esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 2 System Modeling and Definitions
### Performance Evaluation Metrics  
Based on the assumptions, models and problem formulation, we define the following performance metrics to study the impact of intruder's intelligence on the intrusion detection capability of a WSN for various applications.  
* Detection probability \(P_{det}\): the probability that an intruder is detected by a connected sensor in a WSN within the maximum allowable intrusion time \(T_{max}\).
* Prevention probability \(P_{prev}\): the probability that an intruder is not detected, however is prevented from reaching its destination within the maximum allowable intrusion time \(T_{max}\).
* WSN win probability \(P_{vsen}\): the sum of the detection probability \(P_{det}\) and the prevention probability \(P_{prevt}\), and \(P_{vsen}=P_{det}+P_{prevt}\).
* Escape probability \(P_{intru}\): the probability that an intruder manages to escape the WSN FoI and arrives at the destination within the allotted \(T_{max}\).  
In addition, we include the definitions for general WSN evaluation metrics including coverage, connectivity and connected coverage for the purpose of comparison [5]:  
* Coverage: Any point in the field of interest is said to be covered if it is within the sensing range of a sensor. The network coverage is defined as the probability of a random point in \(A\) being covered by at least one sensor.
* Connectivity: Consider two random sensors \(s_{i}\) and \(s_{j}\) located inside \(A\); \(s_{i}\) and \(s_{j}\) are connected if there exists a communication path between them. The network connectivity is defined as the probability of a random sensor that can communicate with the base station through a direct or multi-hop communication path, _i.e._, a connected sensor.
* Connected coverage: An intruder is considered to be detected when it enters the sensing coverage area of a connected sensor (to the BS). The network connected coverage is thus defined as the probability of a random point in \(A\) that is covered by at least one connected sensor.  
The notations used in this work are summarized in Table 1.
## 2 Results
### Identification of VOCs by HS-SPME-GC-Ms  
In this study, forty-two VOCs were identified through HS-SPME-GC-MS, including nineteen terpen, four alcohols, four esters, eleven ketones, one phenol, and three others, as summarized in Table 2. The results showed that l-menthone, (+)-pulegone, piperitone, menthofuran, verbenone, (+)-isomenthone, trans-carveyl acetate, and caryophyllene oxide were the main volatile compounds in all of the analyzed SS samples. Five substances passed the standard substance verification, as shown in Figure 2B, which was consistent with the identification results of the database. Surprisingly, cubebene was the volatile component only found in A and Z. Moreover, menthofuran was only found in the B and Z. The MetaboAnalyst 5.0 was utilized for heat map clustering analysis to better understand the variations between SS samples in different regions. Each variable is normalized to generate the clustering heat map of SS in different regions on the basis of row-scale, using the relative contents of the discovered 42 compositions as variables. The distribution frequency of each substance in the sample is shown by row comparison. As shown in Figure 2C, the main VOCs in SS are terpenoids. (+)-pulegone is a key active component of SS essential oil and has been determined to have anti-inflammatory properties [23]. Compared with samples in other regions, those originating from A exhibit higher levels of pulegone. Moreover, the contents of other terpenoids (8-13) in A are significantly higher than those detected in other samples. As shown in Figure 2C, according to the Euclidean distance, samples from the six regions could be divided into three categories: B and N, Z, G and S, and A. The A has the highest content of terpenoids. Unlike the other four groups of samples, the samples from B and A are rich in ketones and esters. The contents of VOCs have a low concentration in the three kinds of J, Z, and S, respectively.
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 3 Noise Distribution
### Ratio Distribution  
The equalized noise random variable can be written as  
\[\hat{w}_{k}=w_{k}/h_{k}, \tag{5}\]  
where subscript \(k\) represents the elementwise operation; hence, \(k\) is eliminated from subsequent operations to maintain simplicity. In addition, channel distribution is assumed to be zero mean, which means substituting \(\mu=0\) in Equation (4).  
Assuming uncorrelated noise and channel PDFs, the ratio distribution of Equation (5) can be calculated using [18]:  
\[p(\hat{w})=\int_{0}^{\infty}|h|p(h,w)dh, \tag{6}\]  
Figure 1: Transceiver of turbo-coded system model.  
The second approximation assumes the power of exponential \((n+1)^{2}\approx 2n+1\), and the simplified formula can be written as:  
\[p(\hat{w})=\frac{\exp(\sigma_{h}^{2}/2)\sigma_{w}^{2}}{2(\sigma_{w}^{2}+|\hat{w} |^{2}\exp(2\sigma_{h}^{2}))^{1.5}}. \tag{14}\]  
To verify the close match between the derived PDFs and the histogram plot, we used the Kolmogorov-Smirnov (KS) goodness-of-fit test [20; 21]. At a 5% significance level, the decision of the KS test for the first approximation was 0, implying that we cannot reject the null hypothesis that both vectors have the same PDFs. On the other hand, the KS test for the second approximation was 1, which implies that we must reject the null hypothesis, and the two PDFs are not the same.
## 2 Antenna Array Design  
### Wideband Dual-Dipole Antenna Element Design  
To design the antenna array, we first designed the antenna element. A printed J-shaped balun and two identical parallel half-wavelength dipoles formed the proposed element antenna. To boost gain and front-to-back ratio, a half-wavelength rectangular director was placed in front of the dual-dipole, as shown in Figure 1a.  
The proposed element was designed on a 0.8 mm-thick Roger 4003C substrate with a dielectric constant of 3.55 and a loss tangent of 0.0027, as shown in Figure 1a. The manufactured prototype of the antenna with a dimension of 100 mm \(\times\) 80 mm \(\times\) 0.87 mm (0.74A \(\times\) 0.59A \(\times\) 0.006A) is shown in Figure 1b. The top layer of the antenna consisted of three parts. The first part included two quarter-wave feeding lines with the length \(H_{d}\) and the width \(W_{g}\), separated by an air gap of length (\(H_{d}-w_{s}\)) and width \(W_{s}\), the first half-wave dipole of length \(L_{d}\) and width \(W_{d}\). The second part, which was employed for expanding bandwidth, is the extending length \(H_{e}\) of the feed line connected to the second dipole with the length \(L_{d}\) and the width \(W_{e}\). The third part was a director of length \(L\) and width \(w\) placed in parallel with the second printed dipole at a distance \(a\). The bottom layer was an adjusted integrated J-shaped balun that is evaluated in [24; 28]. The entire length of the J-shaped balun was \(H_{m}+W_{m}+L_{m}\).  
The first resonance at 1.85 GHz was created by optimizing the length of the two dipoles. Then, we increased the distance \(H_{e}\) between them, which created the second resonance as shown in Figure 2a. Then, to obtain the second resonance at 2.7 GHz, \(H_{e}\) was selected as 19 mm. The antenna impedance was mainly influenced by \(W_{s}\), \(H_{d}\), and \(H_{m}\). As demonstrated in Figure 2b, the placement of the feeding point \(H_{m}\) had a significant effect  
Figure 1: (**a**) The antenna element structure; (**b**) the antenna prototype.  
on the input impedance from 1.8 GHz to 2.7 GHz. The impedance of the antenna element was well matched with 50 \(\Omega\) of the SMA connector when \(H_{m}\) equals a quarter wavelength at 2.1 GHz. All of these dimensions were then optimized to obtain the desired bandwidth from 1.8 GHz to 2.6 GHz. The final antenna geometry after optimizing is shown in Table 1.  
Figure 2(a) shows the simulated and measured reflection coefficient of the antenna element. In simulation, the frequency bands were 1.73-2.81 GHz (with a bandwidth of 1080 MHz) and in measurement, 1.575 GHz to 2.875 GHz, fully encompassing the GSM 1800/4G, UMTS 2100/3G, LTE/NR 2600/4G/5G, and Wi-Fi. Unfortunately, the reflection coefficient was measured in the environment, not in an anechoic chamber, so the 3G and 4G in the environment will cause errors in measurement and simulation. Furthermore, the solder joints between the SMA and the antenna generate losses. They cause a difference between simulated and measured values. Figure 2(b) presents the simulated and measured antenna gain over the operating frequency.
# Study of the Ciprofloxacinacin Adsorption of Activated Carbon Prepared from Mangosteen Peel
## 3 Results and Discussion
### Isotherm Adsorption Model
#### 3.5.2 Freundlich Isotherm Adsorption Model  
The Freundlich isotherm is applicable to adsorption processes that occur on heterogenous surfaces. This isotherm gives an expression which defines the surface heterogeneity and the exponential distribution of active sites and their energies.  
The Freundlich isotherm adsorption equation has the form:  
\[\ln\mathrm{q}_{\rm e}=\ln\mathrm{K}_{\rm F}+\frac{1}{n}\cdot\ln\mathrm{C}_{\rm e} \tag{7}\]  
where K\({}_{\rm F}\) is the Freundlich adsorption constant and n is a constant that is always greater than 1.  
Figure 13A describes the CIP adsorption process on the ACMP according to the Freundlich isotherm model. The Freundlich adsorption constant was measured as K\({}_{\rm F}\) = 13.57 (mg/g) (L/mg) 1/n, with a constant value of n = 6.73. The correlation coefficient is R\({}^{2}\) = 0.9421. The large values of K\({}_{\rm F}\) and n indicate the good adsorption capacity of the material, and they show that there is an adsorption bond between the adsorbent and the adsorbate.  
Figure 12: The dependence of C\({}_{\rm cb}\)/q on C\({}_{\rm cb}\) to the CIP adsorption parameter value R\({}_{\rm L}\) (**A**) versus C\({}_{0}\) (**B**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Adsorbents** &
\begin{tabular}{c} **Adsorption** \\ **Capacity (mg/g)** \\ \end{tabular} & **Reference** \\ \hline
1 & Activated carbon from Lemma minor plant & 25.00 & [4] \\
2 & Nano Fe\({}_{3}\)O\({}_{4}\) & 24.00 & [5] \\
3 & Montmorillonite clay & 128.00 & [6] \\
4 & Potato stems and leaves & 23.36 & [10] \\
5 & Humic acid & 13.64 & [11] \\
6 & Bamboo charcoal & 36.02 & [12] \\
7 & Porous graphene–soy protein & 160.00 & [15] \\
8 & ACMP & 29.76 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 5: The maximum CIP adsorption capacity (q\({}_{\rm max}\)) of the ACMP and other adsorbent materials.
## 3 Simulation and Results
### Adding White-Gaussian Noise  
We created six sets of sine wave which is _i.i.d._\(N(0,\sigma^{2})\) for the frequencies in Hz of 1, 5 and 10 with the amplitude of 10% and 20% of the original datasets, at 100% random distribution. Those parameters were applied to the exchange rates data points \(x_{k}(t)\) in time series. The distribution of these added noise are similar to white-Gaussian noise as shown in Figure 6.  
Figure 5: Flowchart shows simulation and results of aIMF, WT, EKF and PF.  
Figure 6: Six sets of sine wave as frequencies in Hz of 1, 5 and 10 with the amplitude of 10% and 20% of exchange rates.  
For ease of presentation, Figure 6 shows the \(x\)-axis representing just 100 data points out of the total 2322 data points in the time series, whereas the \(y\)-axis is representing the amplitude. We introduced a variety of testing methods, namely; (a) Anderson-Darling, Lilliefors (Kolmogorov-Smirnov) and Pearson chi-square for nonlinear test; and (b) Augmented Dickey-Fuller and Elliott-Rothenberg-Stock for nonstationary test. Referring to Equation (23) \(x_{k}\) represented datasets, EUR-USD, exchange rates, in which their nonlinear and nonstationary characteristics were verified by all the testing methods; and \(w_{k}\) represented the six sets of sine wave created in Section 3.1. The equation which used to add white Gaussian noise to the original datasets can be rearranged as follows  
\[x_{kg}(t)=x_{k}\left(t\right)+\varepsilon_{kg}\left(t\right) \tag{43}\]  
where \(x_{kg}(t)\) is the original signal with the noise, \(x_{k}(t)\) is the original datasets and \(\varepsilon_{kg}\left(t\right)\) is the added noise with _i.i.d._\(N\)(0,1) at section of various frequencies in Hz of 1, 5 and 10 with 100% of random distribution of \(x_{k}(t)\). To verify that the Equation (43) is nonlinear equation, we tested the \(x_{kg}(t)\) with the different parameters of the noise added. As per the results, all of the \(p\)-value generated by all the testing methods mentioned earlier were less than 0.05. This served to imply that the characteristics of \(x_{kg}(t)\) was nonlinear and nonstationary. Later, \(x_{kg}(t)\) was used as input signal to estimate the performance of the following algorithms: (i) aIMF, (ii) WT, (iii) EKF and (iv) PF.
## 5 Results
### Comparative Analysis of Predictive Models  
In this study, we evaluated the performance of traditional machine-learning models, such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Gradient Boosting Decision Trees (GBDT), against advanced graph-based models like Graph Attention Networks (GAT), Multi-Scale Graph Attention Networks (MSGAT), and Multiview Multi-Scale Graph Attention Networks (MVMSGAT). The traditional models were optimized using grid search to find the best combination of parameters, ensuring optimal model performance. Furthermore, to assess the robustness and consistency of these models, we employed a five-fold cross-validation method. This approach divides the dataset into five parts, using one as the test set and the others for training in rotation, thus minimizing fluctuations in model performance across different data splits.  
We also explored advanced graph-based models such as GAT, MSGAT, and MVMSGAT. GAT introduces an attention mechanism to weight neighboring nodes' features, enabling the model to focus on key information. MSGAT expands upon this by incorporating multi-scale processing, capturing both local node features and overall graph structure. MVMSGAT further extends this approach by combining data from multiple views, capturing richer and more comprehensive graph structure features across multiple scales.  
In evaluating these models, we used metrics such as AUC-ROC, accuracy, F1 score, recall, and precision. These metrics helped us comprehensively assess the models' performance in predicting the effectiveness of neoadjuvant chemotherapy in bladder cancer patients. Through this comparison, we aimed to understand the relative strengths and limitations of traditional machine-learning models versus advanced graph-based models in handling complex medical data.  
Although RF and GBDT from the traditional models showed notable performance, it was the graph-based models, especially the MVMSGAT, that outshined the others. The MVMSGAT model recorded the highest AUC-ROC at \(0.8724\pm 0.0511\), accuracy of \(0.7789\pm 0.068\), F1 score of \(0.8529\pm 0.0338\), recall of \(0.9231\pm 0.0719\), and precision of \(0.8038\pm 0.0955\). This indicates that incorporating multiview biological prior knowledge  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **co-ex** & **ppi** \\ \hline Number of Nodes & 308 & 847 \\ Number of Edges & 1221 & 3186 \\ Average Number of Neighbors & 8.914 & 8.944 \\ Network Diameter & 8 & 13 \\ Network Radius & 4 & 7 \\ Characteristic Path Length & 3.305 & 3.668 \\ Clustering Coefficient & 0.404 & 0.25 \\ Network Density & 0.033 & 0.013 \\ Network Heterogeneity & 1.192 & 1.157 \\ Network Centralization & 0.215 & 0.127 \\ Connected Components & 18 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Topological statistics of the gene co-expression and protein–protein interaction networks.  
into the graph attention mechanism significantly enhances the accuracy of predicting bladder cancer treatment outcomes. For a comprehensive model comparison, see Table 4.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.1 Key Exchange (PQC KEM)  
The key exchange phase uses a post-quantum secure KEM, such as CRYSTALS-Kyber, standardized by NIST.  
Security Assumption  
The security of the key exchange depends on the computational infeasibility of solving the underlying hard mathematical problem (e.g., Module-LWE problem) for quantum adversaries. This is based on cryptanalysis conducted during the NIST PQC standardization process.
## 2 Principles of Local and Dual-Connected SSDI Techniques
### Dual-Connected SSDI
The vibration reduction can also be illustrated by comparing frequency-response curves with and without dual-connected SSDI, as shown in Figure 7. In addition, it's worth noting that the larger the voltage inversion factor, the better the damping effect. When \(\gamma=0.9\), the vibration reduction can be above 90%.
## 5 Multi-UAV Reconnaissance Proximal Policy Optimization Algorithm
### MURPPO Based on Branch Training
```
1:Input: Trajectories \(Tr\), mini-batch size \(M\), policy network \(\pi_{\theta_{i}}\), value network \(V_{\omega_{i}}\)
2:Output: Updated parameters \(\theta_{i}\) and \(\omega_{i}\) for the \(i\)-th UAV
3: Initialize actor learning rate \(\alpha\), critic learning rate \(\beta\)
4: Unpack trajectories \(Tr\) into states \(s_{i,t}\), actions \(a_{i,t}=(a_{i,t}^{\text{move}},a_{i,t}^{\text{scan}})\), old log probabilities \(\log\pi_{\theta_{i}}^{old}=(\log\pi_{\theta_{i},\text{move}}^{old},\log\pi_{ \theta_{i},\text{scan}}^{old})\), rewards \(r_{i,t}\), next states \(s_{i,t+1}\)
5:for\(j=1,\ldots,M\)do
6: Compute \(\pi_{\theta_{i},\text{move}}^{\text{move}}(a_{i,t}^{\text{move}}|s_{i,t})\), \(\pi_{\theta_{i},\text{scan}}^{new}(a_{i,t}^{\text{scan}}|s_{i,t})\) using \(\pi_{\theta_{i}}\)
7: Compute \(V(s_{i,t})\) and \(V(s_{i,t+1})\) using \(V_{\omega_{i}}\)
8: Compute advantages \(A_{i,t}^{\text{norm}}\) using Equation (22)
9: Compute surrogate losses \(I_{\text{move}}^{\text{CLIP}}(\theta_{i})\), \(I_{\text{scan}}^{\text{CLIP}}(\theta_{i})\) using Equations (23) and (24)
10: Compute policy loss \(L_{\text{total}}^{\text{CLIP}}(\theta_{i})\) using Equation (25)
11: Compute entropy bonus \(S[\pi_{\theta_{i}}](s_{t})\) using Equation (29)
12: Compute total policy loss \(L^{\text{PF}}(\theta_{i})=L_{\text{total}}^{\text{CLIP}}(\theta_{i})+0.01 \cdot S[\pi_{\theta_{i}}](s_{t})\)
13: Update policy network parameters: \(\theta_{i}\leftarrow\theta_{i}+\alpha\
abla_{\theta}L^{\text{PF}}(\theta_{i})\)
14: Compute value loss \(L^{\text{VF}}(\omega_{i})\) using Equation (17)
15: Update value network parameters: \(\omega_{i}\leftarrow\omega_{i}-\beta\
abla_{\omega}L^{\text{VF}}(\omega_{i})\)
16:endfor
```  
**Algorithm 2** MURPPO Algorithm Execution Process  
```
1:Input: system parameters
2:Output: localization time duration \(I(t)\)
3:Initialize:\(I(t)\gets 0\), \(Tr\leftarrow\emptyset\), \(t\gets T\)
4:while\(t>0\)do
5:for each UAV \(i\)do
6: Observe current state \(s_{i,t}\)
7: Select actions \(a_{i,t}=(a_{i,t}^{\text{move}},a_{i,t}^{\text{scan}})\) using \(\epsilon-greedy\) policy (26)
8: Compute log probabilities \(\log\pi_{\theta_{i}}^{old}=(\log\pi_{\theta_{i},\text{move}}^{old},\log\pi_{ \theta_{i},\text{scan}}^{old})\)
9: Measure RSSI and calculate distance \(d_{i}(t)\) using Equation (11)
10: Calculate \(r_{i,t}\) as the sum of immediate rewards and penalties
11:endfor
12:if\(|VM(t)|\geq 3\) and \(d_{i}(t)<d_{max}\), where \(VM(t)\) is defined in Equation (13) then
13: Execute three-point localization process
14: Update \(r_{i,t}\) by distribute localization rewards
15:\(I(t)\gets I(t)+1\)
16:endif
17:for each UAV \(i\)do
18: Observe next state \(s_{i,t+1}\)
19: Store \((s_{i,t},a_{i,t},\log\pi_{\theta_{i}}^{old},r_{i,t},s_{i,t+1})\) in trajectories \(Tr\)
20:endfor
21:if\(|Tr|=M\)then
22:\(\theta_{i},\omega_{i}\leftarrow\) UpdateParameters (\(Tr\), \(M\), \(\pi_{\theta_{i}}\), \(V_{\omega_{i}}\)) [See Algorithm 1]
23: Clear \(Tr\)
24:endif
25:\(t\gets t-1\)
26:endwhile
```  
**Algorithm 3** MURPPO Algorithm Execution Process
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 2 Isolated Power System Modeling
### Modeling of CI-RES Generation  
With respect to CI-RES modeling, a simplified grid-following model proposed in [30] was adopted both for wind and PV generators, which is based on a first-order transfer function approximation of the real dynamic response as well as the converter current limits. The model is endowed with fault ride through (FRT) capability, as shown in Figure 5. During normal operation conditions, \(P_{0}\) and \(Q_{0}\) correspond to the active and reactive power setpoints (a unitary power factor was assumed). However, for voltage dips lower than \(V_{th}\), the CI-RES generators will adjust their output power according to the active (\(I_{p}^{FRT}\)) and reactive (\(I_{q}^{FRT}\)) current requirements usually defined in grid codes. In this case, \(I_{q}^{FRT}\) is calculated proportionally to the terminal voltage deviation (a voltage dead-band of 0.15 p.u. was considered, with the \(I_{q}^{FRT}\) current setpoint being set to 1 when the terminal voltage reaches 0.5 p.u.). Complementarily, the active current is given by:  
\[I_{p}^{FRT}=\sqrt{I_{max}{}^{2}-I_{q}^{FRT}{}^{2}} \tag{5}\]  
where \(I_{max}\) corresponds to the CI-RES nominal current. The CI-RES output power is bounded to its maximum (\(P_{max}\)) and minimum (\(P_{min}\)) limits.  
Figure 4: Transient virtual impedance loop.  
Figure 5: Converter-interfaced renewable energy source power control model (adapted from [30]).
## 2 Materials and Methods
### Health-Related Quality
#### 2.5.3 Antioxidant Activity  
The antioxidant activity of the samples was evaluated in terms of the free radical scavenging activity by using the DPPH assay [25] and in terms of the capacity to reduce ferric ion (Fe\({}^{3+}\)) to ferrous iron (Fe\({}^{2+}\)) by the FRAP assay [26]. A BioTek Synergy HT multi-mode microplate reader with BioTek's Gen 5 TM software (BioTek Instruments Inc., Winooski, VT, USA) and 96-well microplates was used for the measurements. The working DPPH\({}^{\bullet}\) reagent was prepared by dissolving DPPH\({}^{\bullet}\) standard in methanol (1000 \(\upmu\)M), whereas the working FRAP reagent was made by mixing acetate buffer (0.3 M) at pH 3.6 with 2,4,6-tripyridyl-s-triazine (TPTZ) in HCl (40 mM) and FeCl\({}_{3}\)-6H\({}_{2}\)O (20 mM) at a ratio of 10:1:1. The same procedure was applied for both assays. A 290 \(\upmu\)L volume of the reagent (i.e., DPPH\({}^{\bullet}\) or FRAP) was added to 10 \(\upmu\)L of the sample in each well. Subsequently, the mixtures were incubated at 37 \({}^{\circ}\)C for 30 and 20 min in the dark, respectively. The absorbance was measured at 515 nm for the DPPH assay and at 593 nm for the FRAP assay. The values of absorbance provided by the DPPH\({}^{\bullet}\) or FRAP reagent solutions were used as a blank in each case. A Trolox standard curve was used to obtain the data, and all analyses were performed in duplicate. When necessary, extracts were diluted to be adapted to the linear range of the curve. Results were expressed as mg of Trolox equivalents per g of DW.
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 5 Experiments
### Performance Evaluation
#### 5.1.2 Precision (P)  
As shown in Equation (2), precision is the fraction of truly positive predicted levels to overall positive predicted levels. A low precision value indicates a high false-positive rate and vice versa.  
\[P=\frac{TP}{TP+FP}\times 100 \tag{2}\]