esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving Ad Hoc Networks--A Comparative Study
## 5 Sea Ad-Hoc Networks (SANET)  
(a) Terrestrial wireless communication. Wireless communication between ships and land or between ships is mainly wireless communication using frequencies in the very-high-frequency band, medium- and short-wave band, and short-wave band. GMDSS divides the sea area where vessels navigate into four sea areas, from the A1 sea area to the A4 sea area and requires the installation of the necessary radio equipment according to the sea area in which the vessel navigates (Figure 12). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Routing Protocols** & **Routing Category** & **Metrics** & **Load Balancing** & \
\begin{tabular}{c} **Communication** \\ **Overhead** \\ \end{tabular} &
\begin{tabular}{c} **Computational** \\ **Complexity** \\ \end{tabular} \\ \hline
[54] &
\begin{tabular}{c} Stochastic Packet \\ Algorithm (SPA) \\ \end{tabular} & Stochastic Forward & Link throughput, & \multirow{2}{*}{No} & \multirow{2}{*}{Low} & \multirow{2}{*}{Low} \\ \cline{1-1}
[55] & \begin{tabular}{c} Fountain-code based \\ Greedy Queue and \\ Position Assisted (FQPA) \\ \end{tabular} & \
\begin{tabular}{c} Store-carry- \\ forward (SCF) \\ \end{tabular} &
\begin{tabular}{c} Queue backlog, \\ Position \\ information \\ \end{tabular} & Yes & High & High \\ \hline
[56] & \begin{tabular}{c} Location-Aided Delay \\ Tolerant Routing (LADIR) \\ \end{tabular} & \
\begin{tabular}{c} Geographic, Store- \\ carry–forward (SCF) \\ \end{tabular} &
\begin{tabular}{c} Position \\ Information \\ \end{tabular} & No & Low & High \\ \hline
[57] & \
\begin{tabular}{c} Link-quality and \\ Traffic-load Aware \\ Optimized Link-State \\ Routing (ILTA-OISR) \\ \end{tabular} &
\begin{tabular}{c} Proactive \\ Traffic load \\ \end{tabular} & Yes & High & High \\ \hline
[58] & \begin{tabular}{c} Mobility and Load \\ Optimized Link-State \\ Routing (ML-OISR) \\ \end{tabular} & \
\begin{tabular}{c} Proactive \\ Route (ML-OISR) \\ \end{tabular} &
\begin{tabular}{c} Stability degree of \\ node, butter load \\ \end{tabular} & Yes & High & High \\ \hline
[59] & \begin{tabular}{c} Predictive OISR (P-OLSR) \\ \end{tabular} & \
\begin{tabular}{c} Proactive \\ \end{tabular} &
\begin{tabular}{c} Relative speed- \\ weighted ETX \\ \end{tabular} & No & High & High \\ \hline
[60] & \
\begin{tabular}{c} SDN-based UAV \\ communication for routing \\ and topology management \\ of flying and hoc \\ networks (STFANET) \\ \end{tabular} &
\begin{tabular}{c} Centralized SDN \\ \end{tabular} & Length of each link. & No & High & High \\ \hline
[61] & \
\begin{tabular}{c} Particle Swarm \\ Optimization–Greedy \\ and Limited Flooding \\ Routing (PSO-GLER) \\ \end{tabular} &
\begin{tabular}{c} Geographic, Reactive \\ the Deflection angle \\ \end{tabular} & No & Low & High \\ \hline
[62] & \
\begin{tabular}{c} Reliable and Predictive \\ Routing Protocol (RARP) \\ \end{tabular} & Reactive &
\begin{tabular}{c} Hop, Failure prob., \\ Connection time. \\ \end{tabular} & No & High & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compression routing protocols for FANET.
## 1 Introduction  
In recent decades, there has been a notable surge in free-space optics (PSO) driven by the escalating demand for real-time quality of service and the necessity to efficiently transfer large amounts of data both at terrestrial and satellite networks, with a pronounced focus on optical inter-satellite links (OILs) [1]. While radio-frequency (RF) technologies are still prominent in satellite applications, the emergence of OILs is quickly gaining leverage in commercial applications. OILs offer several key advantages, including a vast available spectrum, high data rates, narrow optical beams favoring security, small antenna sizes, and reduced power consumption [2, 3, 4, 5].  
Low Earth orbit (LEO) Walker-delta type constellations are widely being considered in commercial deployments, with Starlink Phase 1 V2 being a typical example [6, 7]. This constellation consists of a total of 1584 mini-satellites at an altitude of 550 km. In LEO applications, satellite sizes vary from large to pico satellites, the latter commonly being referred to as cubesats [8]. OILs are a favorable communication solution in all these scenarios, leading to laser terminals that are both lightweight and compact [9].  
Various OIL systems have been developed. One notable example is the Optel-\(\mu\) terminal, constructed by TNO and utilized for both downlink and uplink communications [10]. Another is the LCT terminal produced by TESAT, which is employed for LEO satellite networks [11]. NASA has also developed several systems for LEO-to-LEO and LEO-to-ground communication links [12]. Similarly, the German Aerospace Center has also demonstrated mini-satellite OIL transceivers [13]. In [14], a spherical system with multiple transceivers was developed, which offers omnidirectional coverage of nearly 360 degrees and a data rate of 1 Gbps over a distance of 200 km. Another approach, as described in [15], involves equipping each satellite side with a 100-degree field-of-view OIL, which also enables omnidirectional coverage.
### Use of C. sativa  
For more than 12,000 years, _Cannabis_ spp. is used as a source of textile fiber and food worldwide [23]. The earliest data for medical use of _Cannabis_ by the Assyrians goes back to 3000 B.C. Follow-up records date from around 2700 B.C. in China, where _C. sativa_ was used as a medicine for menstrual fatigue, rheumatism, malaria, constipation and other conditions. It was later used by other ancient civilizations-the Egyptians (ca. 1700 B.C.), the Indians (ca. 1600 B.C.), the Persians (ca. 750 B.C.), the Greeks and the Romans (ca. 450 B.C.). Historically, _Cannabis_ was also used for additional indications such as glaucoma, anal fissures, diarrhoea, as obstetric aid and as anxiety relief [23]. The plant was introduced to the modern western medicine in the early 19 th century, and was mainly indicated for treating pain, glaucoma, nausea, depression and neuralgia [24].  
Today, medical use of _C. sativa_ includes multiple indications supported by reliable clinical evidence. Such are the treatment of chronic pain, mutiple sclerosis, resistant epilepsy and chemotherapy-associated nausea and vomiting, appetite and weight loss associated with HIV/AIDS, Tourette syndrome, anxiety disorders, sleep disorders, post-traumatic stress disorder and schizophrenia [1; 25; 26]. Multiple additional health benefits of _C. sativa_ extracts are reported in in vitro and in vivo trials, such as lowering blood cholesterol, triglycerides and blood pressure, antioxidant and antimicrobial activity [27]. _C. sativa_ and its extracts are also used in the treatment of dermatitis and degenerative immunological diseases, both as supplements and as traditional medications [25]. Cannabis oils, oral solutions, oil-like concentrates and timcures used orally and sublingually, lotions, balms, creams, bath salts, salves, gels, patches and other topical products as well as rectal and vaginal products (suppositories, tablets) are the most common pharmaceutical dosage forms employed [26].  
Moreover, hemp is additionally used in food and beverage production, as hempseeds are shown to have great nutritional value: high content of protein, containingine essential amino acids, dietary fibers and an ideal ratio of \(\omega\)-\(\omega\)-\(3\) fatty acids (3:1). The same applies to hempseed flour and oils, which have high content of proteins, insoluble fibers and polyunsaturated fatty acids. The \"cannabis\" are the latest type of cannabis-based products that recently became popular. Chewing gums, lollipops, caramel hard candy, berry gummies, lozenges, candy bars, jam, tea, soda, coffee, water, honey, etc., containing \(\Delta^{9}\)-THC- and/or CBD-dominant extracts and concentrates are widely marketed [28].  
Figure 1: Mid\(-\)IR spectra from the main cannabinoids; THCA, THC, CBDA, CBD were adapted from [21; 22]; CBN, CBGA and CBG were adapted from [18].  
Today, cannabis is the most commonly used illicit drug worldwide, despite the strict international control for more than eight decades [9].
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## Appendix C  
The zenith receiver on CYGNSS works much the same way as the nadir science receiver. However, the zenith receiver does not have an onboard calibration system, and data are processed from the pre-flight characterizations of the electronics. The counts of the receiver are converted to watts via a quadratic regression. Because the satellite is subject to the same thermal dynamics as the nadir receiver, one can expect that the zenith power estimate contains errors due to thermally driven gain variations. We model the \(Ecorr_{P^{S}}\) similarly to the nadir receiver but with some important distinctions. Because the zenith receiver operates without an onboard blackbody to calibrate against, a number of simplifying assumptions are made. Errors are broken up into just two components: correlated \(Ecorr_{P^{S}_{1}}\) and uncorrelated \(Ecorr_{P^{S}_{2}}\). We further assume that correlated error due to the absence of an onboard blackbody are outside the timescale of interest. The correlated error term \(Ecorr_{P^{S}_{1}}\) is assumed to be totally correlated, provided that the matching conditions are met:  
\[Ecorr_{P^{S}_{1}}\big{(}x_{i},x_{j}\big{)}=\left\{\begin{array}{cc}1,&conditions\ met\\ 0,&else\end{array}\right. \tag{11}\]  
The conditions for \(Ecorr_{P^{S}_{1}}\) are that samples \(x_{i}\) and \(x_{j}\) are made with the same CYGNSS observatory and within 10 min of each other. In addition, an uncorrelated component is allowed:  
\[Ecorr_{P^{S}_{2}}\big{(}x_{i},x_{j}\big{)}=\left\{\begin{array}{cc}1,&x_{i}= x_{j}\\ 0,&x_{i}\
eq x_{j}\end{array}\right. \tag{12}\]  
The rolled-up correlated error model from the sources in \(P^{Z}\) between any arbitrary samples \(i\) and \(j\) can be expressed as follows:  
\[K_{P^{Z}}(i,j)=\left[\beta\cdot E\big{(}P^{Z}_{1}\big{)}^{2}\cdot Ecorr_{P^{S }_{1}}(i,j)\big{]}+\left[\alpha\cdot E\left(P^{Z}_{2}\right)^{2}\cdot Ecorr_{ P^{S}_{2}}(i,j)\right], \tag{13}\]  
with the same tuning parameters \(\alpha\) and \(\beta\), as in Appendix A. Note that we assume \(E\big{(}P^{Z}_{1}\big{)}=0.18\) dB, as suggested in [33]. \(E\big{(}P^{Z}_{2}\big{)}\) is estimated directly from a 24 h of CYGNSS data as approximately 1% of the magnitude of the signal \(P^{Z}\); therefore, this model assumes \(E\big{(}P^{Z}_{2}\big{)}=0.04\) dB.
# Conductive Ink Printed Fabric Antenna with Aperture Feeding Technique
## 5 Measurement and Result Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Aperture Feeding** & **Antenna Size \({}^{*}\)** & **Bandwidth** & **Fractional** & **Gain (dBi)** & **Rad. Eff. (\(\eta_{\sigma\theta}\)) \({}^{**}\) & **FBR.** \\  & **Technique** & **L \(\times\) W \(\times\) H (mm)** & **(GHz)** & **Bandwidth** & **Gain (dBi)** & **Rad. Eff. (\(\eta_{\sigma\theta}\)) \({}^{**}\) & **(dBi)** \\ \hline
[19] & Proximity feed with & \multirow{2}{*}{\(50\times 50\times 4.0\)} & \multirow{2}{*}{2.36–2.51} & \multirow{2}{*}{6.16\%} & \multirow{2}{*}{1.5} & \multirow{2}{*}{55\%} & \multirow{2}{*}{-} \\  & probe & & & & & & \\ \hline
[20] & Dielectric & \multirow{2}{*}{\(100\times 100\times 4.8\)} & \multirow{2}{*}{2.33–2.49} & \multirow{2}{*}{6.63\%} & \multirow{2}{*}{5.6} & \multirow{2}{*}{47\%} & \multirow{2}{*}{15} \\  & back-reflector & & & & & & \\ \hline
[21] & Dielectric & \multirow{2}{*}{\(40\times 60\times 1.03\)} & \multirow{2}{*}{2.39–2.49} & \multirow{2}{*}{4.1\%} & \multirow{2}{*}{-} & \multirow{2}{*}{58\%} & \multirow{2}{*}{-} \\  & back-reflector & & & & & & \\ \hline
[22] & PCB solar cell & \multirow{2}{*}{\(120\times 120\times 11.95\)} & \multirow{2}{*}{0.902–0.928} & \multirow{2}{*}{2.84\%} & \multirow{2}{*}{3.0} & \multirow{2}{*}{-} & \multirow{2}{*}{4.0} \\  & reflector & & & & & & \\ \hline
[24] & SMA side-feed with & \multirow{2}{*}{\(68\times 68\times 4.5\)} & \multirow{2}{*}{2.0–2.6} & \multirow{2}{*}{26.01\%} & \multirow{2}{*}{2.86} & \multirow{2}{*}{65\%} & \multirow{2}{*}{-} \\  & MMC back-reflector & & & & & & \\ \hline \multicolumn{2}{c}{Proposed designs} & \multirow{2}{*}{\(52\times 42\times 3.24\)} & \multirow{2}{*}{2.4–2.482} & \multirow{2}{*}{3.36} & \multirow{2}{*}{3.85} & \multirow{2}{*}{56\%} & \multirow{2}{*}{9.35} \\ \multicolumn{2}{c}{( coaxial cable with epoxy resin)} & \multirow{2}{*}{\(150\times 145\times 3.24\)} & \multirow{2}{*}{0.853–0.886} & \multirow{2}{*}{3.73} & \multirow{2}{*}{3.9} & \multirow{2}{*}{44.92\%} & \multirow{2}{*}{20.62} \\ \hline \hline \end{tabular}  
\({}^{*}\)L \(\times\) W \(\times\) H = length \(\times\) width \(\times\) height \(\mid\) ** Rad Eff. = radiation efficiency \(\mid\) fractional bandwidth is at \(-\)10 dB \(S11\).  
\end{table} 
Table 3: Comparison of fabric-based patch antennas with aperture/proximity feeding techniques.
## 3 Method
### Recruitment of Survey Participants and Data Analysis
The goodness of fit of the measurement model was evaluated using several indices, including the chi-square statistic (\(\chi^{2}\)/df) less than 3, the goodness-of-fit index (GFI), the normed fit index (NFI), the relative fit index (RFI), and the comparative fit index (CFI) all exceeding 0.8, as well as the root mean square error of approximation (RMSEA) less than 0.1 [59; 60]. Next, the means and standard deviations (SDs) for the attributes were calculated. To examine the relationships between attributes and ensure discriminant validity, a correlation matrix was employed. According to prior research, discriminant validity is considered acceptable when the square root of the AVE for each construct exceeds the correlation coefficients between constructs [58; 59; 60]. This study also employed [PERSON] Process Macro Model 7, which utilizes ordinary least squares regression for path analysis with 5000 bootstrap iterations. According to [PERSON] [61], the Process Macro Model is less affected by sample distortions, allowing for more robust estimates. Additionally, we applied the simple slope method to examine the moderating effects of nutrition disclosure. This method enables a graphical representation of the interaction effect by comparing the slopes at three levels of the moderating variable: low, medium, and high [60]. \begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Frequency** & **Percentage** \\ \hline Male & 126 & 41.3 \\ Female & 179 & 58.7 \\
20 s & 47 & 24.3 \\
30 s & 101 & 33.1 \\
40 s & 92 & 30.2 \\
50 s & 33 & 10.8 \\ Older than 60 & 5 & 1.6 \\ Monthly household income & & \\ Less than USD 2500 & 86 & 28.2 \\ USD 2500–USD 4999 & 111 & 36.4 \\ USD 5000–USD 7499 & 48 & 15.7 \\ USD 7500–USD 9999 & 13 & 4.3 \\ More than USD 10,000 & 47 & 15.4 \\ Weekly use frequency & & \\ Less than 1 time & 118 & 38.7 \\
1–2 times & 121 & 39.7 \\
3–6 times & 44 & 14.4 \\ More than 7 times & 22 & 7.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Profiles of survey participants (N = 305).
## 3 Network Structure
The altitude of each orbit is shown in Table 1. LEO and GEO are 700 to 1500 km and 35,790 km away from the Earth's surface, respectively, while MEO is between 2000 and 20,000 km away from the Earth's surface, generally about 10,000 km away. Satellites transmit signals through radio waves, and the propagation speed is approximately the speed of light. The delay of data transmission by MEO satellites to the Earth's surface is about 56 ms, and that by GEO satellites to the Earth's surface is about 120 ms. As the delay is too long to meet user needs, MEC is not considered to be deployed on MEO and GEO satellites. In view of the limited resources in SAGIN, an efficient dynamic resource management method is needed to deal with the unbalanced allocation of resources in converged networks to ensure stability of services. The dynamic resource management method under the SAGIN \begin{table}
\begin{tabular}{c c} \hline \hline
**Satellites** & **Orbit Height/km** \\ \hline GEO satellites & 35,790 \\ MEO satellites & 2000–20,000 (10,000) \\ LEO satellites & 700–1500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distance of satellite from Earth’s surface. Figure 2: SAGIN topology. structure based on the SDN and NFV is to combine VNFs into SFC orderly in the form of logical links so as to guide traffic through according to specific policies. As shown in Figure 3, there are two service requests, SFC-1 and SFC-2, where the traffic of SFC-1 passes through the VNFs carried by the non-ground node. Currently, the satellite nodes and the aircraft nodes have three functions. Firstly, the traffic can flow through the node without laying ground optical cables. In addition, it can balance the traffic load. For example, when some routes on the local network are congested, tasks are unloaded to the non-ground network. Furthermore, it shortens the delay by reducing the number of end-to-end hops. Relative to terrestrial edge cloud networks, SAGIN-MEC utilizes SDN\(/\)NFV to achieve unified resource scheduling across different heterogeneous networks, which can improve the request acceptance rate and reduce the end-to-end latency of SFCs, and the specific results are presented in detail in the experiments and analyses in Section 4.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 4 Microbial Remediation of Heavy Metal-Contaminated Soil
### Remediation Mechanisms
#### 4.1.2 Bieleaching  
Biomining is a general term that covers both bioleaching, which involves the mobilization of positive heavy metal ions from insoluble ones often by biological dissolution or complexation processes [41; 43], and bio-oxidation [44]. Microbial metabolism can produce secretions, such as low molecular weight organic acids, that can dissolve heavy metals and soil particles containing heavy metal minerals (Figure 2). [PERSON] et al. [45] showed that microbes can effectively use nutrients and energy to secrete organic acids and promote the leaching of Cd in nutritious conditions. For example, the leaching rate in the absence of nutrients was found to be 9%, and that with the addition of glucose and other nutrients was 36% [45]. There have also been studies that showed that some microbes, for example, _Citrobacter_, could generate free inorganic phosphate, leading to the formation of an insoluble metal phosphate coat that can entrap a large volume of toxic metals (Figure 2, [46]).  
Microorganisms, which are mostly prokaryotic, participate in redox reactions and change the valence of heavy metals (Figure 2), thereby changing their activity, which can affect their mobility or toxicity [47]. For example, Hg\({}^{2+}\), once reduced by aerobic bacteria to Hg\({}^{0}\), can evaporate, and tobacco smoke can reduce Hg\({}^{2+}\) to Hg\({}^{0}\)[21]. Toxic and water-soluble Cr\({}^{6+}\) can be reduced by _Coryne bacterium_ and other microbes into toxic and poorly water-soluble Cr\({}^{3+}\), and dead _Bacillus licheniformis_ R08 can reduce Pb\({}^{2+}\) to Pb\({}^{0}\)[48].
## 3 Problem Formulation, Assumptions, and Notations  
### Model of the Production Line  
We consider the classic continuous flow model of production lines [1; 44]. A line with \(K\) machines and \(K-1\) buffers is depicted in Figure 1. The following list presents the characteristics of the line:  
1. The material is modeled as a continuous flow. It flows through machine \(M_{1}\), buffer \(B_{1}\), machine \(M_{2}\), buffer \(B_{2}\), and so on until it arrives at machine \(M_{K}\), from which it flows out of the system.
2. The first machine \(M_{1}\) is never starved.  
The last machine \(M_{K}\) is never blocked.  
4. The machines have unique deterministic processing times. The processing rate (speed) of \(M_{i}\), \(i=1\ldots K\), is \(\mu_{i}\).  
5. The capacity of buffer \(B_{i}\) is a real number \(b_{i}\geq 0\), \(i=1\ldots K-1\).  
6. There is no delay in the transfer from the machines to the buffers or from the buffers to the machines.  
7. Machines are subject to failures. The time to failures and time to repairs are positive random variables with general distributions. The mean time to failure (MTTF) and mean time to repair (MTTR) of machine \(M_{i}\), \(i=1\ldots K\), are denoted as \(MTTF_{i}\) and \(MTTR_{i}\), respectively.  
8. When buffer \(B_{i}\), \(i=1,\ldots,K-1\), is empty, the actual processing rate of the downstream machine \(M_{i+1}\) is limited by that of the upstream machine \(M_{i}\).  
9. When buffer \(B_{i}\), \(i=1,\ldots,K-1\), is full, the actual processing rate of the upstream machine \(M_{i}\) is limited by that of the downstream machine \(M_{i+1}\).  
10. The failures are assumed to be operation-dependent, i.e., the machines can break down only when they are operating.
## 2 Materials and Methods
### Quantification of Total Phenolic Content, Total Carotenoid Content and Antioxidant Capacity
#### 2.3.3 Total Antioxidant Activity (TAA) Assessment  
According to [PERSON] et al. [7], three different assays were used to evaluate the antioxidant activity of the samples: the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical binding method, the cationic radical binding assay of 2,2\({}^{\prime}\)-azino-bis-(3-ethylbenzothiazol-6-sulfonic acid) (ABTS) and the trivalent iron-reduction antioxidant power (FRAP) method.  
In accordance with the DPPH assay, each sample received 0.2 mL of ethanol, 0.8 mL of Tris-HCl buffer (pH 7.4) and 1 mL of DPPH solution. The solutions were agitated with a vortex in between each reagent addition. Immediately after 30 min at room temperature, the solutions' absorbance was measured at 517 nm.  
The percentage of inhibition (%) was calculated from the following equation:  
\[\text{Suspension (\%)}=\text{(A1}-\text{A2)}\times 100/\text{A1},\]  
where  
A1: the absorbance of the control sample solution; and  
A2: the absorbance of the test sample solution.  
The IC50 (the concentration of each extract that has the ability to neutralize 50% of the DPPH radical), was then calculated. By using Trolox as a standard, the DPPH radical scavenging activity of the sample was expressed as a Trolox equivalent antioxidant capacity (TEAC), which was calculated as follows:  
TEAC = IC50 of Trolox (\(\upmu\)g/L)/IC50 of the sample (\(\upmu\)g/L).  
According to the ABTS assay, after the addition of 2 mL of ABTS solution to each sample and vortex agitation followed an incubation of the solutions in a dark place for 7 min and the immediate measurement of their absorbance at 734 nm.  
Trolox was used as a standard and the results were expressed as ABTS values, meaning as \(\upmu\)mol Trolox Equivalent (TE)/g dry weight (DW) of the extract, according to the following formula:  
\[\text{ABTS (\upmu\text{mol TE/g DW) value = c}\times\text{V}\times\text{t/m}},\]  
where  
c: the Trolox concentration (\(\upmu\)mol/mL) of the corresponding standard curve of the diluted sample;  
V: the volume of the sample (mL);  
t: the dilution factor; and  
m: the weight of the dry substance of the sample (g).  
With respect to the FRAP assay, 3 mL of FRAP solution was transferred to each sample, followed by vortex stirring. The solutions were incubated in a dark place at 37 \({}^{\circ}\)C for 15 min and immediately afterwards the absorbance at 593 nm was measured. Again, Trolox was used as a standard and the results were expressed as FRAP values, meaning as \(\upmu\)mol TE/g DW according to the following formula:  
\[\text{FRAP (\upmu\text{mol TE/g DW) value = c}\times\text{V}\times\text{t/m}},\]where  
c: the Trolox concentration (\(\upmu\)mol/mL) of the corresponding standard curve of the diluted sample;  
V: the volume of the sample (mL);  
t: the dilution factor; and  
m: the weight of the dry substance of the sample (g).