esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Ionic Liquids
Upon biorecognition of hCG on the antibody, peak current of rutin decreased. Through DVP measurements, it was found that hCG could be detect within a linear range between 2 and 350 mIU mL\({}^{-1}\), with a LoD of 0.4 uU mL\({}^{-1}\). Figure 34: (**A**) CEA and AFP immunosensor based on metal NPs and Prussian blue NPs immobilized on IL-modified GO sheets; (**B**) DPV responses for different concentration of CEA and AFP in PBS, pH 6.5. Reproduced from [172], Copyright 2014, with permission from Elsevier. Finally, [PERSON] et al. [176] reported detection of PSA where most of the above-reviewed strategies were cumulated in a single sensor. Anti-PSA and thionine (as redox mediator) were immobilized onto AuNPs-PAMAM and MWCNT/IL/CS nanocomposite as substrate (Figure 35). Capture and transduction was made by a sandwich assay between anti-PSA immobilized on the MWCNTs/IL/CS/AuNPs-PAMAM and anti-PSA labeled with HRP-labeled signaling antibodies. The electrocatalytic reduction of H\({}_{2}\)O\({}_{2}\) by HRP was monitored by DPV. The calibration curve for PSA was linear up to 80 ng mL\({}^{-1}\), with a LoD of 1 pg mL\({}^{-1}\). Figures of merit discussed in this section are summarized in Table 11. \begin{table}
\begin{tabular}{c c c c c} \hline
**Target** & **Probe** & **LoD** & **Method** & **Reference** \\ \hline CEA protein & Ab & 0.1 fg mL\({}^{-1}\) & IL/GR/AuNPs/Fe(CN)\({}_{3}\)\({}^{-4/-}\)/PPV & [171] \\ CEA protein & Ab & 10 pg mL\({}^{-1}\) & IL/RG/OP/B/DPV & [172] \\ Microcystin-LR & Ab & 0.04 fg mL\({}^{-1}\) & IL/GO/AuNPs/PPV/Fe(CN)\({}_{3}\)\({}^{-4/-}\)/DPV & [173] \\ Aflatoxin B1 & Ab & 0.3 fg mL\({}^{-1}\) & IL/GO/AuNPs/PPV/Fe(CN)\({}_{3}\)\({}^{-4/-}\)/EIS & [174] \\ hCG hormone & Ab & 0.4 \(\mu\)U mL\({}^{-1}\) & IL/PtNPs/Rutin/DPV & [175] \\ PSA protein & Ab & 1 pg mL\({}^{-1}\) & MWCNT/IL/CS/AuNPs/PAMAM/HRP & [176] \\ \hline \end{tabular}
\end{table} 
Table 11: Figures of merit of selected immunosensors using ionic liquids. Figure 35: **(A)** PSA immunosensor using AuNPs–PAMAM for signaling and MWCNT/IL/CS as substrate, and transduction with HRP and thionine as immobilized mediator; **(B)** CVs of the immunosensor after incubation with PSA from O up to 80 ng mL\({}^{-1}\) in PBS, pH 7 + 2.5 mM H\({}_{2}\)O\({}_{2}\). Insets, plots of the immunosensor response vs. PSA concentrations; **(C)** Nyquist curves for 2.5 mM [Fe(CN)\({}_{6}\)]\({}^{3/-4/-}\) in 0.1 M KCl recorded at anti-PSA/AuNPs-PAMAM-dendrimer/MWCNTs/IL/Chit/GC after incubation with 5, 10, 15, 20 and 25 ng mL\({}^{-1}\) PSA. Insets are plot of \(R_{\text{cl}}\) vs. PSA concentration and the equivalent circuit used to fit the experimental impedance data. On the right, Nyquist curves for the immunosensor in the presence of 15 ng mL\({}^{-1}\) PSA and in serum spiked with 15 ng mL\({}^{-1}\) PSA. Adapted from [176], Copyright 2014, with permission from Elsevier.
## 4 Application Testing and Analysis  
Two geohazard risk locations--the Zhangcungou slope of the Pillow Head Dam First-Level Hydropower Station and the Longtan slope of the Shaping Second-Level Hydropower Station in the Dadu River Basin--were selected for landslide risk assessment example validation, as shown in Figure 6.  
The Zhangcungou slope is located outside the camp of the Pillow Head Dam Grade 1 Hydropower Station. Landslides originate from the gully of the mountain behind the camp. The length of the upper and lower mountain gully is about 3100 m, and the difference in elevation between the upper and lower mountains is about 1200 m. A landslide disaster occurred in the rainy season of September 2022, blocking the culvert of Zhangcungou. The current mudslide silt may impact the interior of the camp, resulting in damage to the facilities and equipment.  
The Longtan landslide body is located upstream of Shaping Dam, approximately 12.5 km from the dam site. The foot of the slope is approximately 569 m above the surface elevation of provincial road S306. The gully serves as the boundary, with a width of about 180 m along the direction of the highway and a length of about 250 m along the slope. The average thickness of the cover layer is about 17.0 m, with a total cubic volume of approximately 65 to 90,000 m\({}^{3}\). In the rainy season of 2017, the slope experienced creeping and slipping, resulting in surface cracks and some damage to houses. Currently, the original cracks show no signs of expansion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Risk Assessment Models**} & \multicolumn{5}{c}{**Testing Accuracy**} \\ \cline{2-6}  & **Blanket** & **Attention Level** & **Warning Level** & **Alert Level** & **Alarm Level** \\ \hline Fuzzy-BLS & 92.08\% & 91.67\% & 88.33\% & 93.33\% & 95\% \\ BLS & 90.83\% & 95\% & 86.67\% & 90\% & 91.67\% \\ XGBoost & 88.33\% & 88.33\% & 88.33\% & 85\% & 91.67\% \\ SVM & 79.17\% & 78.33\% & 73.33\% & 81.67\% & 83.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of model training with different different models.  
Figure 6: Geographic location of the study area.  
On 27 August 2023, after on-site investigation and analysis, the slopes in Zhangcungou were classified as the warning level, while the slopes in Longtan were classified as the attention level. These classifications were made by integrating various factors such as geological structure, monitoring data, and potential risks.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Overview of the ODYSSE Protocol  
ODYSSErouters switch to the sleep state asynchronously and randomly. As a consequence, the wireless network topology is dynamic and unpredictable which makes the application of the classical reactive and proactive routing not practical. Thus, ODYSSE protocol adopts an opportunistic routing approach.  
Unlike classical routing schemes, opportunistic routing does not build a fixed routing table beforehand. However, forwarders (i.e., next hops) are selected on the fly. To keep low complexity forwarder search method, in ODYSSE, a forwarder is a neighbor offering the best compromise for the protocol adopted policy depending on its: (i) residual energy, (ii) geographic distance relative to the gateway, and (iii) quality of wireless links. To ensure routing progress towards the gateway and to avoid routing loops at each hop, a DAG (Directed Acyclic Graph) rooted at the gateway is implicitly constructed and maintained by each node as its \"distance\" relative to this gateway. This distance is essentially the number of hops separating the node from the gateway but taking into account the wireless link quality by exploiting the Received Signal Strength Indicator (RSSI) metric.  
The source node generates, in addition to the original data packets, repair (redundant) packets which are linear combinations of original ones. The repair packets are routed the same way as original packets. This data redundancy enables the final destination to decode the received packets in order to retrieve the lost ones. ODYSSE is a distributed protocol. As routes are computed on the fly, nodes do not need to store topological information as in classical routing protocols. ODYSSE is implemented and experimented on a large-scale experimental platform of Arduino devices under real radio conditions.  
In the following sections, we will detail the main operations of ODYSSE.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 6 EKF, UKF, and CKF
### Unified Algorithm of UKF and CKF
#### 6.2.1 Prediction  
The following steps describe the computation of the predicted state estimate \(\mathbf{\hat{x}}_{k|k-1}\) and predicted covariance \(\mathbf{P}_{k|k-1}\) at time \(t_{k}\).  
**Step 1**: Perform a Cholesky decomposition on \(\mathbf{P}_{k-1|k-1}\),  
\[\mathbf{P}_{k-1|k-1}=\mathbf{S}_{k-1|k-1}\mathbf{S^{\prime}}_{k-1|k-1}. \tag{65}\]  
**Step 2**: Compute the sample points  
\[\mathbf{X}_{i,k-1|k-1}=\mathbf{S}_{k-1|k-1}\mathbf{\bar{\xi}}_{i}+\mathbf{ \hat{x}}_{k-1|k-1},\;i=i_{0},...,m, \tag{66}\]  
where  
\[\mathbf{\bar{\xi}}_{0}=\mathbf{0}_{n\times 1},\quad\mathbf{\bar{\xi}}_{i}= \sqrt{\gamma}[\mathbf{I}_{n}\quad-\mathbf{I}_{n}]_{i}\quad i=i_{0},...,m. \tag{67}\]  
Therefore,  
\[\mathbf{S}_{k-1|k-1}\mathbf{\bar{\xi}}_{i}=\sqrt{\gamma}[\mathbf{S}_{k-1|k-1} \quad-\mathbf{S}_{k-1|k-1}]_{i},\;\;i=i_{0},...,m, \tag{68}\]  
where \([\mathbf{S}_{k-1|k-1}\quad-\mathbf{S}_{k-1|k-1}]_{i}\) is the \(i\)th column of the \(n\times m\) matrix \([\mathbf{S}_{k-1|k-1}\quad-\mathbf{S}_{k-1|k-1}]\).  
**Step 3**: Propagate the sample points from time \(t_{k-1}\) to time \(t_{k}\),  
\[\mathbf{X}_{i,k|k-1}^{*}=\mathbf{f}_{k}(\mathbf{X}_{i,k-1|k-1}),\quad i=i_{0},...,m. \tag{69}\]  
**Step 4**: Compute the predicted state estimate at time \(t_{k}\),  
\[\mathbf{\hat{x}}_{k|k-1}=\sum_{i=i_{0}}^{m}w_{i}\mathbf{X}_{i,k|k-1}^{*}. \tag{70}\]  
**Step 5**: Compute the predicted error covariance at time \(t_{k}\),  
\[\mathbf{P}_{k|k-1}=\sum_{i=i_{0}}^{m}w_{i}\mathbf{X}_{i,k|k-1}^{*}(\mathbf{X}_ {i,k|k-1}^{*})^{\prime}-\mathbf{\hat{x}}_{k|k-1}\mathbf{\hat{x}}_{k|k-1}^{ \prime}+\mathbf{Q}_{k-1}. \tag{71}\]
## 2 Geodetic Data Sets and Processing
### Levelling Data  
In addition to GPS data, levelling measurements (locations shown by the dark-blue dashed box in Figure 1A and the displacements in Figure 1D) were procured by CGS (MOEA) from 1998 to 2002 and converted from TWD67 to WGS84. They give more information about the vertical displacement, and are consistent with the GPS. Two of the six levelling lines are presented in Figure 3.
# Enhancing Unmanned Marine Vehicle Security: A Periodic Watermark-Based Detection of Replay Attacks
## 2 Problem Formulation and Preliminaries
### Replay Attack Model  
[PERSON] et al. (2018) initially defined a model for a replay attack. This model assumes that an attacker can capture a substantial amount of sensor data. During the attack, the real sensor measurement \(y(t_{1}+i)\) is obstructed and replaced by \(y(t_{1}+i)=y(t_{0}+i)\), which is forwarded to the estimator as demonstrated in Figure 4, with \(d=t_{1}-t_{0}\) identified as the replay delay. The model is expressed as:  
\[y(t_{1}+i)=y(t_{0}+i),0\leq i\leq T. \tag{11}\]  
**Remark 1**.: _In UMV systems, two prevalent types of replay attacks are commonly discussed: Stuxnet-like [PERSON] et al. [24] and time-delay attacks, which often target network-transmitted signals such as \(y(k)\) and \(r(k)\). Stuxnet-like attacks, which involve manipulated control inputs, present significant detection challenges due to their reliance on historical feedback signals. Time-delay attacks disrupt system performance by modifying the poles of the closed-loop system. This study specifically focuses on replay attacks that affect sensor signals \(y(k)\). Future research will investigate the simultaneous tampering of sensor data and control actions._  
**Remark 2**.: _Replay attack threats to UIMVs may come from state-sponsored groups, hacktivists, criminals, and insiders motivated by espionage, data theft, disruption, financial gain, and geopolitical leverage. The likelihood and success rate of replay attacks is contingent upon the security of the UIMVs system's transmission network--specifically, whether it employs advanced encryption technologies to protect its communications and data transmissions and the complexity of the attacker's replay attack methods. When UIMVs are fully deployed underwater, the inherent challenges of data transmission may reduce the success rate of such network attacks. Conversely, during docking operations or surface activities, UMV communications are more susceptible to the threat of replay attacks._  
**Assumption 1**.: _In this paper's discussion of the replay attack scenario, the attack phase and the subsequent attack segment are temporally distinct with no overlap._
## 3 Results
### Overall Results  
The overall results of the study are visualized in Figure 6. The participants of session 1 rated the user experience of each individual component and the overall system the highest. The UK evaluation of session 2 was lower, with the lowest values for session 3. Especially the rating of the robot-assisted health assessment was noticeably lower in session 3 than in sessions 1 and 2. Additionally, the system usability was rated as high in session 2 and average in session 3.  
Figure 6: The overall results of the study, including the individual values, means, and standard deviations for the KPIs of the individual components and the whole system, the system usability score, and the success (SR) and completion rates (CR), are visualized.  
Regarding the success rates of the conversation turns in the interaction with the social robot for the health assessment, small differences can be seen. The success rates in sessions 1 and 3 are similar at around 80%. The success rate in session 2 with the expert users is slightly higher at 86.23%. Large differences can be seen in the completion rates of the health assessment after the first session. While only five of nine assessments could be completed in session 1, all assessments were completed in sessions 2 and 3. Overall, the results indicate good user experience and usability of our health monitoring system.  
Additionally, the combined results of sessions 1 and 3 are shown in Figure 7. Minor changes, mostly of technical nature, were introduced into the system after session 1, as described in Section 2.4. Both sessions 1 and 3 were conducted with the target group of older adults; however, giving good indications of the rated UX of the system for this group. The combined KPIs of all system components, as well as the overall system, are rated very highly in sessions 1 and 3, with all values above 2.
# A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks
## 4 Lifetime-Extended Transmission Power Optimization Algorithm
### Constraint Conditions of Transmission Power  
The optimal transmission powers \(P_{\rm{S}}\) and \(P_{\rm{R_{i}}}\) should satisfy the constraint conditions in two aspects. On one hand, they are large enough to guarantee the SNR of the received signal satisfying a certain BER. On the other hand, they are within the range of the maximum transmission power \(P_{\rm{max}}\).  
According to [PERSON] theorem, to ensure cooperative nodes to receive data packets correctly, the transmission power of the sender should satisfy the following constraint condition:  
\[R\leq\frac{1}{2}{\rm{log}}_{2}\left(1+\frac{P_{\rm{S}}\|{H_{\rm{SR_{i}}}}\|^{ 2}}{N_{0}}\right),i\in[1,N] \tag{3}\]  
where \(R\) is the bandwidth utilization between S and D, \({h_{\rm{SR_{i}}}}\) is the channel gain between the sender and its cooperative node \(\rm{R_{i}}\), and \(N_{0}\) is the power of Gaussian white noise. In the cooperative transmission, each data packet transmission is composed of two-hop transmissions, i.e., from the sender to the cooperative node and then from the cooperative node to the recipient, and each one-hop transmission only occupies a half of the channel resource. Therefore, there is a factor \(1/2\) in Equation (3).  
Deduced from Equation (3), the received SNR of a cooperative node should satisfy the following condition:  
\[\frac{P_{\mathrm{S}}\parallel h_{\mathrm{SR_{i}}}\parallel^{2}}{N_{0}}\geq 2^{2R} -1,i\in[1,N] \tag{4}\]  
In the cooperative transmission, the recipient receives the signals from the sender and cooperative nodes, and decodes the signals by the MRC technique. Therefore, the transmission powers of the sender and cooperative nodes should satisfy the following condition:  
\[R\leq\frac{1}{2}\mathrm{log}_{2}\left(1+\frac{P_{\mathrm{S}}\parallel h_{ \mathrm{SD}}\parallel^{2}+\frac{\sum\limits_{i}P_{\mathrm{R_{i}}}\parallel h _{\mathrm{R_{i}}D}\parallel^{2}}}{N_{0}}\right) \tag{5}\]  
That is to say, the received SNR at the recipient should satisfy the condition:  
\[\frac{P_{\mathrm{S}}\parallel h_{\mathrm{SD}}\parallel^{2}+\frac{\sum\limits_ {i}P_{\mathrm{R_{i}}}\parallel h_{\mathrm{R_{i}}D}\parallel^{2}}}{N_{0}}\geq 2 ^{2R}-1,\ i\in[1,N] \tag{6}\]  
where \(h_{\mathrm{SD}}\) is the channel gain between the sender S and its recipient D, and \(h_{\mathrm{R_{i}}D}\) is the channel gain between the cooperative node \(\mathrm{R_{i}}\) and the recipient D.  
Meanwhile, all nodes in the network should adjust their transmission powers in range of the maximum transmission power as follows:  
\[0<P_{\mathrm{S}}\leq P_{\mathrm{max}}, \tag{7}\]  
\[0\leq P_{\mathrm{R_{i}}}\leq P_{\mathrm{max}}. \tag{8}\]  
The transmission power of the sender \(P_{\mathrm{s}}\) is always positive because the sender always needs to transmit data packets. However, the transmission power of a cooperative node \(P_{\mathrm{R_{i}}}\) can be zero. If the transmission power of a cooperative node is zero, this node does not forward the data packet to the recipient in the cooperative transmission.
## 6 Detection Methods: Optical Frequency Comb Spectroscopy
### Virtual Imaging Phased Array Spectroscopy  
VIPAS provides alternative approaches that circumvent this problem by directly measuring the power or phase of individual comb teeth that have interacted with the molecular gas [188]. In this case, a novel high-resolution crossed spectral disperser is employed to project the various frequency comb modes onto a two-dimensional digital camera. The distinguishing feature of the present approach is the use of a side-entrance etalon called a VIPA disperser in the visible spectral range. The multiple reflections within the VIPA etalon interfere such that the exiting beam has different frequencies emerging at different angles. Sensing with a broadband comb directly interrogates an absorbing sample, after which the spectrum is dispersed in two dimensions and sensed with detector arrays. Thus, the spectrometer transforms the one-dimensional comb into something more reminiscent of a two-dimensional 'brush'.  
With the VIPA method, [PERSON] et al. [189] resolved 2200 comb modes covering a 6.5 THz span with resolution 1.2 GHz, while [PERSON] et al. [190] resolved 4000 modes covering a 4 THz span with resolution 1 GHz. However, due to the limitations in optical coating and array detector technology, VIPA is available in the visible and NIR ranges. Proof-of-principle demonstrations have been carried out in the MIR wavelength region spectral resolution 600 MHz (0.02 cm\({}^{-1}\)) [191], and resolution 1 GHz (0.03 cm\({}^{-1}\)) [192]. MID APD arrays may change this situation in the near future.
## 3 Results
### Volatile Compounds Analysis by HS-SPME-GC/MS
koriensis_ (Figures 3 and 4). The 3-carene content (183.27 \(\upmu\)g/g, 54.7 times) of _P. koriensis_ was drastically enhanced at 20 days after PWN inoculation and showed the highest peak among the monoterpends (Figure 4). The retention time and fragmentation pattern of seven major compounds (\(\alpha\)-pinene, camphene, \(\beta\)-pinene, \(\beta\)-myrcene, 3-carene, \(\beta\)-phellandrene, and D-limonene) were identical to those of authentic standards (Supplementary Figure S1). Overall, the emission of volatile monoterpenes was increased until 20 days from the sampling period and decreased afterward (data not shown). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{_P. densiflora_} & \multicolumn{2}{c}{_P. koriensis_ (\(\upmu\)g/g)} \\ \cline{2-5}  & **Control** & **Infected Tree** & **Control** & **Infected Tree** \\ \hline \(\alpha\)-pinene & 134.47 \(\pm\) 16.77 & 269.33 \(\pm\) 24.14 & 10.14 \(\pm\) 1.28 & 171.84 \(\pm\) 55.02 \\ camphene & 22.97 \(\pm\) 10.53 & 18.16 \(\pm\) 0.48 & 2.52 \(\pm\) 0.05 & 27.86 \(\pm\) 4.06 \\ \(\beta\)-pinene & 48.40 \(\pm\) 8.17 & 141.83 \(\pm\) 30.78 & 3.84 \(\pm\) 1.50 & 51.37 \(\pm\) 20.57 \\ \(\beta\)-myrcene & 2.99 \(\pm\) 1.45 & 7.59 \(\pm\) 1.50 & 1.16 \(\pm\) 0.45 & 9.21 \(\pm\) 1.04 \\ \(3\)-carene & 0.79 \(\pm\) 0.09 & 7.65 \(\pm\) 2.63 & 3.34 \(\pm\) 0.07 & 183.27 \(\pm\) 48.99 \\ \(\beta\)-phellandrene & 24.83 \(\pm\) 12.09 & 95.14 \(\pm\) 26.64 & — & — \\ D-limonene & — & — & 1.27 \(\pm\) 0.50 & 36.76 \(\pm\) 12.45 \\ \hline \multicolumn{5}{l}{– indicates not detected.} \\ \end{tabular}
\end{table} 
Table 2: Content of major monoterpene in volatile compounds of control and pine wood nematode _Pinus desiflora_ and _P. koriensis_. Figure 4: HS-SPME/GC-MS analysis of volatile organic compounds in pine wood nematode (PWN)-infected and non-infected _Pinus koriensis_ plants: (**a**) GC chromatograms of volatile monoterpenes in control _P. koriensis_, (**b**) GC chromatograms of volatile monoterpenes in _P. koriensis_ after PWN infection, and (**c**) chromatograms of volatile monoterpenes authentic standards. 1. oxime-methoxy-phenyl, 2. tricyclene 3. \(\alpha\)-pinene, 4. camphene, 5 \(\beta\)-pinene, 6. \(\beta\)-myrcene, 7. 3-carene, 8. \(p\)-cymol, 10. D-limonene.
## 4 Simulation Results and Comparative
Another 45 nm RFSOI LNA is introduced in [10], based on a 3-stage cascode amplifier with baluns and switched capacitors to achieve multiband operation. The LNA presents a gain of 19.5 dB, a \(NF\) of 4.7 dB, but at the expense of a total power consumption of 59 mW and an area of 0.32 mm\({}^{2}\), thus lowering the value of the _FoM_ obtained with this solution. As shown in Table 2, the proposed LNA achieves the highest _FoM_ value (14.5) of all the solutions considered, since circuit performance is a result of an excellent trade-off between the gain, \(BW\), \(NF\), \(P_{DC}\) and area. Note that the gain of other contributions is higher at the expense of a significant increase in power consumption. The developed LNA presents the lowest power consumption and \(NF\) reported in a differential LNA, to the best of the authors' knowledge. In fact, the power consumption in this work is less than half the one used in [8], which reports the lowest power consumption and highest gain of all the state-of-the-art solutions considered. In addition, we reported a very low \(NF\), high gain, and high bandwidth in a very compact area. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Reference** & [7] & [8] & [9] & [10] & **This Work \({}^{*}\)** \\ \hline Technology & 65 nm CMOS & 65 nm CMOS & 45 nm SOI & 45 nm SOI & 45 nm SOI \\ Topology & 2-stage CAS & 2-stage CAS CG & 1-stage CAS & 3-stage CAS & 2-stage CS \\ BW\({}_{3 dB}\) [GHz] & 4 (19–23) & 2 (21–23) & 4.5 (25.5–30) & 8 (20–28) & 3.8 (20.3–24.1) \\ Gain [dB] & 18.5 & 25 & 9 & 19.5 & 12.9 \\ NF [dB] & 4.1 & 3.2 & 3.1 & 4.7 & 2.2 \\ IIP3 1 [dBm] & \(-4\) & - & 10 & - & \(-4\) \\ DC Supply [V] & 1.2 & 1.2 & 1.8 & 1 & 0.38 \\ Power [mW] & 67.2 & 25.2 & 34 & 59 & 11.7 \\ Area [mm\({}^{2}\)] & 0.19 & 0.2 & 0.11 & 0.32 & 0.15 \\ FoM & 1.71 & 6.48 & 9.63 & 3.26 & 14.5 \\ \hline \end{tabular}  
* Input Third Order Intercept. \({}^{*}\) Post-layout simulation results.  
\end{table} 
Table 2: Performance comparison of the designed LNA with state-of-the-art differential LNAs.