x_{t|t}\). The initial value \(x_{0}\) is set to be 1, \(Q\) is set to be 1 \(\times\) 10\({}^{-4}\), and \(R_{0}\) is set to be 4 \(\times\) 10\({}^{-3}\).
## 3 Performance Predictions of Cement-Eggshell Powder Binary System
### Calibration of Hydration Parameters  
According to the content mentioned in the previous Section 2, the parameters B, C, kr, and \({\rm De_{0}}\) of the hydration model can be calibrated through the experimental results of hydration degree. Hydration heat is one of the indicators of hydration degree.  
The relationship between hydration heat and hydration degree is shown in Equation (3) as follows:  
\[\alpha={\rm Q(t)}/{\rm Q_{max}} \tag{3}\]  
where \({\rm Q(t)}\) is the result of the cumulative hydration heat measured experimentally, and \({\rm Q_{max}}\) is the hydration heat released when the 1 g cement is fully hydrated. According to the mineral composition of cement, the maximum hydration heat of 1 g cement is calculated to be \({\rm Q_{max}}=484\) J/g cement [8].  
The calibration of hydration model parameters is divided into two steps. First, based on the cumulative hydration heat measurement results of cement paste without eggshell powder, the numerical regression method is used to calibrate the parameters B, C, kr, and \({\rm De_{0}}\) of the cement hydration model. Secondly, keeping the parameters B, C, kr, and \({\rm De_{0}}\) unchanged, the nucleation effect parameter nu of eggshell powder was calibrated according to the measurement results of the cumulative hydration heat of 15% eggshell powder. The parameters of these hydration models are presented in Table 1.  
The calculation results of the early hydration heat are shown in Figure 1. Figure 1a shows that in the early stage of the hydration reaction, the cumulative hydration heat increases rapidly. This is because in the early stage of the hydration reaction, the rapid reactions of C\({}_{3}\)A and C\({}_{3}\)S releases a large amount of hydration heat. After about 50 h, the growth rate of hydration heat slows down. This is mainly because diffusion becomes the controlling process of the hydration reaction, which reduces the rate of heat release. From Figure 1a to Figure 1b,c, the cumulative hydration heat of cement based on unit mass increases slightly. This is mainly because the physical effect of eggshell powder accelerates the hydration of cement and releases more heat.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**B (cm/h)** & **C (cm/h)** & **kr (cm/h)** & **De\({}_{0}\) (cm\({}^{2}\)/h)** & **Nu** \\ \hline \(7.04\times 10^{-9}\) & 0.0023 & \(3.24\times 10^{-5}\) & \(8.97\times 10^{-11}\) & 8 \\ \hline \end{tabular}
\end{table}
Table 1: The values of input parameters of hybrid hydration model.  
Figure 1: Analysis results of hydration heat. (**a**) Analysis results of hydration heat of no eggshell powder specimen. (**b**) Analysis results of hydration heat of 7.5% eggshell powder specimen. (**c**) Analysis results of hydration heat of 15% eggshell powder specimen.