esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 HTOPSIS for Solving the HMADM Model with Deviation and Fixation Attributes
### New Normalization Approach for Heterogeneous Attributes
#### 3.1.3 Normalization of the Attribute Described as TFN Based on Relative Preference Relation  
For a benefit or cost attribute described as TFN \(\widetilde{f}_{ij}=(f_{ij^{\prime}}^{L}f_{ij}^{M},f_{ij}^{U})\), it holds  
\[r_{ij}=(r_{ij}^{L},r_{ij}^{M},r_{ij}^{U})=\bigg{(}\frac{f_{ij}^{L}}{c_{j}^{*}}, \frac{f_{ij}^{M}}{c_{j}^{*}},\frac{f_{ij}^{U}}{c_{j}^{*}}\bigg{)}, \tag{8}\]  
where \(c_{j}^{*}=\sqrt{\sum\limits_{i=1}^{m}\frac{1}{3}\Big{(}f_{ij}^{L2}+f_{ij}^{M2 }+f_{ij}^{U2}\Big{)}}\).  
Supposing that the value of a deviation attribute is described as TFN \(\widetilde{f}_{ij}=(f_{ij^{\prime}}^{L},f_{ij}^{M},f_{ij}^{U})\) and the given TFN to be deviated from is \(\widetilde{k}_{j}=(k_{j}^{L},k_{j}^{M},k_{j}^{U})\), the attribute value is \(\widetilde{f}_{ij}^{\prime}=(f_{ij}^{L}-k_{j}^{U},f_{ij}^{M}-k_{j}^{M},f_{ij} ^{U}-k_{j}^{L})\) when the deviation attribute is transformed into the benefit one with zero NIS. According to the relative preference relation between two TFNs [26], the preference degree of TFN \(\widetilde{f}_{ij}^{\prime}\) over 0 can be calculated as  
\[\mu_{P}(\widetilde{f}_{ij}^{\prime},0)=\frac{1}{2}\bigg{(}\frac{f_{ij}^{L}-k_ {j}^{U}+2(f_{ij}^{M}-k_{j}^{M})+f_{ij}^{U}-k_{j}^{L}}{2\|T\|}+1\bigg{)}, \tag{9}\]  
where \(\|T\|=\left\{\begin{array}{ll}\frac{(l^{L*}-t^{U}-)+2(t^{M*}-t^{M-})+(t^{L*} -t^{L-})}{(t^{L*}-t^{U}-)+2(t^{M*}-t^{L-})}+(t^{U*}-t^{L-})},\;t^{L*}<t^{U-}. \end{array}\right.\)  
\[\begin{array}{ll}t^{L*}=\max(f_{ij}^{L}-k_{j}^{U},0);\;t^{M*}=\max(f_{ij}^{ M}-k_{j}^{M},0);\;t^{U*}=\max(f_{ij}^{U}-k_{j}^{L},0);\\ t^{L-}=\min(f_{ij}^{L}-k_{j}^{U},0);\;t^{M-}=\min(f_{ij}^{M}-k_{j}^{M},0);\;t^ {U-}=\min(f_{ij}^{U}-k_{j}^{L},0).\end{array}\]  
With the attribute values satisfying \(\mu_{P}(\widetilde{f}_{ij}^{\prime},0)<0.5\) symmetrically mapped about 0, the equivalent attribute values of all the alternatives can be described as  
\[\widetilde{f}_{ij}^{\prime}=\begin{cases}(k_{j}^{L}-f_{ij}^{U},k_{j}^{M}-f_{ ij}^{M},k_{j}^{U}-f_{ij}^{L}),&\mu_{P}(\widetilde{f}_{ij}^{\prime},0)<0.5;\\ (f_{ij}^{L}-k_{j}^{U},f_{ij}^{M}-k_{j}^{M},f_{ij}^{U}-k_{j}^{L}),&\mu_{P}( \widetilde{f}_{ij}^{\prime},0)\geq 0.5.\end{cases} \tag{10}\]  
Finally, the normalized attribute value \(r_{ij}\) can be calculated as  
\[r_{ij}=\left(r_{ij}^{L},r_{ij}^{M},r_{ij}^{U}\right)=\left\{\begin{array}{ ll}\left(\frac{k_{j}^{L}-f_{ij}^{U}}{c_{j}^{*}},\frac{k_{j}^{M}-f_{ij}^{M}}{c_{j}^{*}} \frac{k_{j}^{U}-f_{ij}^{L}}{c_{j}^{*}}\right),\;\mu_{P}(\widetilde{f}_{ij}^{ \prime},0)<0.5;\\ \left(\frac{f_{ij}^{L}-k_{j}^{U}}{c_{j}^{*}},\frac{f_{ij}^{M}-k_{j}^{M}}{c_{j}^ {*}},\frac{f_{ij}^{U}-k_{j}^{L}}{c_{j}^{*}}\right),\;\mu_{P}(\widetilde{f}_{ij} ^{\prime},0)\geq 0.5.\end{array}\right. \tag{11}\]  
where \(c_{j}^{*}=\sqrt{\sum\limits_{i=1}^{m}\frac{1}{3}\Big{[}\big{(}f_{ij}^{L}-k_{j} ^{U}\big{)}^{2}+\big{(}f_{ij}^{M}-k_{j}^{M}\big{)}^{2}+\big{(}f_{ij}^{U}-k_{j} ^{L}\big{)}^{2}\Big{]}}\).
## 5 Performance Measures of PAS
### Sensitivity Analysis of PAS Reliability  
In order to identify the critical components of a PAS, the authors performed a sensitivity analysis for the system's reliability. Sensitivity analysis is a method that indicates the effects of different failures on the reliability of a system. The following Table 5 and corresponding Figure 7 indicate the impact of different failure rates on the PAS's reliability.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Fundamental Concept of the Energy-Efficiency
#### 3.3.2 Energy Consumption Model for WSN  
Based on the cost values presented in [9] and [13] Chapters 3 and 7, of a multi-Hop network with the transport scheme Hop-by-Hop, according to Lemma 4, the energy consumed by a network responsible for the transport of the control signal is given by  
\[\mathcal{E}(E_{T}(p,L,N))\ =\Xi\times\frac{(E_{TX}+2E_{SW}+pE_{RX})\, \mathcal{E}(M)}{T_{S}} \tag{17}\]  
where \(T_{S}\), called _sampling time_, is the time interval used to transmit measured and control signals in a digital control system. The value of \(\Xi\) is dimensionless, and it is used to adjust the amount of time. The proof of Equation (17) is fully discussed in [9; 13] for Bernoulli loss processes.  
Modeling of the Markov Modes and Transition Probability Matrix for a Network with Multiple Information Sources  
This section provides the definition of a control structure through WSN based on the client-server control architecture. Additionally, this section presents the modeling of the transition probability matrix and the operation modes for output-feedback control design in semi-reliable communication networks with multiple sources of information.
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 5 Conclusions  
In this study, a cascading vector tracking loop for highly dynamic environments was designed and verified. First, two-stage filters were used to reduce the strong nonlinearity and the computation cost. Second, the original observations were re-organized to eliminate the impact of unknown parameters on the I-Q values. Finally, this paper proposes a feasible feedback method, which combines with pre-filter estimation, and the main filter ensures fast and stable loop convergence from both phase and frequency.  
Testing with the high dynamic signals generated by navigation signal generators, the results of this vector loop show that the high tracking accuracy is the same as that in the static environment. The vector carrier loop assisting code loop in this study can work normally at highly dynamic conditions in which the FLL-PLL loop cannot track stably. In the test of acceleration (i.e., \(-\)100 g), the convergence time of this cascading vector loop can be decreased to approximately 100 ms, and this convergence time has been shown to have no obvious relationship with dynamic change through comparing with the static test. After stable loop tracking, the STD of the carrier phase estimation is below 0.6 mm, and this STD is only 20% that of the FLL-PLL with a large noise bandwidth. That is, the proposed vector structure is low in complexity and can be applied to highly dynamic environments.  
Conceptualization, Z.T.; methodology, Z.T.; software, Z.T.; validation, Z.T. and W.G.; formal analysis, Z.T. and W.G.; investigation, Z.T. and W.S.; resources, Y.W., Y.L. and Y.W.; data curation, Z.T. and Y.L.; writing--original draft preparation, Z.T.; writing--review and editing, W.G., Y.L. and Y.W.; visualization, Z.T.; supervision, Y.L.; project administration, Y.L.; funding acquisition, W.S. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Joint Foundation for Ministry of Education of China, grant number 6141A02011907, the National Key Research and Development Project of China, grant number 2017 YFB0503401-01, and the Joint Foundation for Ministry of Education of China, grant number MCM20200J01.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to project regulations.  
**Acknowledgments:** We thank the China Academy of Launch Vehicle Technology and the Beijing Hoyatejo Co., Ltd. for the data and equipment supports.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Smart Breaker in Smart Grid  
In a smart electrical distribution network, all probable fault locations [6] which can occur in the network were listed in the CC and the SB was designed for online monitoring. The observed electrical network can use the data from the Supervisory Control and Data Acquisition (SCADA) or WAMS. Then, the automatic breakers will isolate the faulty line (during fault occurrence) till the network reconfiguration. The SB sends data about its status to CC, while the CC send commands to the remote switches to connect or disconnect (on or off) [28]. The reconfiguration allows to handover the demand on other lines that have lower loads [29]. The action of the SBs is an automatically process from the CC, which permits the SG to implement the strategies for the continuous process. The CC has fault analysis and application software, where the CC generates an automatic breaker action, while in some situations, directly sends a movement action to the SBs [30]. SG communication mostly uses wireless infrastructure to send the breakers actions. Using wireless technology in parts of SGs causes a delay in the sensitive data and in various types of attacks [18]. Therefore, there is a necessary need to find a secure path or secure scheme.  
The smart breaker which will be used is ABB eVD4, the hardware minimum requirements for _\"Initialization Phase\"_ are: Pentium III, 800 MHz; 128 MB of RAM; 40 MB of available disk space. The software requirements are: Operating system Microsoft Windows 98/2000/XP; PCM600 Version 2.3 Production Build; Microsoft.NET Framework 2.0; and eVD4-RBX615 Connectivity Package. The eVD4 classified as Intelligent Electronic Device (IED) and use the international standard for substation communication and modeling IEC 61850 with GOOSE messaging and Modbus. The controller of the SB will be REF542_plus_, the block diagram of this controller shown in Figure 1. The ability of REF542_plus_ is extremely powerful in terms of automation.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 2 Biomimetic Mechanism and Inspiration of Insect Flapping Flight
### Structure of Insect Wings  
The wings of insects are usually triangular in shape, with veins and other structures distributed on the surface [18]. According to their morphology, they can be divided into eight categories, such as membranous wings, as shown in Figure 1a. In biomimetic design, membranous wings are chosen as the design prototype and carbon fiber materials with high strength and fatigue resistance [19] are used as the veins, and lightweight and tough film materials are used as the wing membranes. The distribution form of the veins on the wing surface is called venation [20]. Reference [21] indicates that the most sensitive part of the mass response was mainly concentrated in a part of the wing veins. Removing some of its mass will not have a great influence on the structural and mechanical properties of the hindwing model. In the design of biomimetic flapping-wing aircraft, a reasonable venation is beneficial to increase the strength of the biomimetic wing and enable it to produce passive deformation conducive to flight.  
Insect flight involves three modes of interaction between the fore-wings and hindwings. For Lepidoptera insects, both pairs of wings participate in flight, but the fore-wings are dominant, while the hind wings are connected and follow along through a linking structure known as the \"wing coupling device\". The main types of wing coupling devices include amplexi form, jugate form, freate form, haumlate form, and fold form, as shown in Figure 1b. In biomimetic design, an improved version of the jugate form is selected as the structure for the wing coupling device.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 4 Parametric Study on Leakage and Performance  
In this section, the effects of scavenging pressure \(p_{sc}\), ignition position \(x_{ign}\), combustion duration \(t_{cd}\) and piston mass \(m_{p}\) are investigated based on the established dynamic model. In addition, the indicated thermal efficiency \(\eta_{i}\) and effective thermal efficiency \(\eta_{e}\) are calculated as well to evaluate the performance of the MFPG system.  
The mass of the in-cylinder gas at exhaust port closing is denoted as \(m_{gspc}\), which implied the mass sealed in cylinder. The gas mass at combustion start, denoted as \(m_{gvac}\), is the mass of charge that actually participated in combustion. Thus, the difference between these two is the mass leaked during compression, denoted as:  
\[m_{lcp}=m_{gspc}-m_{gvac} \tag{7}\]  
where, \(m_{lcp}\) is the mass leakage of the in-cylinder gas during compression, kg; \(m_{gspc}\) is the mass of the in-cylinder gas at EPC, kg; \(m_{gsoc}\) is the mass of the in-cylinder gas at SOC, kg.  
Likewise, the mass leaked during combustion and expansion, which affects the work performed by the in-cylinder gas, is denoted as \(m_{lexp}\). It can be calculated by deducting the gas mass at the exhaust port opening (EPO) from that of the start of combustion (SOC).  
\[m_{lexp}=m_{gsec}-m_{ggeo} \tag{8}\]  
where, \(m_{lexp}\) the mass leakage of the in-cylinder gas during combustion and expansion, kg; \(m_{ggeo}\) is the mass of the in-cylinder gas at EPO, kg; \(m_{gvac}\) is the gas mass at SOC, kg.  
To investigate the effects of leakage loss and heat loss on system performance, the efficiencies are calculated under three hypotheses:  
**Hypothesis 1 (H1).**_The cylinder was completely sealed and combustion was adiabatic, which is the ideal case and baseline, denoted as \(\eta_{i,a}\) and \(\eta_{e,a}\)._  
**Hypothesis 2 (H2).**_Combustion is still adiabatic, but the gap between the piston and the cylinder is considered, denoted \(\eta_{i,b}\) and \(\eta_{e,b}\). The indicated thermal efficiency difference between Hypothesis 1 and Hypothesis 2, \(\Delta\eta_{i,a-b}=\eta_{i,a}-\eta_{i,b}\) characterizes the effect of leakage loss, \(Q_{lcp}\)._  
**Hypothesis 3 (H3).**_Account for both the leakage loss and heat loss, with the denotation of \(\eta_{i,c}\) and \(\eta_{e,c}\). Similarly, the indicated thermal efficiency difference between Hypothesis 2 and Hypothesis 3, \(\Delta\eta_{i,b-c}=\eta_{i,b}-\eta_{i,c}\) characterizes the effect of heat loss, \(Q_{w}\)._  
Figure 6: Model validation against the measured in-cylinder gas pressure.  
In the three hypotheses, Hypothesis 1 is the baseline hypothesis. It does not have energy loss, which is an ideal circumstance. By taking the two energy loss into consideration one by one, the two thermal efficiencies in Hypotheses 2 and 3 decrease successively. Therefore, the efficiency drops of the two adjacent hypotheses (\(\Delta\eta_{i,a\text{-}b}\) and \(\Delta\eta_{i,b\text{-}c}\)) are regarded as the indicators of leakage loss, \(Q_{lcp}\), and heat loss, \(Q_{w}\), respectively.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 4 Case Analysis
### Comprehensive Evaluation of Prediction Performance  
As can be seen from Figure 11 and Table 4, the maximum error between the predicted value and the actual value was 12 \(\times\) 10\({}^{4}\) m\({}^{3}\), and the corresponding serial number was 37 (the first red dot in the figure), which appeared on January 8 of the third year. The reason may be that the temperature decreased gradually in the previous days, and the user's perceived comfort decreased due to the cumulative effect of temperature, leading to a sudden increase in the daily gas load on January 8. The MWP-DLP model was slow to predict the abrupt load point, which resulted in a large error between the predicted value and the actual value. The maximum relative error between the predicted result and the actual value was \(-8.2\%\), and the mean absolute percentage error (MAPE) was 2.68%. The corresponding serial number was 67 (the second red dot in the figure), which appeared on March 12 of the third year. The reason may be that the temperature on March 12 was 7.5 \({}^{\circ}\)C higher than that on March 9, and the solar radiation value was 443 J/cm\({}^{2}\) higher, which led to the reduction of the daily gas load on March 12, and the model was slow to respond to the sudden change of data. As a result, the predicted results of the model differed greatly from the actual values.  
In conclusion, the MWP-DLP model established in this section can predict the gas load in a certain time in the future, and the error was controlled below 9%, so the model has good feasibility and high effectiveness.
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 4 Discussion
### Mechanism of Amplification Effect of Mining-Induced Earthquakes on Surface Damage
#### 4.2.2 Quantification of Seismic Amplification Effects  
While consistent with [PERSON] et al. [20] in finding a high spatial correlation between high-energy mining-induced earthquakes and maximum subsidence areas in deep coal mines, this study innovatively quantifies the amplification coefficients of seismic-induced subsidence. Through UDEC simulations, we propose a critical microscopic mechanism: seismic waves accelerate rock mass compaction by promoting movement and compressing pores in fractured rocks. This microscopic insight complements [PERSON] et al.'s [20] macroscale observations.
# Fabrication and Photo-Detecting Performance of 2D ZnO Inverse Opal Films
## 4 Conclusions  
An oil-water interfacial co-assembly method was used to fabricate 2D crack-free ZnO IO nanofilms, in which the assembly of PS template colloids and infiltration of Zn\({}^{2+}\) ions were carried out simultaneously. The morphologies of 2D ZnO IO nanofilms varied with the CA/ZA molar ratio. Photo-detecting devices based on such nanofilms showed excellent selectivity and reversible response to optical switch. When the CA/ZA molar ratio was 1:1, the nanofilm device exhibited the largest photocurrent up to 4.6 \(\mu\)A at 1 V bias, which was 1917 times that of the dark current. In this way, this type of crack-free 2D IO film-based device has great potential in high-performance photodetectors, optoelectronic switches, and solar cells.  
Financial supports of this research from the National Natural Science Foundation of China (51322307, 51273218), and \"[PERSON]\" project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation are appreciated.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**CA/ZA**c** **Molar Ratio** & \(\boldsymbol{I_{\text{d}}}\)**a** (\(\mu\)A) & \(\boldsymbol{I_{\text{p}}}\)**b** (\(\mu\)A) & \(\boldsymbol{I_{\text{P}}}\)**/\(\boldsymbol{I_{\text{d}}}\)** \\ \hline
0:1 & 9.7 \(\times\) 10\({}^{-4}\) & 0.46 & 474 \\
0.2:1 & 1.7 \(\times\) 10\({}^{-3}\) & 1.1 & 647 \\
1:1 & 2.4 \(\times\) 10\({}^{-3}\) & 4.6 & 1917 \\ \hline \end{tabular}  
* \(I_{\text{d}}\): Dark current at 1 V bias; b \(I_{\text{P}}\): Photocurrent at 1 V bias illuminated under 370 nm illumination; c CA/ZA, citric acid/zinc acetate.  
\end{table}
Table 1: Summary of the ultraviolet (UV) detecting performance of inverse opal (IO) film photodetectors.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Nanostructures** & **Light of Detection** & **Bias (V)** & **Dark Current** & **Photo Current** & **Reference** \\ \hline NPs & 325 nm & 1 & \(\sim\)0.4 pA & 0.3 \(\mu\)A & [30] \\ HS & 350 nm, 1.32 mW\(\cdot\)cm\({}^{2}\) & 5 & 0.05 \(\mu\)A & 2.62 \(\mu\)A & [27] \\ IO & 370 nm, 2.12 mW\(\cdot\)cm\({}^{-2}\) & 1 & \(2.4\times 10^{-3}\)\(\mu\)A & 4.6 \(\mu\)A & This work \\ \hline \end{tabular}
\end{table}
Table 2: Representative results of ZnO nanostructures with different shapes, and their corresponding UV photodetector performances. NPs: nanoparticles; HS: hollow-sphere; IO: inverse opal.
## 3 Multi-GNSS PPP/INS Mathematical Model
### Uncombined PPP/INS Coupled Model  
Considering the bias error and noise of low-cost IMU, the initial alignment of INS is usually conducted with dynamic mode. The pitch and roll angle are calculated by accelerometer of IMU:  
\[\phi=-sign(f_{D}^{n})sin^{-1}\bigg{(}\frac{f_{L}^{n}}{g}\bigg{)} \tag{18}\]  
\[\theta=sign(f_{D}^{n})sin^{-1}\bigg{(}\frac{f_{N}^{n}}{g}\bigg{)} \tag{19}\]  
where \(\phi\) and \(\theta\) are pitch and roll angle, respectively; \(f^{n}\) is the specific force vector in the \(n\)-frame; \(E\), \(N\), and \(D\) denote the east, north, and down directions, respectively; g is the gravity vector. With the roll angle in the plane set to zero approximately, the approximate pitch and yaw angle are solved by GNSS with the information of location and speed, which can be expressed as [29]:  
\[\phi=tan^{-1}\Bigg{(}\frac{v_{D}}{\sqrt{v_{N}^{2}+v_{E}^{2}}}\Bigg{)} \tag{20}\]  
\[\psi=tan^{-1}\bigg{(}\frac{v_{E}}{v_{N}}\bigg{)} \tag{21}\]  
Moreover, the PPP/INS model coupled with the data of the MEMS module, such as ADIS16405 and the GNSS observations from smartphones, should unify the measurements to the same reference frame using the level arm. The conversion relation with the level arm correction vector \(l^{b}\) calculated by the IMU geometrical center and the GNSS antenna phase center can be expressed as [37]:  
\[r_{\text{GNSS}}^{e}=r_{\text{INS}}^{e}+C_{b}^{e}l^{b} \tag{22}\]  
\[v_{\text{GNSS}}^{e}=v_{\text{INS}}^{e}+C_{b}^{e}\Omega_{ib}^{b}l^{b}-\Omega_{ ie}^{e}C_{b}^{e}l^{b} \tag{23}\]  
where \(r_{\text{GNSS}}^{e}\) and \(v_{\text{GNSS}}^{e}\) denote the position and velocity vectors of the GNSS antenna phase center in the \(e\)-frame, respectively; \(\Omega_{ib}^{b}\) denotes the skew symmetric matrix of \(\omega_{ib}^{b}\). In addition, the state covariance matrix, system noise matrix and state transition matrix should be adjusted. Combining state vector \(X_{\text{GNSS}}\) in Equation (11), the dynamics model of PPP/INS can be expressed as:  
\[\begin{bmatrix}z_{r}^{e}\\ z_{v}^{e}\end{bmatrix}=\begin{bmatrix}r_{\text{GNSS}}^{e}\\ v_{\text{GNSS}}^{e}\end{bmatrix}-\Bigg{(}\begin{bmatrix}r_{\text{INS}}^{e}\\ v_{\text{INS}}^{e}\end{bmatrix}+\begin{bmatrix}C_{b}^{e}l^{b}\\ C_{b}^{e}\Omega_{eb}^{b}l^{b}\end{bmatrix}\Bigg{)} \tag{24}\]  
and the basic parameters of PPP/INS dynamic equation can be written as:  
\[X_{\text{PPP/INS}}=\begin{bmatrix}\hat{r},&\hat{\sigma},&\hat{\phi},&\hat{b} _{a},&\hat{b}_{g}\end{bmatrix} \tag{25}\]  
while the coefficient matrix can be expressed as:  
\[H_{\text{PPP/INS}}=\begin{bmatrix}-I_{3\times 3}&0&C_{b}^{e}l^{b}&0&0\\ 0&-I_{3\times 3}&C_{b}^{e}\Omega_{eb}^{b}l^{b}&0&0\end{bmatrix} \tag{26}\]