esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### DRS UV-Vis Analysis  
Both the DR UV-Vis spectra of all of the samples and the Tauc plot elaboration are shown in Figure 5. The calculation of the bandgap was obtained starting from the Tauc equation and implementing the Kubelka-Munk function. Both TiO\({}_{2}\) and WO\({}_{3}\), as explained, are semiconductors with an indirect transition. The values of the bandgaps can be obtained from the graph, plotting (F(Roe)hv)\({}^{\gamma}\) against the energy. The results obtained are reported in Table 1. The P25 and FSP-TiO\({}_{2}\) bandgaps were estimated, respectively, as 3.22 and 3.10 eV. The titanium dioxide and tungsten trioxide bandgaps obtained by FSP were in agreement with the literature, which attributes a value of 3.2 eV for the former [29] and 2.7 eV for the latter [30]. The bandgap of the TiO\({}_{2}\) FSP sample was slightly lower compared to that of the P25, which can be attributed to the higher rutile content. Anatase and rutile have bandgaps of, respectively, 3.2 eV and 3.0 eV [31], and the TiO\({}_{2}\) FSP bandgap of 3.10 eV is in line with the anatase/rutile composition that was calculated through XRD. All of these characteristics align with the higher flame temperature and longer residence time, which enhance the crystallinities of both the anatase and rutile phases while reducing defects. This is advantageous in photocatalysis, as a lower defect density improves charge separation, reduces recombination and, combined with a high surface area for reactant adsorption and efficient light exposure, enhances the photocatalytic performance.  
The bandgap of the mixed oxide is interesting because its value, 2.70 eV, was lower than that of both of the pure oxides synthetized, and resembles that of the pure WO\({}_{3}\). This behavior is typical of type-II heterojunctions, where a small reduction in the measured bandgap is observed by DRS. This means that better sunlight harvesting can be exploited by this substance: a lower bandgap can be translated into a wider range of wavelengths capable of being absorbed by the substance considered. Being that the sunlight spectrum shifted to visible light, rather than UV radiation, this improvement is relevant for photocatalyst application in real sunlight.  
Figure 4: N\({}_{2}\) physisorption curves of FSP catalysts.
# Sustainable Cross-Laminated Timber Structures in a Seismic Area: Overview and Future Trends
## 5 Modeling of Multi-Story Timber Buildings
### Vertical Walls  
The in-plane behavior of CLT multi-story shear-walls is generally modelled through 2D-shell elements, whose material elastic properties are calculated with one of the methods described in Section 4.1.1. Nevertheless, alternative approaches are available in literature: for instance a set of trusses (pinned in the corners) with a diagonal truss or springs simulating the in-plane strength and stiffness of the panels [66; 67]. With respect to 2D-shell models, truss elements cannot make visible the whole stress path in the panel but only the resultant of the internal forces in the elements, resulting in being less effective in the case of walls with openings. In fact, it has been proved that, in the case of perforated walls, a stress concentration in the corners at the interfaces between lintels and vertical panels can arise. Such stresses can be higher than the design strength values of material and lead to local brittle failure of the material in the corner region due to tension or compression [23; 50; 68].  
Furthermore, also in case of segmented walls having lintels connected by means of metal fasteners to the adjacent vertical panels, brittle failure modes due to stresses concentration in the corners can precede the ductile mechanisms of the connections [20].  
Another sophisticated model based on the use of 3D-solid elements which considers both thickness of the panels and layer orientation of the boards has been presented in [69].  
Particular attention should be paid in modeling of perforated walls, because they are affected by the lintels. As discussed in Section 3.1, the lintels may have a different lay-out depending on how the wall is conceived, as a monolithic or segmented. In the monolithic case, the number of effective vertical layers against flexure and shear forces is less than that in the vertical panels (Figure 10a). On the contrary, in the case of segmented walls the lintels can have the same number of effective layers of the vertical panels (Figure 10 b). In this latter case the lintels can be (i) less or more effectively fastened to the adjacent vertical panels by means of metallic fasteners or (ii) simply supported on adjacent vertical panels (without fasteners).  
The seismic behavior of CLT walls with opening is also affected by the relative stiffness of the mechanical connections at the base of the panels with respect to the in-plane stiffness of the wall (and the lintels). Such an issue has been investigated in [PERSON] et al. [70], where a kinematic model to analyze the effect of different hold-down configurations on the overall in-plane stiffness has been developed, and the results obtained compared with the experimental ones. They concluded that the degree of coupling decreases with increased hold-down stiffness and increases with wall segment width.
## 3 Results
### Implementation Details  
The classifier was pre-trained for 100 epochs to distinguish between original signature pairs and pairs comprising an original and a forged signature. The noise prediction module receives input images of size (128, 256, 3). The input images are halved in size through downsampling or upsampling at each transition block, with filters doubling in number. As shown in Figure 2, the input image is transformed into a feature vector of half the original size and with 64 filters at the first down-transition block. This process is repeated four times, resulting in a feature vector that is one-eighth of the original size and has 512 filters. After passing through the residual block and self-attention layer, the feature vector returns to the original image size through up-transition blocks. In the residual block and self-attention layer located at the model's center, the feature vector size remains unchanged. The feature vector size is maintained in all Conv2D blocks, with resizing occurring only in the downsampling or upsampling blocks. Additionally, all transition blocks require a time-class embedding, with the dense layer in the time-class embedding determining the vector size. In the proposed method, the output size of the dense layer was set to 512 for experimentation.  
As mentioned in Equation (12), MSE is measured as the loss for the noise prediction module, and the binary cross-entropy loss is measured for the auxiliary classifier. The total loss function is calculated as the sum of these two losses and optimized using adaptive moment estimation (Adam) [31]. Adam, which combines RMSprop and momentum techniques, is widely used in neural network training due to its ability to accelerate learning and reduce gradient oscillations, resulting in faster and smoother convergence. The noise prediction module was trained for 3000 epochs. The time step was set to 1000 to ensure fine-grained noise prediction performance. The parameters used in the noise prediction module are detailed in Table 1.
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 3 Fuzzy Clustering and Neuro-Fuzzy Modeling
### FCM (Fuzzy C-Means) Clustering Algorithm
Repeat phases()to()and decide optimized partition number, as well as \(c\) and \(m\) values. To determine the substructure of the compiled database and optimized input parameters, the success rate of any clusters and input parameters was evaluated after combining PCPT parameters, hydrostatic pressure, and total vertical stress, as shown in Table 3. First, the database was divided into 3-6 clusters to determine the optimized clusters of soil type according to combined parameters. Outputs were clay (CH, CL), silt (MH, ML), and sand (SM, SP, SP-SC, SW) for three clusters, clay (CH, CL), silt (MH, ML), sand with fine grained soil (SM, SP-SC), and relatively coarse sand (SP, SW) for four clusters, clay (CH, CL), silt with high liquid limit (MH), silt with low liquid limit (ML), sand with fine grained soil (SM, SP-SC), and coarse sand (SP, SW) for five clusters, and CH, CL, MH, ML, sand with fine grained soil (SM, SP-SC), and coarse sand (SP, SW) for six clusters. A total of 5 (input parameters) \(\times\) 4 (clusters) were considered. The success rates of FCM clustering were evaluated as presented in Table 3. When all data points were concentrated in a specific cluster and appropriate clustering was not possible, is the success rate was regarded as \"bad\". According to the results, the maximum success rate was 74% when \(q_{t}\), \(R_{f}\), and \(B_{q}\) were used as input parameters and three output clusters were selected. Furthermore, the m value representing the fuzziness of the partition was optimized, as given in Figure 5. Success rates were increased to \(m=4\) and seemed to converge after \(m=4\). Thus, \(m=4\) was adopted for this study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multicolumn{4}{c}{**Success Rate (\%)**} \\ \cline{2-5}  & **Three Clusters** & **Four Clusters** & **Five Clusters** & **Six Clusters** \\ \hline \(q_{r}\), \(f_{s}\), \(u_{kt}\) & 71 & 61 & 46 & Bad \\ \hline \(q_{r}\), \(R_{f}\), \(B_{q}\) & 74 & 60 & 48 & 42 \\ \hline \(q_{t}\), \(f_{s}\), \(\Delta u^{*}\) & 70 & 60 & 69 & 42 \\ \hline \(q_{t}\), \(f_{s}\), \(u_{kt}\), \(\sigma_{vv}\) & 71 & 53 & Bad & 52 \\ \hline \(q_{t}\), \(f_{s}\), \(u_{kt}\), \(R_{f}\), \(B_{q}\), \(\sigma_{vv}\) & 70 & 58 & 48 & Bad \\ \hline \(\Delta u=u_{kt}-u_{q}\). & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Success rate of FCM clustering for selected input parameters and specified clusters.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 3 The Limit Magnitude Detection Capability of the Orbital Optoelectric Tracking System Modeled
### Optical Signal Characterization of Noise
#### 3.3.2 Extreme Magnitude Detection Equivalent Model in Illuminated Conditions  
Under illuminated conditions, the photon number of the background noise equivalent is related to the sunlight incident direction. When the angle between the optical axis of the orbital optoelectronic tracking system and the sunlight incident direction is greater than 90 degrees, the system works with a backlight. Then, there is no influence of the background light on the noise, and the limit magnitude detection of the system is the same as that of the model under unilluminated conditions.  
Under illumination conditions, when the angle between the optical axis of the system and the sunlight incident direction is less than 90 degrees, the system is unavoidably affected by the background light. The background noise cannot be ignored, and it also needs to consider the angle \(\theta\) between the optical axis of the system and the sunlight incident direction. Therefore, it is necessary to study the relationship between the limit magnitude of the system and the exposure time, the signal-to-noise ratio threshold, and the sunlight incident direction under illumination conditions. The illuminance of the solar spectral radiation received by the system at the distance \(L\) from the center of the sun can be expressed as follows:  
\[E_{b}(\lambda)=R^{2}C_{1}/(\lambda^{5}L^{2}(e^{(C_{2}/\lambda T)}-1))\cos\theta \tag{26}\]  
where \(C_{1}=3.742\times 10^{-4}\) W \(\cdot\) um\({}^{2}\) and \(C_{2}=1.4388\times 10^{4}\) um \(\cdot\) K are the first and second radiation constants. \(T=5900\) K is the temperature of the equivalent radiation blackbody of the sun. \(R=6.9599\times 10^{8}\) m is the radius of the sun, and \(\theta\) is the angle between the normal of the sensing surface of the CCD optodetector and the incident light from the sun.  
Since the background radiation is a surface light source, take \(M_{b}=N_{x}\times N_{y}\). Then, the background radiation noise equivalent photon number for the solar spectral radiance illuminance is expressed as follows:  
\[n_{b}^{\prime}=(s\eta(\lambda/hc)E_{b}\tau_{0}t_{0}/M_{b})^{\frac{1}{2}} \tag{27}\]  
Then, \(N_{b}\) is transformed to \(N_{b}^{\prime}\).  
\[N_{b}^{\prime}=\frac{1}{4M}\cdot\pi D^{2}\cdot\eta\cdot\frac{\lambda^{*}}{hc} \cdot E_{b}\cdot\tau_{0}\cdot t_{0} \tag{28}\]  
The limit magnitude of the system under illumination conditions must meet Formula (29), which is a limit magnitude calculating model for the system under illumination conditions.  
\[m\leq 1.0857\cdot[-10.392-\lg(\frac{2 Mhc}{\lambda}\cdot\frac{K_{\text{min}^{2} }+\sqrt{K_{\text{min}^{4}}+4(N_{d}+N_{b}^{\prime})K_{\text{min}^{2}}}}{t_{0} \cdot\tau_{0}\cdot\eta\cdot\tau D^{2}})] \tag{29}\]
## 3 Results
### EEG Power Spectrum at Pre and Post Priming Intervention  
RM ANOVA results for each condition and frequency band are shown in Table 2. Log\({}_{10}\)-transformed EEG power spectra of theta, alpha, SMR, and beta frequency bands at pre- (Bin) and post- (Bout) priming intervention are shown in Figure 4.  
Looking at Table 2, the main findings in all the population were a significant decrease between Bin and Bout for the REST EC condition in alpha and beta and an increase in SMR, while for the REST EO a significant increase in theta, alpha, SMR, and beta. As for the NFT EO, a significant increase in theta and alpha was observed. A significant interaction \(T\times G\) (time \(\times\) group) was found for theta value on the REST EC task, while the value of theta in CG group decreases from Bin to Bout. In the EG group, this value is slightly increased. This last result can be confirmed in Figure 4, as well as the similar behaviour of the EG and CG between Bin and Bout. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**EG (\(n\) = 30)**} & \multicolumn{2}{c}{**CG (\(n\) = 30)**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{2-7}  & \(M\) & \(SD\) & \(M\) & \(SD\) & \(F\) & \(p\) \\ \hline
**Demographic** & & & & & & \\ Age (years) & 28.87 & 7.40 & 27.50 & 6.38 & 0.587 & ns \\ Gender (F/M) & 18/12 & & 19/11 & 0.00 \({}^{\text{a}}\) & ns \\ Education & 0/3/10/12/5 & & 0/8/8/11/3 & 5.01 \({}^{\text{b}}\) & ns \\
**(9/12/15/17/21)** & & & & & & \\ \hline
**Conditions** & & & & & & \\ ES (ES1/ES2) & 16/14 & & 15/15 & 0.0 \({}^{\text{a}}\) & ns \\ SS (RS/PS1/PS2) & 0/15/15 & & 30/0/0 & -\({}^{\text{c}}\) & -\({}^{\text{c}}\) \\ \hline
**Baseline Bands** & & & & & \\ theta (EC/EO) & \(-0.02/-0.16\) & 0.28/0.2 & 0.27/\(-0.05\) & 0.42/0.24 & 9.75/3.92 \(\ast\)/ns \\ alpha (EC/EO) & 0.52/\(-0.015\) & 0.61/0.47 & 0.82/0.19 & 0.56/0.45 & 3.90/3.04 & ns/ns \\ SMR (EC/EO) & \(-0.15/-0.35\) & 0.55/0.40 & \(-0.07/-0.28\) & 0.55/0.38 & 0.23/0.53 & ns/ns \\ beta (EC/EO) & \(-0.75/-0.94\) & 0.27/0.23 & \(-0.56/-0.79\) & 0.28/0.22 & 7.31/6.30 \(\ast\)/\(\ast\) \\ \hline \hline \end{tabular}
\begin{tabular}{c} EG, experimental group; CG, control group; \(M\), mean; \(SD\), standard deviation; EC, eyes closed; EO, eyes open; E, female; M, Male; ES, eyes sequence; ES1 and ES2, eyes sequence 1 and 2; SS, stimuli sequence; RS, REST sequence; PSI and PS2, PRIME sequence 1 and 2. Education level values refer to the number of participants (\(n\)) reporting the number of years completed in one of the following five-category: (1) \(n\geq 9\), ninth grade; (2) \(n\geq 12\), Secondary; (3) \(n\geq 15\), Bachelor’s degree; (4) \(n\geq 17\), Master’s degree; (5) \(n\geq 21\), Ph.D. Significant tests are marked with asterisks (\" \(p<0.05\), \"\(p<0.01\)). \({}^{\text{a}}\)\(\chi^{2}\) (\(df=1\)); \({}^{\text{b}}\)\(\chi^{2}\) (\(df=5\)). Stimuli sequence (SS) is intended to be different for the CG and the EG. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Group characteristics at preintervention (T0) (for \(n\) = 60).
## 3 Vibration Signal Preprocessing and Fault Feature Extraction
### GWO-VMD-Based Vibration Signal Decomposition  
#### 3.1.1 Basic Principle of VMD  
VMD is a popular TFA method proposed by [PERSON] et al. in 2014 [34]. For a non-stationary gearbox vibration signal \(v(t)\), VMD decomposes \(v(t)\) into a series of Intrinsic Mode Functions (IMFs), denoted as \(u_{k}(t)\), \(k\) = 1,..., \(K\), where \(K\) is the number of IMFs. Each \(u_{k}(t)\) has limited bandwidth around its frequency center. VMD iterates to update \(u_{k}(t)\) in the frequency domain and extracts the spectrum gravity center as its center frequency \(\omega_{k}\). According to References [34; 35; 36], VMD essentially solves a constrained variational model expressed with  
\[\underset{\{u_{k}\},\{\omega_{k}\},\{\omega_{k}\}}{\text{min}}\left\{\sum_{k=1 }^{K}\parallel\partial_{t}\bigg{[}\bigg{(}\delta(t)+\frac{j}{\pi t}\bigg{)}*u_ {k}(t)\bigg{]}e^{-j\omega_{k}t}\parallel\ 2\right\}\text{s.t.}\ \sum_{k=1}^{K}u_{k}(t)=v(t) \tag{1}\]  
where \(\{u_{k}\}\) = \(\{u_{1}\),..., \(u_{k}\),..., \(u_{K}\}\) is the set of the decomposed IMFs, \(\{\omega_{k}\}\) = \(\{\omega_{1}\),..., \(\omega_{k}\),..., \(\omega_{K}\}\) is the set of frequency centers of IMFs, \(\partial_{t}\) represents a partial derivative of time \(t\), \(\delta(t)\) is Dirac distribution, the symbol * stands for convolution operator, and \(\left\|\cdot\right\|_{2}\) represents Euclidian norm.  
To solve the above constrained variational model, a quadratic penalty factor \(\alpha\) and Lagrange multiplier \(\lambda\) are introduced to transform the constrained optimization problem expressed with Equation (1) to an unconstrained one as follows [34; 35; 36]:  
\[L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k=1}^{K}\parallel\partial_{t }\bigg{[}\bigg{(}\delta(t)+\frac{j}{\pi t}\bigg{)}*u_{k}(t)\bigg{]}e^{-j\omega _{k}t}\parallel\ 2\ +\parallel v(t)-\sum_{k=1}^{K}u_{k}(t)\parallel\ 2 +\left\langle\lambda(t),\ v(t)-\sum_{k=1}^{K}u_{k}(t)\right\rangle \tag{2}\]  
Figure 1: Workflow of vibration signal preprocessing and feature extraction.  
With Equation (2), the alternating direction method of multipliers is applied to update \(u_{k}\), \(\omega_{k}\), and \(\lambda\) iteratively. The updated equations are as follows [35].  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{\
u}(\omega)-\sum_{i\
eq k}\hat{u}_{i}( \omega)+\frac{\hat{\lambda}(\omega)}{2}}{1+2\alpha{(\omega-\omega_{k})}^{2}} \tag{3}\]  
\[\hat{\omega}_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega{\left|\hat{u}_{k}(\omega )\right|}^{2}d\omega}{\int_{0}^{\infty}{\left|\hat{u}_{k}(\omega)\right|}^{2} d\omega} \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega){=\hat{\lambda}^{n}(\omega)+\tau\left(\hat{\
u}( \omega)-\sum_{k=1}^{K}\hat{u}_{k}^{n+1}(\omega)\right)} \tag{5}\]  
where the symbol \(\wedge\) above the variables represents the updated values of \(u_{k}\), \(\omega_{k}\), \(\lambda\), and \(\upsilon\). Additionally, \(\tau\) is the updating parameter of Lagrange multiplier.  
More details concerning the implementation process of VMD can be found in References [34; 35; 36].
## 4 ML-Based Resource Allocation  
Wireless resource allocation techniques can be classified as traditional and learning-based algorithms. The above-formulated problem \(\mathcal{P}1\) is integer nonlinear programming and NP-hard with polynomial time complexity. To address such optimization problems by using conventional methods, the original problem is divided into multiple sub-problems and solved individually. In addition, exact environmental information, such as channel state information (CSI), is required for this method, which is not feasible in a dynamic wireless network. To solve the above problem \(\mathcal{P}1\), we propose a deep reinforcement learning (DRL) algorithm, which is a combination of reinforcement learning (RL) and deep learning (DL) techniques.  
In DRL, sequential decision-making is addressed by minimizing a reward function by interacting with the unknown environment in each layer of applications. Combining DNN with Q-learning, also known as DQN, improves the learning performance as well as speed significantly. The most recent advancement of DRL can achieve the near-optimal solution of a sophisticated network optimization problem without knowing the complete and exact network information. Interacting with the environment, a DRL agent can obtain the best policy locally with or without exchanging information with other agents which significantly reduces communication overheads. Hence, DRL can be highly effective in a large, complex, and dynamic environment like 5G and B5G networks.
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
## 3 Consensus on \(SO(4)^{n}\) with Reshaping
### Local Stability Analysis
In the case of the five-agent ring graph with a balanced \(p_{k}=1\) equilibrium where \(k=1\) or \(2\), \(H(R_{1}^{T}R_{2})\) is positive definite with spectrum \(\sigma(H(R_{1}^{T}R_{2}))=(0.62,1.3,2.0)\). Equal weights \(a_{ij}\) have been assumed; therefore, local stability can be concluded based on a single pair of connected agents. However, if the weights are not equal, an assessment of the spectrum of \(\Gamma(\mathcal{R})\) or \(W_{\Psi}^{*}(\mathcal{R})\) is required for a stability conclusion with different relative configurations between the agents. The extension to the reshaping case can now be applied by modifying the augmented weight matrices \(\mathcal{W}_{\Psi}^{*}(\mathcal{R})=diag(\dots\psi(R_{I}^{T}R_{i})\dots)\) and \(\mathcal{W}_{\Psi}^{*}(\mathcal{R})=diag(\dots\Psi(R_{j}^{T}R_{i})\dots)\). The stability of the consensus equilibrium \(R_{j}^{T}R_{i}=I_{4}\) depends on the critical point classification, which, in this case, is solely driven by the spectrum of \(\Psi(I_{4})\). In the case of the five-agent ring graph in the \(p_{k}=1\) balanced equilibrium with Case 1 reshaping, \(\sigma(\Psi(R_{j}^{T}R_{i}))=(-1.78,1.84,5.46)\), confirming that the non-consensus equilibria have been destabilized. For the nine-agent ring graph with Case 1 reshaping, a balanced \(p_{k}=1\) equilibrium is locally stable with \(\sigma(\Psi(R_{j}^{T}R_{i}))=(1.43,3.44,5.46)\). Case 2 reshaping results in an indefinite \(\Psi\) with \(\sigma(\Psi(R_{j}^{T}R_{i}))=(-1.04,3.37,7.78)\), thus yielding unstable balanced equilibria. Similarly, for the nine-agent ring lattice case with \(m=2\), the balanced \(p_{k}=1\) equilibrium for the Base case is locally stable with \(\sigma(H(R_{1}^{T}R_{2}))=(1.53,1.77,2.0)\) and \(\sigma(H(R_{1}^{T}R_{3}))=(0.35,1.17,2.0)\). For a ring lattice with \(m=2\), two pairs of agents must be assessed in order to determine stability via the spectrum of the Hessian for these pairs. Unlike the ring graph, the relative configurations for edges between consecutive nodes (e.g., agents 1 and 2) and edges between non-consecutive nodes (e.g., agents 1 and 3) will not be equal; hence, the corresponding \(\Psi\) matrices are not equal. Case 1 reshaping results in \(\sigma(\Psi(R_{1}^{T}R_{2}))=(1.43,3.44,5.46)\) and \(\sigma(\Psi(R_{1}^{T}R_{3}))=(-1.90,1.78,5.46)\), and thus Case 1 reshaping yields an unstable balanced equilibrium for a ring lattice with \(m=2\). Since \(b_{n}\) have been chosen such that \(b_{n}>0\), the consensus manifold is locally stable. Furthermore, since it is an isolated critical point, it is locally asymptotically stable. Equation (33) is a Morse-Bott-Lyapunov function for the same reasons as in the case of a single agent. If no other locally stable equilibria exist, then the consensus manifold is AGAS stable. As explained previously, reshaping can be used to enlarge the region of attraction of the consensus manifold and possibly render it AGAS stable.