esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Connection-Aware Scheduling Heuristics
### ReleSEAS  
The main feature of E-SEAS [6] is that it is easy to implement in real-life environments because it requires easy-to-obtain information, such as battery level, nodes' computing capability measured in FLOPs (float point operations per second), and remaining jobs as nJobs to build a rank of devices joined to the cluster. Then, the E-SEAS ranking formula is defined as follows:  
\[E-SEAS=\frac{flops\cdot SOC}{nJobs} \tag{2}\]  
A scheduler applying E-SEAS criteria assigns jobs using this ranking. Every incoming job triggers a recalculation of the ranking, which uses fresh information about jobs' and nodes' statuses, such as job queue size and battery level at each node. Job counters and node status records are maintained by a centralized component called the proxy, from where the scheduler operates. This component keeps track of all information necessary to recalculate the ranking. In this paper, we propose incorporating a new score (reliability score) into the E-SEAS ranking formula to boost the ranking of devices connected to the network for extended periods. This modification derives into what we call ReReSEAS:  
\[ReleSEAS=\frac{flops\cdot SOC\cdot Reliability}{nflobs} \tag{3}\]  
where \(flops\) is the device capabilities (processor speed) measured in float point operations per second, \(SOC\) is the last report of the state of charge (battery level), and the reliability is calculated using Equation (1).
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 2 Materials and Methods
### Experimental Set-Up
#### 2.2.1 Part I  
The fish oil was mixed with 2.5% (_w/w_) _Phaeodactylum_ biomass and stirred for 5 min (room temperature, 300 rpm, IKA RO10 stirrer) to obtain the 'Fish oil + _Phaeodactylum_ biomass' system. The concentration was chosen in line with former research adding microalgal biomass to bulk oil [19; 20; 21; 22; 23; 24]. To obtain a correct impression of the influence of _Phaeodactylum_ biomass addition on the oxidative stability, also a 'Fish oil' (control) system was considered, obtained by stirring pure fish oil for 5 min without _Phaeodactylum_ biomass  
Figure 1: Experimental set-up.  
addition. The systems were characterized in terms of n-3 LC-PUFA content and carotenoid profile according to the analyses described below. Analyses of the 'Fish oil + _Phaodactylum_ biomass' system were performed on the oil phase obtained after sedimentation of the solid parts by a centrifugation step of 5 min at 1000 rpm (SL 16 Centrifuge, Thermo Scientific). To determine the oxidative stability a storage experiment of 3 weeks was set up. Both systems were transferred to amber screw-capped 20 mL vials in quantities of 1 g/vial, and while doing so, the systems were stirred with a magnetic stirrer to ensure a homogenous distribution. Vials were incubated (Memmert IN160 incubator, MLS, Menen, Belgium) at 37 \({}^{\circ}\)C (a temperature that accelerates the oxidation without changing its mechanisms [32]) and transferred to \(-\)80 \({}^{\circ}\)C until further analysis after seven different time points (day 0, 1, 3, 5, 8, 14, 21). For each time point, primary (peroxide value (PV)) and secondary (volatile compounds) oxidation products were determined in duplicate according to the methods described below, starting from independent vials. For the PV analysis of the 'Fish oil + _Phaodactylum_ biomass' system the oil phase (separated by centrifugation) was used again. A storage experiment analyzing the evolution of both primary and secondary oxidation products throughout storage was opted for to obtain a realistic indication of the oxidative stability and therefore the underlying antioxidant potential, rather than using quick antioxidant assays that only give a method-dependent indication [33].
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 4 Experimental Results
### Comparison with the State of the Art
#### 4.4.1 Quantitative Comparison  
To determine the accuracy of the objects that the proposed model segmented, the proposed model and four others were tested on five highly used datasets. To show the results, we plotted precision, recall, and F-measure curves on each dataset (see Figure 7). Additionally, results of the proposed model and the four others in terms of mean absolute error (MAE) and weighted F-measure (WF) are presented (see Table 2).  
Figure 7: Cont.  
Figure 7 shows the PR and F-measure curves of the proposed model with four other state-of-the-art models, which show that the proposed model outperformed all other methods. The other quantitative comparison in terms of weighted F-measure and mean absolute error in Table 2 showed that the proposed model was first among all other models, and the F-measure was improved by 10%, 8%, 7%, 6.1%, and 7.9% for the MSRA-B, DUTS, DUT-OMRON, PASCAL-S, and HKU-IS datasets, respectively.
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
## 4 Results and Discussion
### Study C: MQL vs. Conventional coolant
#### 4.3.2 Tool Wear  
All tools used in this study had similar methods of tool wear, namely large flank wear and a presence of crater wear. Measurements of the final flank wear indicate that MQL generates larger flank wear in each instance of the cutting tests in comparison to the flood coolant, with Test 7 having the largest increase in flank wear of 14.0%, as shown in Table 8. This increased tool wear is similar to the MQL effects on the machining of Inconel 718, where tool life is decreased using MQL compared to the flood coolant [48]. The larger tool wear may be attributed to the oil deposition location during cutting. The MQL deposition nozzle is directed towards the rake face of the tool to promote chip sliding; however, it does not provide any lubrication to the flank face.  
Figure 5: Average surface roughness measurements of three cutting conditions under flood coolant and MQL.  
Figure 6: Surface topography maps of the machined surface using T7 parameters and (**a**) the flood coolant and (**b**) MQL.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coolant** & **T1** & **T4** & **T7** \\ \hline Flood & 112.1 μm & 108.4 μm & 105.9 μm \\ MQL & 122.5 μm & 111.0 μm & 123.2 μm \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final flank wear for the three cutting conditions using both coolant conditions.  
The presence of crater wear on the rake face indicates elevated temperatures occurring in the cut. Due to the high temperatures and abrasive particles, the rake face of the tool was quickly delaminated of its coating in the cutting zone as shown in Figure 7. This caused the Inconel 617 workpiece to weld and adhere itself to the tool substrate, creating a built-up edge along the cutting edge. At higher intensity cuts, such as those performed in tests T6, T8, and T9, the built-up edge led to rapid tool failure and breakage.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 3 Results and Discussion
### Effect of Spring Constant  
All WECs considered have a density of 1000 kg/m\({}^{3}\). The spring constant value \(k\) plays a major role in obtaining stable movement. The values \(k_{1}\) = 0 N/m, \(k_{2}\) = 50 N/m, and \(k_{3}\) = 100 N/m were tested numerically. Their effect is discussed for W1 at \(d_{s0}\) = 0 and \(H\) = 0.04 m. The vertical and horizontal movements of W1 are shown in Figure 13a,b for all three \(k\) values.  
The sinusoidal trend of W1's vertical displacement with time is similar for all \(k\) values tested, but the trend of W1's horizontal displacement is not. As shown in Figure 13a, for \(k_{1}\), the vertical displacement is less stable than for the other two cases. The vertical and horizontal motions of the WECs are more stable in amplitude with \(k_{2}\). It can also be noticed that for \(k_{3}\), the vertical movement is regular, but the moment is lower.  
Figure 13c shows the instantaneous values of the power absorption of W1. It can be seen in Figure 13d that the float efficiency difference between \(k_{2}\) and \(k_{3}\) is negligible at  
Figure 12: (**a**,**b**) Effect of wave height (H) on absolute power absorbed and efficiency at \(d_{s1}\) = 0.1 m. (**c**) Effect of submergence depth (\(d_{s}\)). (**d**) Different wave energy converter shapes for \(d_{s0}\) on absolute power absorption. (**e**) Power absorption of WECs for \(d_{s2}\) at \(H\) = 0.04 m with constant spring \(k_{2}\). (**f**) Efficiency of all WECs at all \(d_{s}\).  
\(d_{s0}=0\) m. It seems that the float efficiency remains constant when increasing the spring constant from \(k_{2}\). Based on the above points, it can be concluded that the \(k_{2}\) spring constant is more suitable for WEC design optimization with increased submergence depth.
# Three Dimensional Digital Sieving of Asphal Mixture Based on X-ray Computed Tomography
## 7 Conclusions  
The aim of this study was to develop 3D digital sieving technique used to estimate the gradation of asphalt mixture in X-ray CT images. For this purpose, a concept named digital sieve size (DSS) was proposed, which was defined as the minimum length of the minimum bounding squares of all possible orthographic projections of an aggregate. The corresponding numerical method used to obtain DSS was introduced. In order to investigate the difference between mechanical sieve analysis and the digital sieving technique, four groups of epoxy-filled aggregate specimens were scanned and analyzed. As a convex basis lurked in the definition of DSS, there were a certain percent of aggregates in each specimen with their DSS larger than the upper limit of their mechanical size in the result. It was suggested that the concave surface of aggregate was the possible reason for the disparity between DSS and mechanical sieve size. After a linear calibration of DSS was performed for granite, the digital sieving technique was adopted to evaluate the gradation of stone mastic asphalt mixtures. The results showed that the closest proximity of the laboratory gradation curve was achieved by calibrated DSS among the gradation curves based on calibrated DSS, un-calibrated DSS, and equivalent diameter.  
Figure 11: Gradation curves for Specimen 1.  
Figure 12: Gradation curves for Specimen 2.  
Apart from good reliability after calibration and being easy to understand, the major advantage of DSS is that it considers the shape of the aggregate and the square opening of the mechanical sieve, as compared to the equivalent diameter. Although the benefit of DSS for a dense-grained asphalt mixture is weakened by the large quantity and volume summation of aggregates in each standard size range, DSS is promising in capturing size distribution of coarse/medium-grained mixture. Moreover, DSS is much more sensitive to the clustering of coarse aggregates than the equivalent diameter since clustering particles often have strange shapes and large aspect ratios. The gradation curve based on DSS can be used to select the optimum parameters for the segmentation algorithm.  
Supports provided by National Natural Science Foundation (51578248) and Pearl River S&T Nova Program of Guangzhou were greatly appreciated.  
[PERSON] proposed the idea, designed the experiments and simulation; [PERSON] performed the experiments and simulation; [PERSON] contributed image analysis tools and provided simulation suggestions.  
The authors declare no conflict of interest.
# Lightweight Sensor Authentication Scheme for Energy Efficiency in Ubiquitous Computing Environments
## 2 Related Work  
### Internet of Things  
Internet-based intelligent device communication technologies provide mutual connection networks between heterogeneous smart devices, including mini-sensors. Thus, environmental characteristics, such as the power of the sensor and computer, memory storage space, battery capacity, and communication bandwidth should be considered in IoT environments. Lightweight Implementation Guidance (LWIG), from the Internet Engineering Task Force (IETF) standardization organization, classifies IoT devices from 0 to 2 based on resource restrictions [11]. Class 0 devices have less than 10 KiB memory, including super lightweight devices with 100 KiB of maximum load code capacity. Therefore, Class 0 devices, as designated by LWIG, should use access technologies such as IEEE 802.15.4 or Low Power, which is categorized as a Low Power Lossy Network (LLN), considering their cost and efficiency [12]. Heterogeneous devices, such as sensors or smart devices, that have different functions are being used in building automation, environment monitoring, energy management, and military purposes through network communication; technologies such as mutual verification between devices, message sending verification, and information confidentiality that compose the IoT environment should be provided. The current IETF CORE group is standardizing the Constrained Application Protocol (CoAP) for IoT environment [13; 14], which applies security protocols used in existing Internet environments, such as Datagram Transport Layer Security (DTLS) and HIP, to provide secure services in resource-limited environments [15].  
However, these systems are still flawed: in the case of DTLS, since the total six-message packet that is sent has the fate-sharing characteristic, the whole message should be re-sent if a packet is missing. Resending message packets can lead to network overload and the degradation of limited devices like mini-sensors [16]. Although security standardization is progressing for IoT environments, the lightweighting plan of suggested security protocols does not accept all super lightweighted devices in heterogeneous IoT environments [17].
## 3 Methodology
### Landscape Pattern Index  
The landscape pattern indices of the construction land were calculated in Fragstats software for each year, and six landscape pattern indices, TA (Total Landscape Area), NP (Number of Patches), PD (Patch Density), LPI (Largest Patch Index), SPILT (Spiltting Index), and ENN-MN (Mean Euclidean Nearest Neighbour Distance), were selected to analyse the characteristics of the patch types of the construction land [43] (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline
**Index Name** & **Abbreviation** & **Unit** & **Ecological Meaning** \\ \hline Total landscape area & TA & \(\text{hm}^{2}\) & TA indicates the total area of patches or landscape. \\ Number of patches & NP & \# & NP indicates the total number of patches or landscape. \\ Patch density & PD & \#/100 \(\text{hm}^{2}\) & PD indicates the number of a given patch type per unit area. \\  & & & The LPI is a direct reflection of the dominant type of landscape and a direct reflection of changes in the intensity and frequency of human activity disturbing land use and ecological change in the landscape. \\  & & & SPILT indicates the degree of fragmentation of patches in the area; the higher the SPILT, the higher the degree of landscape fragmentation. \\ Mean Euclidean nearest neighbour distance & ENN-MN & m & ENN_MN indicates the connectivity of the landscape; the smaller the ENN_MN value, the greater the connectivity of the landscape. \\ \hline \end{tabular}
\end{table} 
Table 2: List of landscape pattern indices selected of this study. In order to meet the housing needs brought by the large population of Hong Kong between 1972 and 1980, the British Hong Kong government introduced the \"Ten-Year Housing Construction Plan\", aiming to build low-rent housing that could accommodate 1.8 million people before 1983. The large-scale construction activities led to the construction land in Hong Kong increasing by 30.4 km\({}^{2}\), at a rate of 3.81 km\({}^{2}\)-a\({}^{-1}\) and an expansion intensity of 3.22%. Sha Tin, Tuen Mun, Yuen Long, and other second-generation new towns began developing rapidly. The construction land in Hong Kong increased by 27.8 km\({}^{2}\), at a rate of 2.78 km\({}^{2}\)-a\({}^{-1}\), from 1980 to 1990, with an expansion intensity of 1.87%. The construction activities in Tuen Mun and Yuen Long districts was further develop during this period, and the degree of connectivity continued to increase; in addition, reclamation activities were an important factor in the expansion of construction land in Hong Kong (Table 3). The growth trend of construction land continued from 1990 to 1997, with an area of 208 km\({}^{2}\), a growth rate of about 3.3 km\({}^{2}\)-a\({}^{-1}\), and an expansion intensity of 2.48% (1990-1995). This was due to the announcement of the Hong Kong Airport Core Programme (also  
Figure 4: Line chart of expansion intensity of construction land in Hong Kong.
## 4 Coordination of PSS and BESS Controllers to Enhance Oscillatory Stability Using Proposed Method
### Power-System-Controller Tuning Using Optimization Technique
#### 4.2.2 Solving Optimization Problem Using Firefly Algorithm  
The firefly algorithm is one of the most popular swarm-based optimization algorithms, and its pseudocode is given below:  
```
Computation of value for objective function \(f(x)\) with t decision variables \(x=(x_{1},x_{2},....x_{t})^{T}\)  Generate initial random population for \(n\) fireflies \(x_{i}(i=1,2,3,....,n)\)  Calculate light intensity \(L_{i}\) for \(x_{i}\) using \(f(x_{i})\)  Define light-absorption coefficient \(\gamma\)  While (k < Maximum Generation) for \(i=1:n\), all \(n\) fireflies  for \(j=1:n\), all \(n\) fireflies  if (\(L_{i}<L_{j}\)), Move firefly \(i\) towards \(j\); end if  Change attractiveness with distance \(r\) using \(\exp^{-\gamma r}\)  Compute new solutions and update light intensity  end for \(j\) end for \(i\) Rank fireflies and find current global best solution \(g_{b}\) end while
```  
**Algorithm 1**General pseudocode of firefly algorithm  
The general flowchart of utilizing the firefly algorithm to tune power0 system controllers in our study is shown in Figure 10 and is briefly described as follows:  
1. In the first step, each control parameter \(x_{i}\) is assigned an array of random numbers, with the total number size of fireflies \(N_{pop}\) as: \[x_{i}= [K_{i,pod},K_{i,pss},T_{1i,pod},T_{1i,pss},T_{2i,pod},T_{2i,pss},T_{3i,pod},T_{3i,pss},T_{4i,pod},T_{4i,pss}]\] (18)
2. Control parameters are then used to compute the objective function using Equation (16), whose values represent firefly light intensity. The fireflies are then sorted and accordingly ranked. The so-obtained control parameters after ranking are termed as \(x_{j}\).
3. The movement of less-bright fireflies \(x_{i}\) to brighter fireflies \(x_{j}\) is given by: \[x_{i}^{t+1}=x_{i}^{t}+\beta\exp^{-\gamma r_{ij}^{2}}\left(x_{j}^{t}-x_{i}^{t} \right)+\alpha_{t}\varepsilon_{j}^{t}\] (19) where \(x_{i}^{t+1}\) is the position of the fireflies at the next iteration, \(\beta\) is the attractiveness coefficient, \(\varepsilon_{j}^{t}\) is the vector of random numbers, \(\gamma\) is the light-absorption constant, \(\alpha_{t}\) is the randomization parameter, and \(r_{ij}\) is the Cartesian distance between two fireflies.
4. If the value of a control parameter exceeds its range, it is reset back to its maximum or minimum value, depending upon its nearness to the extreme value given by Equation (17).
5. Continue to the next iteration until the maximum number of iterations is reached.
## 3 Results
### Preliminary Seeding Growth Tests
#### 3.3.3 The Choice of Soaking Time  
Table 4 presents the effect of different time of seed soaking (1, 12, and 24 h) on seedlings' weight and length. Generally, it was found that the average seedlings' length decreased when the soaking time of seeds increased. The best results were obtained for the control group, where seeds, before tests, were kept in a fridge for 1 h. The average seedlings' length in this group was 2.5 times higher than in C-H\({}_{2}\)O-Zn, 46% higher than in F 50%-Zn, 24% higher than in C-H\({}_{2}\)O and only 3.3% higher than in F 50% (for soaking that lasted 1 h).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Applied Doses for** & **Average Seedlings** & **Average Seedlings Dry** \\  & **Seed Coating** & **Length 1 (cm) \(\pm\) SD** & **Weight 2 (g) \(\pm\) SD** \\ \hline C (Control) & - & 5.75 \(\pm\) 1.96 & 0.241 \(\pm\) 0.013 \\ \hline \multirow{2}{*}{C-H\({}_{2}\)O-Zn} & 50 mg/g of seeds & 6.45 \(\pm\) 1.89 & 0.314 \(\pm\) 0.038 \\  & 100 mg/g of seeds & 6.30 \(\pm\) 1.95 & 0.308 \(\pm\) 0.021 \\ \hline \multirow{2}{*}{H 10\%} & 50 mg/g of seeds & 7.05 \(\pm\) 1.94 & 0.303 \(\pm\) 0.117 \\  & 100 mg/g of seeds & 5.76 \(\pm\) 1.69 & 0.270 \(\pm\) 0.057 \\ \hline \multirow{2}{*}{H 10\%-Zn} & 50 mg/g of seeds & 7.05 \(\pm\) 1.07 & 0.357 \(\pm\) 0.021 \\  & 100 mg/g of seeds & 6.90 \(\pm\) 1.41 & 0.357 \(\pm\) 0.041 \\ \hline \end{tabular}
\end{table}
Table 3: The effect of the dose of algal homogenate (H) on the length and weight of the seedlings (\(N=2\)).  
The negative effect on seedlings' growth came from filtrate enriched with Zn(II) ions (F 50%-Zn) and ZnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O solution (C-H\({}_{2}\)O-Zn). Seedlings' length in these groups for all tested soaking periods (1, 12 and 24 h) was much lower than for both control groups (i.e., without soaking (C) and soaking with distilled water--C-H\({}_{2}\)O).  
Summarizing these experiments, it can be concluded that comparable results were obtained for the group without soaking, and for the group in which seeds were macerated with F 50% for one hour. However, also taking into account the average weight of the seedlings, soaking of seeds with F 50% for one hour is recommended. The average weight of seedlings in this group was 12% higher than for F 50%-Zn, 10% higher than for ZnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O solution, 35% higher than for C-H\({}_{2}\)O (seeds macerated for one hour) and 56% higher than control--without soaking.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## Appendix A
### The Second Term  
The second term is:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\int_{-T_{c}/2}^{T_{c}/2}\exp\biggl{[}-j\biggl{\{} (\omega_{c}+\omega)t+\frac{\mu t^{2}}{2}\biggr{\}}\biggr{]}dt \tag{10}\]  
which can be expressed as:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\exp\biggl{[}j\frac{(\omega+\omega_{c})^{2}}{2 \mu}\biggr{]}\int_{-T_{c}/2}^{T_{c}/2}\exp\biggl{[}-j\frac{\mu}{2}\biggl{(}t+ \frac{(\omega+\omega_{c})}{\mu}\biggr{)}^{2}\biggr{]}dt \tag{11}\]  
with the assumption of \(\sqrt{\mu}\biggl{(}t+\frac{\omega+\omega_{0}}{2}\biggr{)}=\sqrt{\pi}x\), and then \(dt=\sqrt{\pi/\mu}dx\), (11) can be expressed as:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\sqrt{\frac{\pi}{\mu}}\exp\biggl{[}j\frac{( \omega+\omega_{c})^{2}}{2\mu}\biggr{]}\int_{-X_{3}}^{X_{4}}\exp\biggl{[}-j \frac{\pi}{2}x^{2}\biggr{]}dx \tag{12}\]  
\(X_{3}\) and \(X_{4}\) are as:  
\[\left\{\begin{array}{l}X_{3}=\frac{\mu T_{c}/2-(\omega+\omega_{c})}{\sqrt{ \pi}\mu}\\ X_{4}=\frac{\mu T_{c}/2+(\omega+\omega_{c})}{\sqrt{\pi}\mu}\end{array}\right. \tag{13}\]  
Based on the Euler formula:  
\[\exp\biggl{[}-j\frac{\pi}{2}x^{2}\biggr{]}=\cos\biggl{(}\frac{\pi}{2}x^{2} \biggr{)}-j\sin\biggl{(}\frac{\pi}{2}x^{2}\biggr{)} \tag{14}\]  
(12) can be re-written as:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\sqrt{\frac{\pi}{\mu}}\exp\biggl{[}j\frac{( \omega+\omega_{c})^{2}}{2\mu}\biggr{]}\int_{-X_{3}}^{X_{4}}\Bigl{[}\cos\left( \frac{\pi}{2}x^{2}\right)-j\sin\left(\frac{\pi}{2}x^{2}\right)\Bigr{]}dx \tag{15}\]  
which can be further simplified as:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\sqrt{\frac{\pi}{\mu}}\exp\biggl{[}j\frac{( \omega+\omega_{c})^{2}}{2\mu}\biggr{]}\biggl{\{}\int_{-X_{3}}^{X_{4}}\cos \Bigl{(}\frac{\pi}{2}x^{2}\Bigr{)}dx-j\int_{-X_{3}}^{X_{4}}\sin\Bigl{(}\frac{ \pi}{2}x^{2}\Bigr{)}dx\biggr{\}} \tag{16}\]  
Substituting Fresnel cosine and sine function into the above equation, we have:  
\[U_{2}(\omega)=\frac{\sqrt{2}}{2}\sqrt{\frac{\pi}{\mu}}\exp\biggl{[}j\frac{( \omega+\omega_{c})^{2}}{2\mu}\biggr{]}[C(X_{3})+C(X_{4})-jS(X_{3})-jS(X_{4})] \tag{17}\]  
\(C(X)\) and \(S(X)\) are shown in (12). Substituting \(\omega=2\pi f\), \(\omega_{c}=2\pi f_{c}\), \(\mu=B