esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of the Loading-Unloading Contact of Two Cylindrical Rough Surfaces with Friction
## 3 Fractal Contact Model in the Unloading Process with Regard of Friction
### Model Comparison  
The classical KE model [7] and the Yuan model [39] are the available models for loading and unloading contact process and limited to planar surfaces.  
The proposed contact model can be used for the contact of planar surfaces supposing the radius of cylindrical surfaces is infinite.  
To ensure the credibility of the comparison, some parameters of KE model have been adopted include \(E/\sigma_{Y}=950\), \(\
u=0.3\), the plasticity index \(\psi=2\), \(H=2.8\sigma_{Y}\), the roughness parameter \(\beta=0.05\), and \(A^{*}=A/A_{tt}=0.0732\) (\(d_{tt}=0\) in KE statistical model). Further, fractal parameters are \(D=1.45\) and G \(=10^{-10}\) m.  
Figure 7 shows the comparison of the present model, KE model and Yuan model on load-area relation. The calculation results of the present model, KE model and Yuan model have the same trend which shows the feasibility of the model. In loading process, the dimensionless real contact area of present model is greater than that of KE model with \(F^{*}\leq 1.786\). In the unloading process, the dimensionless real contact area of present model is greater than that of KE model with \(F^{*}\leq 0.487\). Further, compared with the Yuan model, the present model is closer to KE model with the same fractal parameters.  
Figure 7: The comparison between the present model, the Kogut and Etsion (KE) model and the Yuan model.  
For further analyzing the correlation between the present model and KE model, the relation between the dimensionless contact area and dimensionless contact load is plotted in Figure 8 with \(G=10^{-10}\) m and \(1.4\leq D\leq 1.5\) in Figure 8a, \(D=1.45\) and \(10^{-11}\) m \(\leq G\leq 10^{-9}\) m in Figure 8b. The trend of the dimensionless contact area in loading and unloading process with different fractal parameters agrees with the KE model. The results of the two models are difficult to perfectly coincide with each other, as shown in Figure 8, as the statistical model adopts the Gauss distribution function of asperities height, and the fractal model adopts the size distribution function of asperities. Further, the fractal dimension \(D\) and the profile scaling parameter \(G\) are not involved in the statistical model, the coincidence of calculation results is affected to some extent. In conclusion, the calculation result of the present model has the same trend with the statistical result. When \(D=1.45\), \(G=10^{-10}\) m, the calculation result in the model proposed is close to that of KE model, which shows the validity of the model.
## 3 Results and Discussion  
### GC-MS Conditions  
From the outset, we initiated the optimization of instrumental conditions using a standard mixture (2.0 mg/L) comprising five monoterponoids (\(\alpha\)-pinene, \(\beta\)-pinene, limonene, 1,8-cineole, and thymol) and one sesquiterpenoid (caryophyllene oxide). These compounds, commonly found in the six Portuguese shrubs under study (_[PERSON] scoparia L._, _Cistus ladanifer_ L., _Cistus nonspeliensis_ L., _Lavandula stoechas_ L., _Thymus villosus_ L., and _Thymus camphoratus_), were selected based on several reports [4; 5; 8; 19]. Figure 2 illustrates the chemical structures of the target BVOCs. The standard mixture containing the six terpenoids underwent analysis by GC-MS using the SL injection mode and conventional instrumental conditions (see Section 2.3). The total ion chromatograms obtained exhibited adequate selectivity and sensitivity, with symmetrical peak shapes and resolution achieved for each compound within an acceptable analytical time (<12 min). Instrumental sensitivity was evaluated by determining the LOD and LOQ for each target compound, with signal-to-noise (S/N) ratios of 3:1 and 10:1, respectively. The values obtained were 0.20 and 0.50 mg/L for \(\alpha\)- and \(\beta\)-pinene, 0.05 and 0.20 mg/L for limonene and 1,8-cineole, 0.01 and 0.03 mg/L for thymol, and 0.10 and 0.30 mg/L for cryophyllene oxide, respectively. Subsequently, instrumental calibration was performed by determining linear regressions for each target analyte within a concentration ranging from 0.25 to 6.00 mg/L. Suitable linearity was achieved, with all \(r^{2}\) values exceeding 0.9831. Finally, instrumental precision was assessed by conducting six consecutive injections of the same standard mixture, yielding relative standard deviations (RSDs) of less than 11.2%.
## 3 The Proposed Method  
### Extracting the Parameters of Basic Footprints  
#### 3.1.1 Direct Linear Transformation (DLT)  
In robotic calligraphy, the handle's movements and changes in height will alter the shape of the footprint. Therefore, the brush footprints were scanned into images. The images were corrected for skew using the Direct Linear Transformation (DLT), which also ensures that all of the model character images and calligraphy scans are the same size (2000 \(\times\) 2000 pixels). The derivation of the DLT is shown below:  
Let (\(u\), \(v\)) and (\(x\), \(y\)) be the raw and corrected image coordinates, respectively. These coordinates are related by  
\[u = \frac{h_{1}x+h_{2}y+h_{3}}{h_{7}x+h_{8}y+1} \tag{1}\] \[v = \frac{h_{4}x+h_{5}y+h_{6}}{h_{7}x+h_{8}y+1}\]  
By using four reference points, (\(u_{1}\), \(v_{1}\)), (\(u_{2}\), \(v_{2}\)), (\(u_{3}\), \(v_{3}\)), and (\(u_{4}\), \(v_{4}\)) and their corresponding (\(x_{1}\), \(y_{1}\)), (\(x_{2}\), \(y_{2}\)), (\(x_{3}\), \(y_{3}\)), and (\(x_{4}\), \(y_{4}\)) coordinates, the eight unknowns in Equation (1), \(h_{1}\)-\(h_{8}\), may then be solved, which gives the relationship between (\(u\), \(v\)) and (\(x\), \(y\)).  
DLT was used to correct the skew in the scanned images. To perform the DLT, there must be four corresponding reference points in the raw image coordinates and corrected image coordinates. The raw image coordinates are (\(u_{1}\), \(v_{1}\)), (\(u_{2}\), \(v_{2}\)), (\(u_{3}\), \(v_{3}\)) and (\(u_{4}\), \(v_{4}\)), which correspond to (\(x_{1}\), \(y_{1}\)), (\(x_{2}\), \(y_{2}\)), (\(x_{3}\), \(y_{3}\)) and (\(x_{4}\), \(y_{4}\)) in the corrected image. As these image coordinates must be selected manually, the corrected image will be distorted in various ways if mistakes were made during the selection of these coordinates.  
Figure 6: Process flow for writing calligraphy using our system.
## 5 Performance Evaluation  
In this section, we first analyzed realistic performance of the Synergetic software-defined sampling spproach. Then, we analyzed the sampling and detection performance of Sonum from the aspects of missed detection ratio and storage requirements. Finally, we evaluated Sonum compared with ECMP and Hedera.  
We have implemented the functional modules of Sonum that is described above using ONOS, Mininet in Ubuntu 16.04. As shown in Figure 1, the simulated topology has four core switches, eight aggregation switches, eight edge switches and 16 servers. ONOS is a lightweight controller that have the enough processing power of acquisition, so we only deployed Sonum Sampling on edge switches A1 and A5 to detect long flow information in above topology. The detailed simulation parameters can be found in Table 3.  
```
0: Long flow set \(F\), Path set \(P\)
0: The mapping solution \(M\) between paths and long flows \(F,P\)
1:\(M=\oslash\);
2:while\(F\
eq\oslash\)do
3:for\(i=0\rightarrow(|F|-1)\)do
4: Obtain the source and destination address \(s,d\) of \(f_{i}\in F\);
5: Calculate the \(SRU_{p(s,d)_{i}}\) according to Equation (10);
6: Sort increasingly \(SRU_{p(s,d)_{i}}\) according to Equation (13) to 19 and save them to an array candidate;
7:if\(candidate\
eq\oslash\)then
8: Generate optional path \(p(f_{i})\) according to \(min(SRU_{p(s,d)_{i}})\);
9:\(F=F\backslash f_{i}\) and \(M=M\bigcup(f_{i},p(f_{i}))\);
10: Update link load \(ll(s,d)_{ij}\) and memory footprint space \(mf(s,d)_{ik}\) according to Equation (13) and Equation (14);
11:else
12: Return mapping failed;
13:endif
14:endfor
15: Generate route policy according to \(M\);
16:endwhile
```  
**Algorithm 2** Heuristic Scheduling Algorithm (HSA)
## 4 Results and Discussion
### Monocular and Binocular Camera Calibrations  
The checkerboard calibration method is used to calibrate the monocular camera and the binocular camera with a baseline distance of 2 m. The calibration board is a Zhang Zhengyou checkerboard with a grid side length of 28 mm and a black and white grid count of 10 \(\times\) 7. The reprojection error is obtained by calibration, and the points with a large error are selected for elimination. Finally, the pixel focal length of the monocular camera and the internal and external parameter matrices of the binocular camera are calculated. As shown in Figures 12 and 13, the reprojection errors of the monocular and binocular camera calibration meet the requirements.  
Figure 11: Test vehicle and camera arrangement.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{3}{c}{**Edge Computing Device: TW-T506S**} & \multicolumn{3}{c}{**System’s Algorithmic Environment**} \\ \hline Memory & 8 GB 128-bit LPDDR4x 51.2 GB/s & packaging & 24.0 \\  & & pillow & 10.3.0 \\ \hline Storage & 1\(\times\) 16 GB@MMC 5.1 & pip & 24.0 \\  & 1\(\times\) M.2 Key M NVMe 2280 & protobuf & 5.26.1 \\ \hline DL Accelerator & 2\(\times\) NVDLA Engines & pyreadline3 & 3.4.1 \\  & & setuptools & 68.2.2 \\ \hline Video output & 1\(\times\) HDMI 2.0 @ 4 Kp60 & sympy & 1.12 \\  & & wheel & 0.41.2 \\ \hline \end{tabular}
\end{table}
Table 5: _Cont_.  
Figure 12: The performance of monocular camera calibration errors.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology
### Communication Phase  
The autonomous devices attempt to connect to the local devices dynamically with the purpose of instructing or information sharing. Subsequently, in the edge network, the devices are not mandatorily required to possess an internet connection or otherwise connect by Bluetooth and can request to connect by authentication with a condition of maximum two challenge-response trials. The autonomous authentication process is initiated only if the public key of the sender and receiver are present within the public-key database. Therefore, absence of such a public key indicates updating the public-key database of the communicating parties and later confirms their legitimacy.
# Automated Personalized Loudness Control for Multi-Track Recordings
## 2 Materials and Methods
### Problem Formulation  
Music mixing, inherently a blend of art and science, necessitates a nuanced understanding of audio signal interplay. A musical piece, essentially a combination of various frequencies and timbres, can be deconstructed into its constituent tracks. This separation is key to our approach, as it allows for the manipulation of individual elements before reassembling them into a harmonious mixture. At the heart of our method, Equation (1) presents a mathematical model [24] representing a mixed song as a sum of its constituent tracks, each modulated by a specific coefficient:  
\[m(t)=\sum_{i=1}^{N}\lambda_{i}x_{i}(t), \tag{1}\]  
where \(m(t)\) denotes the mixed audio at time sample \(t\), \(x_{i}(t)\) symbolizes an individual track and \(\lambda_{i}\) is the corresponding mixing coefficient. \(N\) represents the maximum number of possible tracks, and is equal to 66 in our case. This model encapsulates the essence of mixing--blending various elements (tracks) into a cohesive auditory experience.  
When analyzing the mixing process from an optimization perspective, we view it as a search for the best combination of \(\lambda_{i}\) values that result in a balanced blend. The difficulty lies in automating this process of optimization, which has traditionally been carried out by human professionals. In our situation, the coefficient \(\lambda_{i}\) represents the level or loudness of each recording as perceived in the mix.
## 6 Computational Results and Discussion
### APL Systems
Additionally, the ratio of distances traveled by private vehicles is significantly reduced when introducing more APLs. For APL scenario A1 with 25 APLs, the ratio is 43.4% on average; for A4 with 150 APLs, the ratio is 23.6%; and for A6 with 500 APLs, the ratio is 12.6%. The variance with respect to the different demand scenarios is very small for all APL scenarios. The VKT for each APL configuration has been evaluated for the four demand scenarios H, S1, S2, and S3. Table A2 in appendix A provides detailed results. Figure 12 presents a comparison of VKT metrics for APL systems, including VKT for private vehicles and DD, with a value of C = 250. Our analysis clearly shows that in all scenarios the A1 configuration, featuring 25 APLs within the urban setting, exhibits the highest VKT. This can be attributed to the inadequacy of APLs for parcel pickup through more efficient means such as cycling or walking, which forces customers to cover longer distances using private vehicles. When assessing the A1 APL configuration, it becomes apparent that DD demonstrates remarkable efficiency gains, with improvements of 480%, 517%, 566%, and 611% for the Hermes, S1, S2, and S3 scenarios, respectively. Furthermore, across all additional configurations (A2, A3, A4, A5, and A6), DD consistently exhibits lower VKT in comparison to using APLs in conjunction  
Figure 11: Distances of customers to nearest APL for six scenarios. with private vehicles for parcel pickup. In Figure 13, we examine a comparison of VKT metrics for APL systems, maintaining a value of C = 500. The trends persist consistently throughout the analysis. Notably, it becomes evident that the maximum advantage peaking at 22% over DD can be observed up to the A6 configuration, which comprises 500 APLs. Figure 12: Comparison of VKT for APL systems (including VKT of private vehicles) and DD using a van per day with C = 250. Figure 13: Comparison of VKT for APL systems (including VKT of private vehicles) and DD using a van per day with C = 500.
# FGM: Noisy Label Learning via Fine-Grained Confidence Modeling
## 3 Proposed Method
### Generalization of Sample Clusters
Training on a cleaned dataset effectively prevents the model from over-fitting to false-labeled samples. Moreover, the cleaned training set \(\mathcal{T}\) can be expanded with more reusable samples gradually recalled from NHR and NHI by iteratively repeating the process to strengthen the performance of the trained model further. ```
Input \(\theta\), training dataset \(\mathcal{S}=(\mathcal{X},\mathcal{A}\mathcal{Y})=\{(x_{i},ay_{i})\}_{i=1}^{N}\) for\(k\) = 0 to MaxEpochdo
1 if\(k<\) WarmUpEpochthen
2 /*training on the whole \(\mathcal{S}^{*}\)/
3\(\theta=\)\(\mathit{WarmUp}(\mathcal{S},\theta)\)
4
5 else
6 /*find the maximum repeating pseudo labels*/
7\(\mathcal{M}\mathcal{Y}=\{my_{i}\}_{i=1}^{N}\gets max(\mathcal{H})\)
8 /*construct a joint confidence score*/
9\(jc_{i}^{c_{k-1}}=(\frac{\sum_{j=0}^{k-1}c_{i}^{c_{i}}(ay_{i})}{k},\frac{\sum _{j=0}^{k-1}c_{i}^{c_{i}}(my_{i})}{k})\)
10 /*generate clusters with GMM*/
11\(\mathcal{S},\mathcal{CS},\mathcal{CH},\mathcal{NHR},\mathcal{NHL}\gets GMM (jc_{i-1}^{c_{k-1}})\)
12 /* performing cluster refinements */
13\(\mathcal{X}_{\mathcal{N}\overline{\mathcal{S}}}=\{x_{ay_{i}\
eq my_{i}}\};x_{i} \in\mathcal{X}_{\mathcal{N}\overline{\mathcal{S}}}\)
14\(\mathcal{X}_{\overline{\mathcal{CS}}}=\{x_{ay_{i}\
eq my_{i}}\};x_{i}\in \mathcal{X}_{\mathcal{CS}}\)
15\(\mathcal{X}_{\overline{\mathcal{CH}}}=\{x_{ay_{i}\
eq my_{i}}\};x_{i}\in \mathcal{X}_{\mathcal{CH}}\)
16 /*constructing the new training set \(\mathcal{T}^{*}\)/
17 if\(k==\) WarmUpEpochthen
18\(\mathcal{T}=(\mathcal{X}_{\overline{\mathcal{N}}\overline{\mathcal{S}}}, \mathcal{M}\mathcal{Y}_{\overline{\mathcal{N}}\overline{\mathcal{S}}})\cup( \mathcal{X}_{\overline{\mathcal{CS}}^{*}},\mathcal{A}\mathcal{V}_{\overline{ \mathcal{CS}}})\cup(\mathcal{X}_{\overline{\mathcal{CH}}^{*}},\mathcal{A} \mathcal{V}_{\overline{\mathcal{CH}}})\)
19 else
20\(\mathcal{T}=\mathcal{T}\cup(\mathcal{X}_{\overline{\mathcal{N}}\overline{ \mathcal{S}}^{*}},\mathcal{M}\mathcal{Y}_{\overline{\mathcal{N}}\overline{ \mathcal{S}}})\cup(\mathcal{X}_{\overline{\mathcal{CH}}^{*}},\mathcal{A} \mathcal{V}_{\overline{\mathcal{CH}}})\)
21
22 end if
23\(\theta=\)\(\mathit{Train}(\mathcal{T},\theta)\)
24
25 end for
26 /*record history confidence and prediction*/
27\(\mathcal{C}^{\kappa}_{k}=\{\mathcal{P}_{model}(x_{i};\theta)\}_{i=1}^{N}\)
28\(\mathcal{H}^{\kappa}_{k}=\{\arg\max\mathcal{C}^{\kappa}_{i}\}_{i=1}^{N}\)
29 end for
```  
**Algorithm 1**Generalization of Sample Clusters and Cluster Refinements  
Figure 2: Sample clusters generated by FGCM. (**a**) is generated using confidence score of ground truth labels and annotation labels. (**b**) is generated using confidence score of maximum repeating pseudo labels and annotation labels.
# Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning
## 3 Servo System Control Strategy Based on Reinforcement Learning
### Design Scheme of Reinforcement Learning Agent
#### 3.3.2 Network Design for Critic  
Two four-layer neural networks are established on the basis of the DQN algorithm. They are the critic evaluation and target networks, which have the same structures but different functions. The structure of the critic network is shown below:  
As shown in Figure 6, the critic evaluation network has exactly the same number of neurons and structure as those of the critic target network. The input layer of the critic evaluation network has seven neurons, which correspond to seven input nodes, namely, for the angular velocity \(X(t-1)\) at the last moment, the output error value \(e(t-1)\) of the previous moment, the angular velocity \(X(t)\) at the current moment, the output error value \(e(t)\) at the current time, the signal \(H(t+1)\) needed to track for the next moment, the signal \(H(t+2)\) to track after two times, and the output value \(\mu(s_{t}|\theta^{\mu})\) of the actor evaluation network. The output layer contains a neuron, which outputs the action-value function \(Q\big{(}s_{t},\mu(s_{t}|\theta^{\mu})|\theta^{Q}\big{)}\). \([X(t-1),e(t-1),X(t),e(t),H(t+1),H(t+2)]\) is a set of reinforcement learning agent state \(s_{t}\). The critic target network also has seven nodes, namely, for the angular velocity \(X(t)\) at the current moment, the output error value \(e(t)\) at the current time, the angular velocity \(X(t+1)\) at the next moment after the interaction between action \(\mu(s_{t}|\theta^{\mu})\) and the environment, the output error value \(e(t+1)\) at the next moment, the signal \(H(t+2)\) to track after two times, the signal \(H(t+3)\) to track after three times, and the output value \(\mu\Big{(}s_{t+1}|\theta^{\mu-}\Big{)}\) of the actor target network. The critic target network outputs the state-action value function \(Q\Big{(}s_{t+1},\mu\Big{(}s_{t+1}|\theta^{\mu-}\Big{)}|\theta^{Q-}\Big{)}\). \([X(t),e(t),X(t+1),e(t+1),H(t+2),H(t+3)]\) is a set of states \(s_{t+1}\) at the next moment. The critic network contains three hidden layers. The first hidden layer comprises 200 neurons, and its activation function is ReLu6.The second hidden layer also consists of 200 neurons, and its activation function is ReLu6. The third hidden layer is composed of 10 neural networks, and its activation function is ReLu. L2 regularization is adopted to prevent overfitting of the neural network.  
Figure 5: Structure diagram of the actor network.
## 3 Forecasting Approach  
In this study, a three-step procedure based on a SOM-SARIMA-SVR model is proposed in order to predict the daily number of containers passing through a sanitary facility of a seaport. A further objective is to compare the prediction results of the proposed three-step procedure with the obtained using other possible methodologies, such as the SVR models in a single way, the hybrid SARIMA-SVR model and the combined SOM-SVR model. The proposed approach combines the capability of SARIMA models in capturing the linear patterns and the ability of SVR in modelling nonlinear patterns, together with the advantages of the SOM clustering algorithm.  
The experimental database comes from the BIP of the Port of Algeciras Bay, located in the South of Spain. The BIP of Algeciras is the approved facility where import goods are inspected and checked before entering the Community territory of the European Union. The Port of Algeciras Bay was the first port in the Mediterranean Sea and the fourth port in the European continent related to the total throughput in 2019 (109.4 million tons) only surpassed by the ports of Rotterdam (The Netherlands), Antwerp (Belgium) and Hamburg (Germany). The database was provided by the Port Authority and contains daily records of the number of containers at the Algeciras BIP from 2010 to 2014.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Experimental Results and Analysis  
The ODGNet is trained and tested on the Cornell dataset with RGB-D as the input, and the results of this network for grasp detection on the Cornell dataset are shown in Figure 8. In Figure 8, from top to bottom, each row is, in turn, the TOP-3 predicted grasping rectangular boxes in the color image, the TOP-3 predicted grasping rectangular boxes in the depth image, the grasping quality map \(Q\), the grasping angle map \(\Phi\), and the grasping width map \(W\).  
TOP-3 predicted grasping refers to finding three local extreme points from the grasping quality map \(Q\). And then, three grasping rectangular boxes are generated based on these three points, the grasping angle map, and the grasping width map. Since some of the grasping quality maps only have two extreme points, only two grasping rectangular boxes are generated.  
As can be seen from the grasp quality map in Figure 8, the grasping region is well predicted and the grasp quality score of the background is almost 0, indicating that ODGNet can separate the grasping region from the background well. From the predicted grasping rectangular boxes for each object, it is clear that our method exhibits excellent grasping adaptability for all shapes of objects.  
Figure 8: Grasp detection results with the Cornell dataset.
# Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm
## Methodology
### Geometric Model of a Single-Curvature Arch Dam
#### 2.1.1 Arch Crown Cantilever  
As shown in Figure 1a, if the upstream face curve \(\mathrm{y}_{cu}\) of the arch crown cantilever and the thickness \(B\) of the arch crown cantilever are determined, the downstream surface curve \(\mathrm{y}_{cd}\) can be determined, and the geometric structure of the arch crown cantilever can thus be obtained. The upstream surface curve \(\mathrm{y}_{cu}\) is a linear function of the \(z\)-axis, which can be expressed as follows:  
\[y_{cu}(z)=0. \tag{1}\]  
The thickness \(B\) is modeled as a cubic function of the \(z\)-axis and can be expressed as follows:  
\[B(z)=b_{0}+b_{1}z+b_{2}z^{2}+b_{3}z^{3} \tag{2}\]where \(b_{0}\), \(b_{1}\), \(b_{2}\), and \(b_{3}\) can be obtained by substituting four groups of control variables--\(z_{1}=0\), \(B_{1}=B_{c}\); \(z_{2}=H/3\), \(B_{2}=\alpha B_{b}\); \(z_{3}=2H/3\), \(B_{3}=\beta B_{b}\); and \(z_{4}=H\), \(B_{4}=B_{b}\)--into Equation (2). \(z_{i}\) (\(i=1,\,2,\,3,\,4\)) is the distance extending vertically downward from the crest of the dam, and \(B_{i}\) (\(i=1,\,2,\,3,\,4\)) is the thickness of the arch crown cantilever at \(z_{i}\). \(B_{c}\) and \(B_{b}\) are the crest and bottom widths of the arch crown cantilever, respectively, and \(\alpha\) and \(\beta\) are coefficients. \(H\) is the maximum height of the arch dam. Therefore, the curve \(y_{cd}\) of the downstream surface of the arch crown cantilever can be obtained using Equations (1) and (2).  
\[y_{cd}(z)=y_{cu}(z)+B(z)=b_{0}+b_{1}z+b_{2}z^{2}+b_{3}z^{3}. \tag{3}\]