esa-sceva/satcom-chunk-collection huggingface dataset:
# Observer-Based Distributed Fault Detection for Heterogeneous Multi-Agent Systems
## 3 Results
### State-Feedback-Based Distributed FD
Then, Theorems 1 and 2 together guarantee that the faulty agent will be detected._  
Remark 7: _Existence of FD observers just requires \(c>0\), which is the same as realisation conditions of COR, i.e., control law (7) and observers (15) are able to run simultaneously. What is more, if the remaining follower agents still have paths to the leader after removing the faulty agent, the remaining follower agents could still realise COR._  
According to residual dynamics (17), residual generators will not converge to zero until time approaches infinity, therefore, appropriate isolation thresholds need to be set [21]. Then, the following location algorithm is given to locate the possibly faulty agent in \(\bar{\mathcal{N}}_{i}\). 1. In agent \(i\), construct residual generators \(r_{i_{\tilde{H}_{k}}}\) and set appropriate thresholds \(\Theta_{i_{\tilde{H}_{k}}}>0\), \(k=1,\cdots,|\mathcal{N}_{i}|\). 2. Run residual generators \(r_{i_{\tilde{H}_{k}}},k=1,\cdots,|\mathcal{N}_{i}|\). (1) Check \(r_{i_{\tilde{H}_{k}}},k=1\). If \(||r_{i_{\tilde{H}_{k}}}||<\Theta_{i_{\tilde{H}_{k}}}\), and \(||r_{ij}||\geq\Theta_{ij},j\
eq\tilde{i}_{k}\), stop and remove the faulty node. Otherwise, go to step (2). \[\vdots\]  
\((|\mathcal{\bar{N}}_{i}|)\) Check \(r_{i_{\tilde{H}_{k}}},k=|\mathcal{\bar{N}}_{i}|\). If \(||r_{i_{\tilde{H}_{k}}}||<\Theta_{i_{\tilde{H}_{k}}}\), and \(||r_{ij}||\geq\Theta_{ij},j\
eq\tilde{i}_{k}\), stop and remove the faulty node. **Remark 8**. : _A simple selection method of isolation thresholds \(\Theta_{ik}\) is shown as follows. Assume that the initial error \(e_{i_{\tilde{H}_{k}}}(0)\leq\) is bounded, i.e., \(||e_{i_{\tilde{H}_{k}}}(0)||\leq\epsilon\). Then, one can compute the threshold by considering an upper estimate of the error expression (17):_  
\[\Theta_{i_{\tilde{H}_{k}}}=\Gamma_{i_{\tilde{H}_{k}}}(t)||\mathbf{C}_{i}||\epsilon\]  
_where \(\Gamma_{i_{\tilde{H}_{k}}}(t)\), which can be obtained by Jordan decomposition, is such that \(||e^{G_{i_{\tilde{H}_{k}}}t}||\leq\Gamma_{i_{\tilde{H}_{k}}}(t)\)._  
_What is more, selection of isolation thresholds is also related with trade-offs between false alarm and misdetection rate, among others [30], where more details could be found in [24] and references there-in._
## 5 Simulation Analysis
### Simulation Results
#### 5.2.2 Altimeter Scene
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Error** & **Scene** & **Mean** & **STD** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Lon (\({}^{\circ}\)) \\ \end{tabular} } & altimeter without deviation & \(-5.9788\times 10^{-5}\) & \(7.0759\times 10^{-5}\) \\  & fixed deviation 30 m & \(-7.4776\times 10^{-5}\) & \(6.5614\times 10^{-5}\) \\  & fixed deviation \(-30\) m & \(-4.4802\times 10^{-5}\) & \(7.9162\times 10^{-5}\) \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Lan (\({}^{\circ}\)) \\ \end{tabular} } & altimeter without deviation & \(-1.3456\times 10^{-4}\) & \(2.4540\times 10^{-4}\) \\  & fixed deviation 30 m & \(1.3196\times 10^{-4}\) & \(2.4609\times 10^{-4}\) \\  & fixed deviation \(-30\) m & \(-4.0106\times 10^{-4}\) & \(2.5189\times 10^{-4}\) \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Alt (m) \\ \end{tabular} } & altimeter without deviation & \(8.1980\times 10^{-4}\) & \(0.0182\) \\  & fixed deviation 30 m & \(-29.9270\) & \(1.4696\) \\ \cline{1-1}  & fixed deviation \(-30\) m & \(29.9286\) & \(1.4617\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Trajectory error statistics. Figure 7: Single-satellite navigation and positioning results in altimeter scenes. 3. In terms of the final trajectory error, the mean error accuracy of longitude error is increased from 10.59% to 81.77%, and the STD is increased from 38.12% to 77.21%; the latitude error accuracy mean and STD accuracy are improved by 77.13% and 62.27%, respectively; the mean value of the altitude error accuracy has increased from 32.40% to almost 100%; and the STD has increased from 15.00% to 99.98%. From the above qualitative and quantitative analysis, we can find that due to the addition of altimeters, the positioning error of the LEO 1 satellite + INS + altimeter algorithm has been significantly improved. Taking the mean value as an example, the position error is reduced from the 100 to 10 m level. Especially in the case of no deviation altimeter, the error can be reduced to a sub mm level. Even if the altimeter has a deviation, after deducting the fixed deviation, the average error is 0.0730 m (altimeter fixed deviation is 30 m) and 0.0714 m (altimeter fixed deviation is \(-\)30 m), and the positioning accuracy reaches the cm level. The INS position divergence can be effectively suppressed. Similarly, the velocity error has also been significantly improved. The longitude, latitude, and altitude accuracy mean and STD of the final navigation trajectory have also been greatly improved, especially the mean and STD of the altitude error, which decreased from 70.4270 and 309.3698 m without an altimeter to 29.9286 and 1.4617 m under the altimeter scene, respectively. The variance is more concentrated, which indicates that the navigation system is more stable and robust.
## 5 Conclusions
### Nomenclature  
PV photovoltaic  
PV/T photovoltaic/thermal  
STC standard test condition  
RMSE
root mean square error  
MAE mean absolute error  
NOCT nominal operating cell temperature  
MPPT maximum power point tracking  
EVA ethylene-vinyl acetate  
Quantities used in equations:  
\(A_{\text{PV}}\)surface area of the PV module  
C heat capacity  
\(d\) thickness  
\(E_{\text{g0}}\) bandwidth of cell material  
\(FF\) fill factor  
\(G\) solar radiation  
\(G_{\text{STC}}\) input signal  
\(h_{\text{cond}}\) conductive heat transfer coefficient  
\(h_{\text{conv}}\) convective heat transfer coefficient  
\(I_{\text{01}}\) reverse saturation current of the first diode  
\(I_{\text{Q2}}\) reverse saturation current of the second diode  
\(I_{\text{DC}}\) direct current  
\(I_{\text{MPPSTC}}\)  
\(I_{\text{Ph}}\) photocurrent  
\(I_{\text{SC}}\) short-circuit current  
\(I_{\text{SC,STC}}\) maximal output current  
\(K\) Boltzmann constant (1.38065 \(\times\) 1023 J/K)  
\(k\) thermal conductivity  
\(K_{\text{i}}\) extinction coefficient  
\(n\) real refractive index  
\(n_{1}\) the diode ideality factor of the first diode  
\(n_{2}\) the diode ideality factor of the second diode  
\(N_{\text{S}}\) number of series-connected PV cells  
\(P_{\text{DC}}\) direct output power  
\(P_{\text{STC}}\) output power under STC conditions  
\(q\) electron charge (1.602 \(\times\) 1019 C)  
\(R_{\text{s}}\) series resistance  
\(R_{\text{sh}}\) shunt resistance  
\(T\) operating temperature of the PV module  
\(T_{\text{a}}\) ambient temperature  
\(T_{\text{STC}}\) output signal  
\(v\) wind speed  
\(V_{\text{DC}}\) direct voltage  
\(V_{\text{MPPSTC}}\)  
\(V_{\text{OC}}\) open-circuit voltage  
\(V_{\text{OC,STC}}\) open-circuit voltage under STC conditions  
\(V_{\text{T1}}\) thermal voltage of the first diode  
\(V_{\text{T2}}\) thermal voltage of the second diode  
\(\alpha\) absorptivity  
\(\Theta_{1}\) angle of incidence  
\(\Theta_{2}\) angle of reflection  
\(\mu_{\text{MPP}}\) temperature coefficient of \(P_{\text{MPP}}\)  
\(H_{\text{SC}}\) temperature coefficient of \(I_{\text{SC}}\)  
\(\mu_{\text{VC}}\) temperature coefficient of \(V_{\text{OC}}\)  
\(\rho\) density  
\(\tau_{\text{g}}\) transmissivity\(\varphi_{1}\) heat flux ratio  
\(\varphi_{2}\) thermal radiation flux
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## Experimental Program
### Experimental Setup and Measurement Scheme  
The test setup is depicted in Figure 2. All specimens were tested using a 5000 kN compression machine. The roller hinge support was placed above and below the specimen to realize hinge support at both ends. The load eccentricity was varied by moving the hinge support. The load was recorded using a 2000 kN pressure sensor placed on the top of the specimen, and the crack development was recorded using a crack observer with an accuracy of 0.02 mm. A DH3821 static strain tester was used to record the changes in strain and displacement.  
Five electronic displacement meters were evenly arranged along the height of the specimen, at 250 mm intervals, as depicted in Figure 2. Steel strain gauges were placed on the longitudinal reinforcement along the height of the column. Six concrete strain gauges were placed on the concrete surface of the column at 0, 50, 100, 150, 225, and 300 mm from the edge of the section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\begin{tabular}{c} **Eccentricity** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Stirrup** \\ **Diameter** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Stirrup** \\ **Spacing** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Longitudinal** \\ **Reinforcement** \\ **Bar** \\ **Bars** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Reinforcement** \\ **Ratio (\%)** \\ **(mm)** \\ \end{tabular} } \\ \hline SSRC1 & 80 & 8 & 125 & 4 & 12 & 0.60 \\ SSRC2 & 270 & 8 & 125 & 4 & 12 & 0.60 \\ SSRC3 & 80 & 8 & 125 & 4 & 16 & 1.07 \\ SSRC4 & 150 & 8 & 125 & 4 & 16 & 1.07 \\ SSRC5 & 200 & 8 & 125 & 4 & 16 & 1.07 \\ SSRC6 & 270 & 8 & 125 & 4 & 16 & 1.07 \\ SSRC7 & 80 & 8 & 125 & 4 & 25 & 2.61 \\ SSRC8 & 270 & 8 & 125 & 4 & 25 & 2.61 \\ HRC9 & 270 & 8 & 125 & 4 & 16 & 1.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specim specifications.  
Figure 1: Geometry of column specimen (considering SSRC1 as an example; units: mm).
## 2 Thick Level Set Damage Model
### Local Governing Equations and Time-Independent Damage Evolution Law  
Under the assumptions of small strains and displacements, the following set of equations is solved:  
\[\left\{\begin{array}{lclcl}\
abla.\sigma&=&0&on&\Omega\\ \sigma.n&=&f&on&\partial\Omega_{n}\\ u&=&u_{0}&on&\partial\Omega_{d}\end{array}\right. \tag{2}\]  
where \(\sigma\) is the Cauchy stress tensor, \(u\) the displacement vector, \(f\) an external load applied on \(\partial\Omega_{n}\), \(n\) the normal vector and \(u_{0}\) prescribed displacements imposed on \(\partial\Omega_{d}\).  
Defining a free energy \(W\), the stress tensor \(\sigma\) and the energy release rate \(Y\) can be related to the strain tensor and the damage as follows:  
\[\left\{\begin{array}{lcl}\sigma&=&\frac{\partial W(\varepsilon,d)}{\partial \varepsilon}\\ Y&=&-\frac{\partial W(\varepsilon,d)}{\partial d}\end{array}\right. \tag{3}\]  
Figure 1: Schematic illustration of the sound, partially-damaged and fully-damaged zones on a computational domain \(\Omega\) under Neumann and Dirichlet boundary conditions.  
In the present paper, we use a free energy form \(W\) reflecting the concrete dissymmetric behavior in tension and compression:  
\[W(\varepsilon,d)=\mu(1-\alpha_{i}d)\varepsilon_{i}^{2}+\frac{\lambda}{2}(1- \alpha d)tr(\varepsilon)^{2} \tag{4}\]  
where \(\varepsilon_{i}\) are the eigenvalues of \(\varepsilon\), \(\mu\) and \(\lambda\) are Lame coefficients and \(\alpha\) and \(\alpha_{i}\) are two parameters ensuring the dissymmetric behavior: \(\alpha=\left\{\begin{array}{ccc}0&if&tr(\varepsilon)<0\\ 1&if&tr(\varepsilon)\geq 0\end{array}\right.\) and \(\alpha_{i}=\left\{\begin{array}{ccc}0&if&\varepsilon_{i}<0\\ 1&if&\varepsilon_{i}\geq 0\end{array}\right..\)  
As for the local damage evolution law [22; 23; 24; 25], a time-independent law is adopted where the damage grows when \(Y\) reaches the critical energy release rate \(Y_{c}\):  
\[Y-Y_{c}\leq 0;\quad\dot{d}\geq 0;\quad(Y-Y_{c})\dot{d}=0 \tag{5}\]
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Effectiveness of IoTBystander
#### 7.2.5 Generalizability on Standard Datasets  
To further assess the generalizability of IoTBystander, we test IoTBystander on additional datasets. Given the absence of datasets that contain both types of data for smart home scenarios, we selected the _ESC-16_ audio dataset and the _PingPong_ dataset for network traffic. The _ESC-16 dataset_ is a subset of the _ESC-50_ dataset [61], containing 5-second audio recordings of 16 relevant environmental sound classes, which were chosen to fit smart home applications. The _PingPong_ dataset [58], on the other hand, comprises Wi-Fi traffic traces from thirteen different events involving seven IoT devices. Both datasets were divided into training, validation, and test sets in a 70%, 15%, 15% split, respectively.  
The results from these experiments, presented in Table 10, highlight the robustness of IoTBystander across varied datasets. On the ESC-16 dataset, the system achieved an accuracy of 93.42% with a 5.18% false positive rate (FPR), demonstrating its effective recognition of environmental sounds relevant to smart home applications. In contrast, the PingPong dataset, which deals with IoT traffic, resulted in a higher accuracy of 97.71% with no false positives, indicating the model's precision in identifying IoF events based on network traffic signatures. These results highlight that IoTBystander's dual-channel  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Metric**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \cline{3-10}  & & **TER** & **AER** & **DCA** & **TER** & **AER** & **DCA** & **TER** & **AER** & **DCA** \\ \hline \multirow{3}{*}{Smart Devices} & SD & 0.1816 & – & 0.1816 & 0.1816 & – & 0.1816 & 0.1816 & – & 0.1816 \\ \cline{2-10}  & \multirow{3}{*}{CI} & [0.8239, 1.0333] & – & [0.8239, 1.0333] & [0.8239, 1.0333] & – & [0.8239, 1.0333] & – & [0.8239, 1.0333] & – & [0.8239, 1.0333] \\ \hline \multirow{3}{*}{Dumb Devices} & SD & – & 0.0633 & 0.0633 & – & 0.0371 & 0.0371 & – & 0.0413 & 0.0413 \\ \cline{2-10}  & \multirow{3}{*}{CI} & – & [0.9115, 0.9115, 0.9115, 0.9191] & – & [0.9257, 0.9257, 0.9277] & – & [0.9580, 1.09580, 1.0086] & \multirow{3}{*}{0.1086} \\ \cline{2-10}  & & & 0.9919] & 0.9919 & – & 0.9727] & 0.9727] & – & [1.0086] & [1.0086] \\ \hline \multirow{3}{*}{All Devices} & SD & 0.4899 & 0.4042 & 0.2915 & 0.4899 & 0.4816 & 0.3339 & 0.4899 & 0.4846 & 0.137 \\ \cline{2-10}  & \multirow{3}{*}{CI} & [0.3022, 0.4135, 0.8058, 1.03022, 0.3586, 0.718, 1.0302] & 0.781 & [0.3022, 0.3166, 0.883, 0.6978] & 0.7708] & 1.016 \\ \cline{1-1} \cline{2-10}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistical stability of the performance results in Table 8.  
approach remains effective even when applied to external datasets, further demonstrating its generalizability and confirming its capability to adapt to various smart home environments without overfitting to the original testbed.
# Design and Implementation of a Polar Wind and Solar Hybrid Power Supply Controller
## 3 Controller Hardware Design
### Photovoltaic Power Generation Circuit  
The main circuit of photovoltaic power generation is shown in Figure 11. This circuit is also a Boost structure, and will not be described here. The rated power of the PV array is 750 W, the rated input voltage is 30 V, the inductor ripple factor is 10%, the voltage ripple factor is 1%, and the switching frequency of the main power tube is set to 48 kHz. After calculation, the main circuit inductance of the photovoltaic power generation is taken as 220 \(\mu\)H, the output filter capacitor is 680 \(\mu\)F and 0.1 \(\mu\)F, and the input capacitance is 1000 \(\mu\)F and 0.1 \(\mu\)F.
## 3 Results and Discussion
### Ni Renoval Mechanism
#### 3.6.3 XRD  
Figure 12 shows the XRD plots of nZVI@ATP before and after Ni\({}^{2+}\) adsorption. The characteristic peaks of Fe\({}^{0}\) were found at 44.9\({}^{\circ}\) and 65.2\({}^{\circ}\)[37; 38], those of Fe\({}_{2}\)O\({}_{3}\) and Fe\({}_{3}\)O  
Figure 11: FT-IR before and after the reaction of nZVI@ATP with Ni.  
Figure 10: SEM–EDS map of nZVI@ATP after adsorption of Ni\({}^{2+}\).  
at 35.59\({}^{\circ}\)and 35.4\({}^{\circ}\)[23], and that of FeOOH at 26.58\({}^{\circ}\), as seen in the plot of nZVI@ATP, indicating that the material was oxidized during the preparation or preservation process and that the nZVI @ATP surface generated iron oxides. After the reaction, the characteristic diffraction peaks at 20 of 44.9\({}^{\circ}\) and 65.2\({}^{\circ}\) disappeared, indicating that the Fe\({}^{0}\) on the nZVI surface underwent a sufficient reaction and was completely consumed. The substantially enhanced characteristic peaks observed at 35.59\({}^{\circ}\) and 35.4\({}^{\circ}\) indicated that the nZVI was further oxidized to Fe\({}_{2}\)O\({}_{3}\) and Fe\({}_{3}\)O\({}_{4}\) during the reaction. The characteristic peak at 62.40\({}^{\circ}\) corresponded to Ni(OH)\({}_{2}\)[39], while the characteristic peak of Ni\({}^{0}\) was detected at 78.2\({}^{\circ}\)[40], indicating that part of the Ni\({}^{2+}\) was reduced to Ni\({}^{0}\) and part of the Ni\({}^{2+}\) combined with hydroxide to produce a Ni(OH)\({}_{2}\) precipitate [41].
## 2 Results
### Expression and Purification of a Recombinant Goat UK114  
A recombinant form of goat UK114 was produced in _E. coli_. The protein was isolated from cell extracts by Ni\({}^{++}\)-affinity chromatography, subjected to thrombin cleavage to remove the N-terminal His-tag, and then purified by gel filtration (see Materials and Methods). SDS-PAGE of the purified protein showed the presence of a single polypeptide of about 14 kDa (Figure S1a). Protein purity, as determined by liquid chromatography-mass spectrometry (LC-MS), was above 95% (Figure S1b). Analytical gel filtration on a calibrated Superdex 75 column showed that UK114 eluted in a single symmetrical peak with an apparent mass of about 36.4 kDa (Figure S2a). This value, which is lower than 43 kDa, is in good agreement with the assembly of UK114 into a compact homotrimer, as reported in a previously published study [44]. Native mass spectrometry (MS) confirmed the homotrimer structure of UK114 yielding a molecular mass of 43736 Da although the monomeric form was also detectable due to protein denaturation during the electrospray process (Figure S2b). Protein sequencing was complete (100% coverage with high confidence) and is consistent with the expected sequence harboring three extra amino acids (GSH) when compared to the predicted UK114 sequence in protein database and four extra amino acids at the N-terminal (GSHM), with respect to the natural UK114 that lacks the initiator methionine and is _N_-acetylated on Ser2 [21]. No traces of miscleaved His-tag were detected (Table S1).  
Secondary structure composition of UK114 in solution was estimated from the far-UV circular dichroism (CD) spectra recorded at 20 \({}^{\circ}\)C, and normalized in terms of mean residual ellipticity ([G]\({}_{\text{MRW}}\)) (Figure S3). Deconvolution of the spectrum results in the following composition: \(\alpha\)-helix18.7%, antiparallel \(\beta\)-sheets 20.2%, parallel \(\beta\)-sheets 5.6%, \(\beta\)-turn 18.6%, and random coil 33.6%. The secondary structure composition estimated from spectroscopic data is in good agreement with the crystallographic structure of UK114 (PDB entry 1 nq3, [42]) that contains, out of the total 137 residues of each monomer, 31 amino acids that are located in \(\alpha\)-helical regions (22.6%) and 42 in \(\beta\)-sheet regions (30%). Moreover, two out of the six strands characteristic of each monomer in the crystallographic structure are parallel, thus supporting the results that were obtained from the deconvolution.
## 3 Survey Results
### Sensor Mote: Microcontroller
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Nr** & **Codename** & **MCU count** & **MCU Name** & **Architecture,** & **MHz** & **RAM, KB** & **Program Memory, KB** \\ \hline
8 & Oil tanker & 1 & Zeevo ARM/TDMI & 32 & 12 & 64 & 512 \\
9 & Enemy vehicles & 1 & Atmel ATMega128L & 8 & 4 & 4 & 128 \\
10 & Trose game & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
11 & Elder RFID & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
12 & Murphy potatoes & 1 & Atmel ATMega128L & 8 & 8 & 4 & 128 \\
13 & Firewanet & 1 & Atmel ATMega128L & 8 & 7.3 & 4 & 128 \\
14 & AlarmNet & 1 & Atmel ATMega128L & 8 & 7.3 & 4 & 128 \\
15 & Ecuador Volcano & 1 & Texas Instruments & (TI) & 16 & 8 & 10 & 48 \\  & & MSP430F1611 & & & & & \\
16 & Pet game & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
17 & Plug & 1 & Atmel ATM51 SAM7S64 & 32 & 48 & 16 & 64 \\
18 & B-Live & 2 & Microchip PIC18F2580 & 8 & 40 & 1.5 & 32 \\
19 & Biomotion & 1 & TI MSP430F149 & 16 & 8 & 2 & 60 \\
20 & AID-N & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
21 & Firefighting & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
22 & Rehabil & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
23 & CargoNet & 1 & TI MSP430F135 & 16 & 87 & 0.512 & 16 \\
24 & Fence monitor & 1 & TI ATM5430F1612 & 16 & 7.3 & 5 & 55 \\
25 & BikeNet & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
26 & BriMon & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
27 & IP net & 1 & TI MSP430F149 & 16 & 8 & 2 & 60 \\
28 & Smart home & 1 & Atmel ATMega128 & 8 & 8 & 4 & 128 \\
29 & SVATS & 1 & Atmel ATMega128L & 8 & 7.3 & 4 & 128 \\
30 & Hitchhiker & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
31 & Daily morning & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
32 & Heritage & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
33 & AC meter & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\
34 & Coal mine & 1 & Atmel ATMega128 & 8 & 7.3 & 4 & 128 \\
35 & ITS & 2 & ARM7 + MSP430F1611 & 32 + 8 & 7 + 8 & 64 + 10 &? + 48 \\
36 & Underwater & 1 & NXP LPC2148 ARM/TDMI & 32 & 60 & 40 & 512 \\
37 & PipeProbe & 1 & Nordic nRF24E1 DW8051 & 8 & 16 & 4.25 & 32 \\
38 & Badgers & 1 & Atmel ATMega128V & 8 & 8 & 8 & 128 \\
39 & Helens volcano & 1 & Intel XScale PVA271 & 32 & 13 (624 max) & 256 & 32768 \\
40 & Tunnels & 1 & TI MSP430F1611 & 16 & 8 & 10 & 48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont_. 32. Therefore, RAM memory effective usage is more important, and a reasonable amount of program memory can be sacrificed for that matter.
## 3 Results and Discussion
### Possible Application Mode of the Sorbent  
To reduce anthropogenic mercury emissions into the environment, more than 128 nations have approved the Minamata Convention on Mercury until 4 November 2018. As one of the most important anthropogenic sources, coal-fired power plants should make efforts to reduce their Hg\({}^{0}\) emissions. The existing air pollution control devices cannot efficiently remove the Hg\({}^{0}\), so some new devices are needed to control the Hg\({}^{0}\) emissions. A possible application mode of the sorbent for Hg\({}^{0}\) control from coal-fired flue gas is shown in Figure 9. One option is to inject the mercury sorbent into the flue gas downstream of the electrostatic precipitator (ESP) and upstream of a small fabric filter (FF), as shown in Figure 9a. In this way, a injector and an FF should be introduced to the flue gas clean system. The sorbent will capture Hg\({}^{0}\) in the flue gas and will be then collected by the FF so as to achieve separation from the fly ash. In this way, the spent sorbent will probably be regenerated. In addition, the FF can efficiently remove fine particulate matter (PM\({}_{2.5}\)) simultaneously. The other option is to make use of a fixed adsorption bed, as shown in Figure 9b. The sorbent could be shaped into a honeycomb or plate, like the selective catalytic reduction (SCR) catalysts.  
Figure 8: Hg\({}^{0}\) removal efficiency of 15%MnO\({}_{\rm x}\)–15%CeO\({}_{2}\)/CNFs(OX) under different individual flue gas components at 120 \({}^{\circ}\)C.