esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Level of Patient Satisfaction
#### 3.2.4 Additional Non-Paid Services
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Measures** & \begin{tabular}{l} **Not at All Satisfied** \\ **\(\pi\) (\%)** \\ \end{tabular} & \begin{tabular}{l} **Not Very Satisfied** \\ **\(\pi\) (\%)** \\ \end{tabular} & \begin{tabular}{l} **Failure Satisfied** \\ **\(\pi\) (\%)** \\ \end{tabular} & \begin{tabular}{l} **Satisfied** \\ **\(\pi\) (\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Very Satisfied** \\ **\(\pi\) (\%)** \\ \end{tabular} &
\begin{tabular}{l} **MS \(\pm\) SEM** \\ \end{tabular} \\ \hline Location of pharmacy is suitable for & 136 & 371 & 306 & 178 & 96 & 2.74 \({}^{*}\pm 0.03\) \\ you! & (12.5) & (34.1) & (28.1) & (16.3) & (8.9) & \\ \hline Are you satisfied about dispensing & 91 & 187 & 146 & 449 & 215 & \\ area in pharmacy shop? & (8.3) & (17.2) & (13.4) & (41.2) & (19.7) & 3.46 \(\pm\) 0.03 \\ \hline Do you feel the comfort and & 509 & 364 & 117 & 86 & 12 & \\ convenience at the waiting area of & (46.8) & (33.4) & (10.7) & (7.9) & (1.1) & \\ pharmacy? & & & & & & \\ \hline Are you satisfied about counseling & 509 & 348 & 129 & 91 & 11 & \\ area of pharmacy shop? (noise free/ & (46.7) & (31.9) & (11.8) & (8.3) & (1.0) & 1.84 \(\pm\) 0.03 \\ separate) & & & & & \\ \hline Are you satisfied with cleanliness & 106 & 258 & 182 & 418 & 124 & \\ and hygien condition of pharmacy & (9.7) & (23.7) & (16.7) & (38.4) & (11.4) & 3.18 \(\pm\) 0.03 \\ shop? & & & & & & \\ \hline Are you satisfied to parking facility & 701 & 201 & 87 & 62 & 37 & 1.65 \(\pm\) 0.03 \\ provided by pharmacy? & (64.4) & (18.4) & (7.9) & (5.7) & (3.4) & 1.65 \(\pm\) 0.03 \\ \hline Are you satisfied by operational & 12 & 51 & 36 & 634 & 355 & 4.16 \(\pm\) 0.02 \\ hours of pharmacy? & (1.1) & (4.68) & (3.3) & (58.3) & (32.6) & 4.16 \(\pm\) 0.02 \\ \hline Are you satisfied to pharmacy & 28 & 183 & 276 & 456 & 145 & \\ operational hours at weekends and & (2.5) & (16.8) & (25.3) & (41.9) & (13.3) & 3.46 \(\pm\) 0.03 \\ public holidays or festivals? & & & & & & \\ \hline Do you think staff is well educated? & 315 & 291 & 287 & 180 & 15 & \\ \hline Are you satisfied with the staff & 47 & 21 & 128 & 678 & 214 & \\ attitude? & (4.3) & (1.9) & (11.7) & (62.3) & (19.6) & 3.91 \(\pm\) 0.02 \\ \hline Are satisfied with age of pharmacy staff & 34 & 17 & 145 & 714 & 178 & 3.90 \(\pm\) 0.02 \\ \hline Are you satisfied that number of staff is adequate to pharmacy & (3.1) & (1.5) & (13.3) & (65.6) & (16.3) & 3.90 \(\pm\) 0.02 \\ \hline Are you satisfied that number of staff is adequate to pharmacy & 99 & 196 & 191 & 578 & 34 & \\ operational requirement? & (8.1) & (18.0) & (17.5) & (53.1) & (3.1) & 3.25 \(\pm\) 0.03 \\ \hline \hline \end{tabular} \({}^{*}\) I participant didn’t respond this question.  
\end{table} 
Table 2: Patients’ satisfaction about the shop, location and staff (\(\eta=1088\)).
## 2 Materials and Test Programs
### Test Programs  
#### 2.2.1 Sample Preparation  
The UCS tests and STS tests both used the cylinder samples prepared in laboratories, with a diameter of 50 mm and a height of 100 mm with optimum water content, according to the impact molding method of the Chinese specification of JTG 3430-2020, the reason for using optimum water content to mold the samples is that the BF did not absorb water [45]. The fiber length selected was 12 mm [37] and unreinforced soil was taken as a comparison. Each condition is molded with five different BF contents: 0.0%, 0.1%, 0.2%, 0.3%, and 0.4% [37], where the fiber content is measured by the percentage of fiber in the total mass of the soil and cement. First, the appropriate water was added to the silty sand until it reached the optimum water content by a mechanical mixer, then stored in an airtight container for 24 h to make the internal water distribute uniformly. Subsequently, incorporating cement and BF into the prepared soil and mixed for 10 min by a mechanical mixer. Thereafter, the blend was compacted at the lowest (minimum 96%) compaction standard of high-grade highway subgrade material. After being molded, each sample was tightly packaged with plastic wrap to prevent water dispersion and evaporation during curing and to block external water sources. Then, according to the Chinese specification of JTG E51-2009, Test Methods of Materials Stabilized with Inorganic Binders for Highway Engineering [46], packaged samples were placed into a curing room with a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and a relative humidity greater than 95% for 3 days, 7 days, 14 days, and 28 days. The preparation process is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Fiber Type** & **Diameter (\(\mu\)m)** & **Density (g/cm\({}^{3}\))** & **Tensile Strength (MPa)** & **Elastic Modulus (Gpa)** & **Melting Point** (\({}^{\circ}\)C)** \\ \hline Value & Monofilament & 7\(\sim\)15 & 2.63 & 3000–4800 & 91\(\sim\)110 & 1050 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance indicator of basalt fibers.  
Figure 2: Basalt fibers used in this study: (**a**) dispersed fiber, and (**b**) clustered fiber.  
The blend design for exploring the effects of BF on cemented silty sand is displayed in Table 3. Group 1 is BFUSM, which is a reference to make a comparison with BFCSM (Group 2). The UCS tests and STS tests need a minimum of six samples molded with the same properties are tested to obtain the typical of data (three for compression and three for splitting), then the mean value of the three data is defined as the value of UCS (\(q_{\text{u}}\)) and STS (\(q_{\text{t}}\)).
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Glycerol
#### 4.2.1 Biosynthesis of 3-HP via the Coenzyme A-Dependent Pathway  
Most studies on 3-HP production via the coenzyme A-dependent pathway were focused on _L. reuteri_, which can naturally metabolize glycerol to produce 3-HP. During the growth of _L. reuteri_ on glucose, 3-HPA, which is produced through glycerol dehydration reaction, tends to be reduced to 1,3-PDO rather than oxidized to 3-HP, and only under anaerobic conditions will 3-HPA be converted to equimolar amounts of 3-HP and 1,3-PDO [81]. It has been shown that the conversion of glycerol to 3-HPA catalyzed by glycerol dehydration was 10 times faster than the redox conversion of 3-HPA to 1,3-PDO and 3-HP in _L. reuteri_[15]. As a result, the 3-HPA could not be completely converted and accumulated in the fermentation broth. A co-culture system of wild-type _L. reuteri_ and recombinant _E. coli_ overexpressing aldehyde dehydrogenase (ALDH) was designed to address this problem. In this system, the 3-HPA produced by _L. reuteri_ was further converted to 3-HP by the engineered _E. coli_, and the titers of 3-HP reached 125.93 g/L in modified fed-batchferentation, respectively [39]. It was reported that recombinant _E. coli_ overexpressing PduP, PduL, and PduW from _L. reuteri_ produced 3-HP at a yield of 0.68 mol/mol of 3-HPA, without accumulating 1,3-PDO during the production process [82]. Recently, _L. reuteri_ was engineered into a whole-cell catalyst with a frozen cross-linked structure to produce 3-HPA and 3-HP at maximum specific reaction rates of 281.4 and 62.4 mg/gCDW/h, respectively [83]. The strategy of immobilized cells reduced production costs and improved production stability.
## 4 Lifting Wavelet Transform
)\) Update operator is specified in Equation (19). \[U(d[k])=\sum_{i}(u_{i}d[k+i]) \tag{19}\]  
Here, \(u_{i}\) are the updating coefficients. In the update process, the approximate value of the obtained signal by passing the signal through a low pass filter is obtained by updating the linear combination of the \(d[k]\) prediction difference as in Equation (20). These examples contain low frequency components that give approximate information (\(\text{s}[k]\)) about the signal. \[\text{s}[k]=X_{\text{even}}[k]+U(d[k]) \tag{20}\]  
Inverse transformation is obtained by applying the exact opposite operations used in the conversion to the obtained \(d[k]\) and \(s[k]\) signals. Inverse Lifting wavelet transformation is given in Figure 3 in I-LWT block in detail. \[X_{\text{odd}}[k]=d[k]+P(X_{\text{even}}[k]) \tag{21}\]  
\[X_{\text{even}}[k]=s[k]-U(d[k]) \tag{22}\]  
After the inverse transformation process, as given in Equation (23), the original signal is obtained by combining the odd index and even index samples with the \(MRG\) combination operator. \[X[k]=MRG\{X_{\text{odd}}[k],\;X_{\text{even}}[k]\} \tag{23}\]  
In addition, Prediction and Update operations are shown in Equation (24) with a \(2\times 2\) matrix by performing intermediate operations. Smooth and detail information is obtained from this matrix. \[\left[ \begin{array}{c}d[k]\\ s[k]\end{array} \right]=\left[ \begin{array}{cc}1&-P(.)\\ U(.)&1-P(.)U(.)\\ \end{array} \right]\left[ \begin{array}{c}X_{\text{odd}}[k]\\ X_{\text{even}}[k]\end{array} \right] \tag{24}\]\[A=\left[ \begin{array}{cc}1&-P(.)\\ U(.)&1-P(.)U(.)\end{array} \right] \tag{25}\]  
Since the determinant of the matrix shown by Equation (25) is equal to one, this matrix is an invertible matrix. Since it is the inverse of the matrix, a completely symmetric analysis can be made. The inverse-LWT shown in Figure 3 and indicated by Equations (21) and (22) is mathematically expressed by Equation (26) with a \(2\times 2\) matrix. The matrix in Equation (26) is the inverse of the matrix in Equation (24) (shown in Equation (27)). \[\left[ \begin{array}{c}X_{\text{odd}}[k]\\ X_{\text{even}}[k]\end{array} \right]=\left[ \begin{array}{cc}1-P(.)U(.)&P(.) \\ -U(.)&1\end{array} \right]\left[ \begin{array}{c}d[k]\\ s[k]\end{array} \right] \tag{26}\]  
\[AA=A^{-1}=\left[ \begin{array}{cc}1-P(.)U(.)&P(.)\\ -U(.)&1\end{array} \right] \tag{27}\]  
To recover the signal, it is sufficient to reverse the operations in Figure 3 in LWT block and change the signs as shown in Figure 3 in I-LWT block.
## 5 Performance Evaluation
Figure 8 is the false positive probability of the proposed structure using the input from Table 3. Figure 8 can be compared with the false positive of theoretical analysis. The theoretical analysis is referenced to in [57]. If \(n_{c}\) is the number of challenges in the authentication server, \(n_{crp}\) is the number of CRPs in the authentication server, \(k\) is the number of hash functions, and \(m\) is the number of cells, and \(p_{pd}\) is the probability that a particular cell is set at least once by \(n_{c}\) elements in the PDBF, \(p_{pd}\) is defined in Equation (10):  
\[p_{pd}=1-\left(1-\frac{1}{m}\right)^{kn_{c}} \tag{10}\]  
If \(p_{crp}\) represents the probability that a particular cell is set at least once by \(n_{crp}\) elements in CRPBF, \(p_{crp}\) is defined as in Equation (11):  
\[p_{crp}=1-\left(1-\frac{1}{m}\right)^{kn_{crp}} \tag{11}\]  
In querying, for the input \(y\) not included in the set \(S\), false positives occur if the PDBF produces false positives from input \(y\) and the CRPBF also produces false positives from input \(y\). Hence, the false positive probability \(P(F)\) is defined as follows:  
\[P(F)=p_{pd}^{\ k}\cdot p_{crp}^{\ k} \tag{12}\]  
Figure 8 shows that the data transmission error rate decreases for all data sets with increasing the sizing factor of the Bloom filter. Additionally, the simulation results for all data sets are close to the theoretical results. If a PUF is implemented without a Bloom filter, responses corresponding to all challenges by an attacker are transmitted, and CRP table information or the PUF model in the server may eventually be exposed. Figure 9 shows the delete performance of the PDBF of the proposed structure. To evaluate the deletion performance of the proposed structure, all CRPs from the authentication server are used once and then again used as inputs. The PDBF should produce negative results for reused CRPs. If the CRPBF produces a positive result, our proposed structure incurs a data transmission error. When the size factor of the Bloom filter is eight, most of the sets do not transmit the response to the server, confirming the successful removal of the reused CRP. The proposed structure applies small Bloom filters to the PUF. The Bloom filters identify an attacker challenge and remove its access to the PUF. Additionally, the Bloom filters verify the response generated by the PUF and transmit only the data that exists in the authentication server. The performance of the proposed structure is confirmed by simulation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & _Set 1_ & & _Set 2_ & & _Set 3_ & & & _Set 4_ & \\ \hline \(\alpha\) & 2 & 4 & 8 & 2 & 4 & 8 & 2 & 4 & 8 & 2 & 4 & 8 \\ total no. of PDBF queries & & 1024 & & & 2048 & & & 4096 & & & 8192 & \\ no. of PDBF false positives & 409 & 144 & 30 & 819 & 257 & 52 & 1514 & 597 & 90 & 3224 & 1236 & 190 \\ no. of CRPBF false positives & 153 & 32 & 1 & 324 & 47 & 0 & 580 & 81 & 1 & 1245 & 182 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bloom filter performance.
## 4 Case Study Analysis
### Simulation Setup
All mobile energy and water storage vehicles have a nominal speed of 60 km/h on clear roads; their actual speed is reduced according to the traffic-impedance factor \(\gamma_{m,n}\) (see Equation (18)), which captures the effects of road icing and congestion. Community freshwater demand is obtained by evenly distributing the annual total reported in [33] across 365 days, thereby assuming a uniform seasonal distribution. Outage penalty costs are 6000 CNY/MWh for electricity and 30 CNY/(t\(\cdot\)h) for water, consistent with the cost items in Table 2. Diesel generators are assumed to have sufficient fuel during the 48-h window. Each water loading/unloading operation is completed within one time step (1 h), whereas the charging duration depends on the vehicle's residual state of charge and the rated charging power \(T_{e}^{\rm power}=0.3\) MW; in both cases, queuing delays at stations or reservoirs are neglected. \begin{table}
\begin{tabular}{l l l l l} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(E_{e}^{\max}\) & 1 MWh & \(E_{w}^{\max}\) & 5 tons \\ \(SOC_{e}^{\max}\) & 1 & \(SOC_{w}^{\max}\) & 1 \\ \(SOC_{e}^{\min}\) & 0.3 & \(SOC_{w}^{\min}\) & 0 \\ \(C_{e}\) & 50 yuan/kwh & \(C_{w}\) & 30 yuan/ton \\ \(C_{e}^{\rm car}\) & 200 yuan & \(C_{w}^{\rm car}\) & 200 yuan \\ \(C_{e}^{\rm stat}\) & 1000 yuan & \(C_{w}^{\rm res}\) & 1000 yuan \\ \(\eta_{\rm cha}\) & 0.95 & \(\eta_{w}^{\rm fill}\) & 0.99 \\ \(\eta_{\rm dis}^{\rm obs}\) & 0.90 & \(\eta_{w}^{\rm dra}\) & 0.98 \\ \(T_{e}^{\rm power}\) & 0.3 MW & \(T_{w}^{\rm total}\) & 1 h \\ \hline \end{tabular}
\end{table} 
Table 1: Simulationparameters (indicative exchange rate: USD 1 \(\approx\) CNY 7.2). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Electric Load** & **Food Load** & **Freshwater** & **Mobile** & **Diesel** & **Total Cost** \\
**Loss** & **Loss** & **Load Loss** & **Storage Cost** & **Generation Cost** & **(yuan)** & **(yuan)** \\ \hline
**Case 1** & 141,300 & 25,980 & 12,000 & 1178 & 8000 & 220,458 \\
**Case 2** & 225,300 & 40,920 & 9000 & 1878 & 8000 & 321,098 \\ \hline \end{tabular}
\end{table} 
Table 2: Cost comparison of different cases.
# Temperature Impacts on Endurance and Read Disturbs in Charge-Trap 3D NAND Flash Memories
## 6 Conclusions  
In this work, to achieve deep insights into the temperature impacts on the reliability properties of the 3D NAND flash, the TLC (3 bits/cell) 3D CT NAND flash memory chip was tested from \(-\)30 to 70 \({}^{\circ}\)C using the FPGA-based raw NAND chip tester together with the temperature-controllable chamber. With comprehensive characterizations, firstly, it was  
Figure 6: Read disturb-related RBER changes were divided into (**a**) down-shift errors and (**d**) up-shift errors from \(-\)30 to 70 \({}^{\circ}\)C; (**b,c**) compares B-to-A errors and G-to-F down-shift errors, respectively, while (**e,f**) compares A-to-B errors and F-to-G up-shift errors, respectively, at \(-\)30, 25, and 70 \({}^{\circ}\)C.  
Figure 7: Measured fail bit count (FBC) of different program levels: error bits from (**a**) D-to-E; (**b**) E-to-F; (**c**) F-to-G.  
observed that program time had a clear dependence on both temperature and P/E cycles by which the TCPT model was proposed; secondly, it was found that RBER can be well suppressed at high temperatures and it degrades obviously at low temperature; then, by the designed cross-temperature measurements, it was found that thermal experience had negligible impacts on RBER degradation; finally, as for read disturbs, it was concluded that read disturbs cause more RBER degradations at cold temperatures while part of RBER can be recovered by read disturbs at high temperatures.  
The work presented here was completed in collaboration between all authors. Conceptualization, F.C. and B.C.; measurements and validation, F.C. and X.L.; simulations, F.C. and H.L.; formal analysis, F.C.; investigation, F.C., B.C., H.L. and Y.K.; writing--original draft preparation, F.C.; writing--review and editing, J.C.; supervision, J.C.; project administration, J.C.; funding acquisition, X.Z. and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62034006 and 91964105), the National Key Research and Development Program of China (No. 2016 YFA0201802), and the Natural Science Foundation of Shandong Province (No. ZR2020 JQ28 and ZR2019 LZH009).  
The authors declare that there is no conflict of interest.
## 4 Combining ground data and VIs to scale biophysical processes over large areas
### Examples of VIs and Flux Tower Data to Estimate ET
In the special case of phreatophytic vegetation rooted into deep soil water in arid zones, [PERSON] _et al._[81] found that a single mid-summer, high-resolution NDVI image calibrated against potential ET could predict actual annual ET determined by flux towers with acceptable accuracy across a wide range of vegetation types. VI methods for ET are valuable monitoring tools but they cannot be used as early indicators of plant stress, important in irrigation scheduling and other water management tasks. However, VIs can be combined with satellite-derived LSTs to provide estimates of ET, soil moisture conditions and land use changes, as in the \"triangle method\" where pixel values of NDVI and LST are plotted against each other to produce a response surface of vegetation density and surface temperature [75].
###### Abstract  
This paper investigates the power control and resource allocation problem in a simultaneously wireless information and power transfer (SWIPT)-based cognitive two-way relay network, in which two secondary users exchange information through a power splitting (PS) energy harvesting (EH) cognitive relay node underlay in a primary network. To enhance the secondary networks's transmission ability without detriment to the primary network, we formulate an optimization to maximize the minimum transmission rates of the cognitive users by jointly optimizing power allocation at the sources, the time allocation of transmission frames and power splitting at the relay, under the constraint that the transmission power of the cognitive network is set not to exceed the primary user interference threshold to ensure primary work performance. To efficiently solve this problem, a sub-optimal algorithm named the joint power control and resource allocation (JPCRA) scheme is proposed, in which we decouple the non-convex problem into convex problems and use alternative steps in the optimization algorithm to get final solutions. Numerical results reveal that the proposed scheme enhances transmission fairness and outperforms three traditional schemes.  
SWIPT; cognitive two-way relay; power splitting; joint resource allocation +
Footnote †: journal: 2023  
1
## 2 Theoretical Framework
### Battery Energy Storage System  
To stabilize the DC voltage at the output of the boost converter and to support energy management strategies such as power curtailment or peak shaving, a BESS is integrated into the proposed wind energy platform.  
The BESS acts as a dynamic load or source, depending on the power balance between the wind turbine and the consumption or grid requirements. During periods of excess wind power, the battery stores energy by absorbing the surplus; conversely, in low-wind or curtailed conditions, it can supply energy to maintain system stability.  
To evaluate the performance and aging of the battery system, two key metrics are monitored [25]:* State of Charge (SOC): indicates the instantaneous energy level relative to the battery's nominal capacity. It is calculated as: \[SOC(t)=\text{SOC}(0)+\frac{1}{C_{\text{nom}}}\int_{0}^{t}\Big{(}I_{\text{ charge}}(\tau)-I_{\text{discharge}}(\tau)\Big{)}\text{d}\tau\] (10) where \(C_{nom}\) is the nominal capacity of the battery, and \(I_{charge}\) and \(I_{discharge}\) are the respective charge and discharge currents over time \(\tau\);
* State of Health (SOH): reflects the battery's ability to retain capacity compared to its nominal value and is defined as: \[SOH=\frac{C_{\text{actual}}}{C_{\text{nom}}}\times 100\] (11) where \(C_{actual}\) is the currently available maximum capacity.  
The inclusion of the BESS in the system architecture provides flexibility for future studies on energy dispatch strategies, hybrid microgrid integration, and enhanced wind power quality control.
## 3 Research Methodology
### Circularity Index (CI)  
The circularity concept is often defined in R aspects, which include reduce, replace, reuse, repair, and recycle [63]. Many research studies have defined circularity of a material only in terms of the recycling aspect while neglecting other key focuses including reusability, repurpose, and repair [64]. However, the importance of each aspect within material circularity will depend on the project objectives, goals, and sustainability priorities. Especially in the case of novel products, achieving the short-term and long-term strength characteristics is critical towards the commercialization of the sustainable products [65; 66]. Therefore, the current study proposes the following circularity index to evaluate the circularity of the proposed mix designs.  
\[\text{CI}=[\frac{\delta_{r}^{\prime}}{\delta_{ref}^{\prime}}*p]_{recycle}* \alpha+[\frac{\partial_{a}}{\partial_{ref}}]_{repair}*\beta+[\frac{w_{r}* \delta_{r}}{w_{ref}*\delta_{ref}}]_{reuse}*\gamma \tag{5}\]  
The \"recycle\" component in the circularity index is defined in terms of percentage of virgin material replaced in the sustainable mix, without significantly compromising the characteristic strength. \(\frac{\delta_{r}^{\prime}}{\delta_{ref}^{\prime}}\) is the characteristic strength ratio of the sustainable mix and the control mix considered. \(p\) is the percentage of virgin materials replaced in the sustainable mix. The \"repair\" component is defined in terms of durability results, assuming that higher durability results can delay the repair works of concrete. \(\frac{\partial_{a}}{\partial_{ref}}\) ratio in the repair component is the ratio of durability characteristic strengths of the novel and control mix designs, respectively. The \"reuse\" component is defined in terms the extent to which the end-of-life concrete can be reused in the same product production without compromising the characteristic strength of the new product. In the current index definition, \(\frac{w_{r}}{w_{ref}}\) is the characteristic strength ratio of the mix designs with reused aggregates and the control mix designs. \(\frac{\delta_{r}}{\delta_{ref}}\) is the material reuse ratio in the mix design with reused aggregates.  
\(\alpha\), \(\beta\), and \(\gamma\) are the prioritisation factors that are obtained from experts involved in a specific project. These factors are obtained from pairwise comparison of experts who can evaluate the circular economy performance of construction materials. The current study used an equal importance factor for all the three stages to maintain uniform comparison. A detailed sensitivity analysis is then conducted using Monte Carlo simulation to investigate the influence of the prioritisation factors on the total circularity performance.