esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods
### Factor Graph Model
#### 2.2.1 Factor Graph Method  
The factor graph consists of two different types of nodes and edges between connected nodes. For the state quantity and quantity measurement of the navigation system, the  
Figure 1: Scheme of integrity detection of integrated navigation.  
factor node represents the quantity measurement, the variable node represents the state quantity and the equivalent measurement represents the measurement of the state; it passes through the corresponding factor node and variable node of the edge. In the theory of a factor graph navigation system, the maximum a posteriori probability estimation is used to represent the state estimation problem in navigation.  
Before discussing the fusion process, we constructed a factor graph model of the standard sensor. The reference sources in the system are the IMU and GNSS. The measurements of sensors are transformed into factor nodes. In Figure 2, white circles represent variable nodes. The black, yellow and green circles represent factor nodes. \(f^{\text{IMU}}\) represents the measurement from the IMU. \(f^{\text{GNSS}}\) represents the measurement information from GNSS. The factor graph system with plug-and-play capability can handle fault isolation and reconnection issues.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 2 Materials and Methods
### Representation of Interaction between Features  
For the problem of remote sensing feature extraction, considering the interaction between spectral features will enhance the independence between categories and improve the recognition accuracy of different categories. The most typical example is the NDVI, which uses the ratio of reflectivity between the R (red) band and the NIR (near-infrared) band to improve vegetation identification. Similarly, NDWI, NDBI, etc. all use the interaction between bands. These ratio indexes come from the analysis of the spectral characteristics presented in laboratory observations and remotely sensed data. The purpose of this article is to express the interaction relationship between original features as new features by multiplication or division operation and generalize these ratio indexes.  
For a logistic regression with \(K\)-category, the softmax transformation can be used as the discriminative model to calculate the posterior probability directly for the response variable belonging to class \(k\):  
\[P(\mathrm{Y}^{(k)}=k|\mathrm{X},\alpha)=\frac{\exp(\sum\limits_{j}\alpha_{j} {}^{(k)}X_{j})}{\sum\limits_{i=1}^{K}\exp(\sum\limits_{j}\alpha_{j}{}^{(i)}X_{ j})} \tag{2}\]  
Among them, \(X=\{X_{1},X_{2},\ldots,X_{P}\}\subset\mathbb{R}^{P}\) is a \(P\)-dimensional predictor variable and \(\mathrm{Y}\subset\mathbb{R}^{K}\) represents the response variable, \(K\) is the total number of classes, \(\alpha\) is the vector of parameters for each classes regression, \(\alpha_{j}{}^{(k)}\) is the \(j\)th parameter for class \(k\).  
The interaction between features is often expressed as a paired product relationship:  
\[P(\mathrm{Y}^{(k)}=k|\mathrm{X},\alpha)=\frac{\exp(\sum\limits_{j}\alpha_{j} {}^{(k)}X_{j}+\sum\limits_{j,k}\alpha_{jk}X_{j}X_{k})}{\sum\limits_{i=1}^{K} \exp(\sum\limits_{j}\alpha_{j}{}^{(i)}X_{j}+\sum\limits_{j,k}\alpha_{jk}X_{j}X _{k})} \tag{3}\]  
the summation part \(\sum\limits_{j}\alpha_{j}{}^{(k)}X_{j}\) represents the main feature item, and the quadratic term \(\sum\limits_{j}\alpha_{jk}X_{j}X_{k}\) represents the item of the interaction relationship between the features.  
Figure 1: Technique flowchart.  
Based on the interaction framework of Equation (3), the interaction between features can be expressed in the form of a ratio, which can be further written as:  
\[P(\Upsilon^{(k)}=k|X,\alpha)=\frac{\exp(\sum\limits_{j}\alpha_{j}{}^{(k)}X_{j}+ \sum\limits_{j,k}\alpha_{jk}{}^{(k)}\frac{X_{j}}{X_{k}})}{\sum\limits_{i=1}^{K} \exp(\sum\limits_{j}\alpha_{j}{}^{(i)}X_{j}+\sum\limits_{j,k}\alpha_{jk}{}^{(i )}\frac{X_{j}}{X_{k}})} \tag{4}\]  
The purpose of changing the form of the product is to capture the phenomenon similar to NDVI or NDWI. The advantage of this formula is that it covers all the pair-wise ratio relationships between features. However, not all the ratio relationships of the feature pairs contribute to the separation of feature categories, so it is necessary to find the ratio of useful feature pairs, sparse optimization is an effective method.
## 3 Controller Design Approach
### Limitations of the Adopted Control Schemes  
The pitch angle control system presented in this paper works in all operating regions of the WTG. When the wind speed is from zero to the cut-in value, there is no power generated and the pitch angle is set as \(\beta=90^{\circ}\). Meanwhile, when the wind speed is between the cut-in and rated values, respectively, the pitch angle is fixed at \(\beta=0^{\circ}\) so that the power can be maximized. As the wind speed exceeds its rated value, the proposed pitch angle control system operates between \(0^{\circ}\) and \(90^{\circ}\) to maintain the rated  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Variable** & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **B7** & **B8** & **B9** \\ \hline Value & 0.0342 & 0.0083 & 0.0453 & 0.0388 & 0.082 & 0.083 & 0.115 & 0.0125 & 0.012 \\ Variable & B10 & B11 & B12 & B13 & B14 & B15 & B16 & B17 & B18 \\ Value & 0.00122 & 0.0021 & 0.00432 & 2.43 & 3.87 & 8.56 & 6.24 & 13.86 & 12.13 \\ Variable & B19 & B20 & B21 & B22 & B23 & B24 & B25 & B26 & B27 \\ Value & 18.46 & 16.45 & 23.93 & 22.72 & 36.84 & 34.13 & 52.62 & 58.32 & 59.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal membership function parameter values.  
Figure 6: (**a**) Fuzzy membership functions for the pitch angle controller’s first input. (**b**) Fuzzy membership functions for the pitch angle controller’s second input. (**c**) Fuzzy membership functions for the pitch angle controller’s output.  
power of the WTG. Above the cut-out speed, the pitch angle is again set as \(\beta=90^{\circ}\) for safety reasons. \(\beta\) is limited by a rate limiter (\(\pm 20^{\circ}/\)s) and a limiter within the range (\(0^{\circ}\)-\(90^{\circ}\)). In addition, all power values are per unit (pu) on 100 MVA base. The maximum limit of active power in each transmission line is 200 MW (2 pu).  
The power system model has been linearized using commands of MATLAB(r) to check its stability performance including the proposed control schemes. The singular value plots of the control loops with/without the suggested controllers are shown in Figure 8. The frequency-closed control loop is specified with rotor speed command \(\omega_{ref}\) as the input signal and \(\omega\) as the output signal. The pitch angle closed control loop is formulated with \(\Delta P_{Wtg}\) as the input signal and \(\beta_{CMD}\) is considered as the output signal. From this figure, it is seen that the singular value plot of the frequency control loop has an integral characteristic at the low-frequency domain without using the proposed control schemes. Because of this, the frequency deviations keep increasing so the characteristic can be eliminated. On the other hand, there is a high gain in the low-frequency domain (below 1 rad/s) clarified in the pitch angle control loop without utilizing the intended control approaches. Therefore, the output power fluctuations of WTG will increase at the low-frequency domain.  
Figure 7: Flowchart of the design process of two GA–fuzzy-based control schemes. GA: genetic algorithm.
## 5 Proposed Method
### Training Framework Based on MAPPO  
MAPPO algorithm is used to train the agents in our paper, which adopts CTDE architecture. The framework of MAPPO is shown in Figure 3. In training stage, parallel training environments generate global state \(s_{t}\). Then observation model \(\mathcal{O}\) and reward function \(R\) respectively generate global observation \(\mathbf{o_{t}}\) and global reward \(\mathbf{r_{t}}\). The policy network of UAV generates joint action \(\mathbf{a_{t}}\) based on observation \(\mathbf{o_{t}}\). Finally, the updated global state \(s_{t+1}\) is obtained by feeding joint action \(\mathbf{a_{t}}\) into the training environment, thereby completing one interaction loop. During each interaction, intermediate data [\(s_{t}\), \(\mathbf{o_{t}}\), \(\mathbf{a_{t}}\), \(\mathbf{r_{t}}\), \(s_{t+1}\), \(\mathbf{o_{t+1}}\)] is stored in the replay buffer. Once the accumulated experience data attains a predetermined amount, the gradients are calculated and the weights of policy and value network are updated.  
In testing stage, the observation model \(\mathcal{O}\) takes global state \(s_{t}\) as input and outputs global observations \(\mathbf{o_{t}}\) for each UAV. The UAVs then generate joint actions \(\mathbf{a_{t}}\) based on \(\mathbf{o_{t}}\). Finally, the testing environment updates the global state based on \(\mathbf{a_{t}}\), completing the interaction. The pseudocode of MAPPO algorithm is shown in Algorithm 1.  
Figure 3: Framework of MAPPO. The joint variables over all agents are bold.  
```
Input : number of steps to execute \(E\), batch size \(B\), maximum number of steps in one episode \(T\), learning rate \(l_{r}\) Output : policy network \(\pi_{\theta}\) and critic network \(V_{\phi}\) Initialize \(\theta\), the parameters for policy \(\pi\) and \(\phi\), the parameters for critic \(V\); whilestep\(\leq E\)do
1 Set the replay buffer \(D=\{\}\); for\(i=1\) to \(B\)do
2 Set \(\tau=[]\); for\(t=1\) to \(T\)do
3 foreach agent \(j\)do
4\(p_{t}^{(j)}=\pi(o_{t}^{(j)};\theta)\);
5\(a_{t}^{(j)}\sim p_{t}^{(j)}\);
6\(v_{t}^{(j)}=V(s_{t}^{(a)};\phi)\);
7
8 end for
9 Execute actions \(\mathbf{a_{t}}\), observe \(\mathbf{r_{t}}\),\(\mathbf{s_{t+1}}\),\(\mathbf{o_{t+1}}\); \(\tau+=\left[\mathbf{s_{t}},\mathbf{o_{t}},\mathbf{a_{t}},\mathbf{r_{t}},\mathbf{s_{t+1}},\mathbf{o_{t+1 }}\right]\);
10
11 end for
12 Compute advantage estimate \(\hat{A}\) and reward-to-go \(\hat{R}\);
13
14 end for
15 Compute \(\
abla_{\theta}L^{CLIP}\) and update \(\theta\) with learning rate \(l_{r}\);
16 Compute \(\
abla_{\phi}L^{CLIP}\) and update \(\phi\) with learning rate \(l_{r}\);
17
18 end for
```  
**Algorithm 1**MAPPO
## 3 Methodology
The training and test portions of the data set are selected from the data with the same level of SNR in dB. 2. The training portion of the data set is selected from the data at the highest level of SNR in dB considered in this study, while the test portion of the data set is selected from data at different levels of SNR in dB. Figure 1: Workflow of the proposed approach. The first method (called SAMESNR in the rest of this paper) is simpler to execute because training and test are extracted from the same data set, and it is also more controlled, but it is not very realistic as the training is usually performed at high values of SNR while the conditions in the field can vary. The second method (called DIFFSNR in the rest of this paper) is more related to a practical application of the proposed approach. Both methods are used in this paper. In particular, the first method is used for the selection and identification of the hyper-parameters apart from the mother wavelet, which is evaluated in both methods. Additional details on the two methods, SAMESNR and DIFFSNR, are provided in Section 7.
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 3 Case Study
### Overall Performance Test Rig  
An overall performance test rig was established to acquire transient process measuring data, see Figure 5. The data for the instrument and control system of the gas turbine, through the isolated safety barrier, is sent to the signal acquisition board, where data acquisition, data pre-processing, A-D signal conversion and data output are being completed. The calculative process is finished in the portable decay controller of the board. The processed digital signal will be published to the level I network through wireless access point (WAP) or access point (AP) web. The data management system of the portable condition monitoring system is in charge of data transformation and transmission from the level I network to level II. The interface management system of the remote data center, obtains data from the internet, sends data to main database via the level III network (remote data center local area network (LAN)) according to the defined interface rules, and stores the test data chronically. The remote data management system is mainly responsible for human-computer interactions for a remote data center, with a level III network and interface management server.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Pressure ratio & 24.1 \\ Exhaust mass flow & 84.31 kg/s \\ Power output & 31.4 MW \\ Efficiency of compressor & 85\% \\ \hline \end{tabular}
\end{table}
Table 1: Base load parameters of gas turbine.  
Figure 5: Gas turbine overall performance test rig.
# Flexible Antennas for a Sub-6 GHz 5G Band:
###### Abstract  
The ever-increasing demand and need for high-speed communication have generated intensive research in the field of fifth-generation (5G) technology. Sub-6 GHz 5G mid-band spectrum is the focus of the researchers due to its meritorious ease of deployment in the current scenario with the already existing infrastructure of the 4G-LTE system. The 5G technology finds applications in enormous fields that require high data rates, low latency, and stable radiation patterns. One of the major sectors that benefit from the outbreak of 5G is the field of flexible electronics. Devices that are compact need an antenna to be flexible, lightweight, conformal, and still have excellent performance characteristics. Flexible antennas used in wireless body area networks (WBANs) need to be highly conformal to be bent according to the different curvatures of the human body at different body parts. The specific absorption rate (SAR) must be at a permissible level for such an antenna to be suited for WBAN applications. This paper gives a comprehensive review of the current state of the art flexible antennas in a sub-6 GHz 5G band. Furthermore, this paper gives a key insight into the materials for a flexible antenna, the parameters considered for the design of a flexible antenna for 5G, the challenges for the design, and the implementation of a flexible antenna for 5G.  
flexible antenna; sub-6 GHz; wireless body area network (WBAN); bending; specific absorption rate (SAR); multiple input multiple output (MIMO) +
Footnote †: journal: Sensors
## 2 System Overview and Analysis
### Disturbance Force Analysis  
It is known from Equation (6) that the output loading force \(F_{\rm L}\) consists of two parts: one part \(F_{\rm v}=G_{\rm p}(s)X_{\rm v}(s)\) is controlled by regulating the spool displacement \(x_{\rm v}\), and the other \(F_{\rm d}\) results from the displacement \(x_{\rm M}\) of the loaded plant. This disturbance force \(F_{\rm d}\) is inevitably produced because of the motion of the loaded plant. The amount of \(F_{\rm d}\) depends on the velocity \(\dot{x}_{\rm M}\) of the booster, and is related to the transfer function \(G_{\rm d}(s)\). According to the given parameters in Table 1 and computing the relative terms in Equation (6), the frequency characteristic curves of \(F_{\rm d}\) for each equivalent linear model in \(P_{0}(s)\) are acquired and are shown in Figure 5. As seen from Figure 5, the disturbance force \(F_{\rm d}\) has differential characteristics at low frequencies and large magnitude (in dB).The magnitude of \(F_{\rm d}\) rises with the slope of +20 dB as the frequency increases at low frequencies, tend to be saturated and stable in the middle frequency band, and sharply rise in high frequency region. In practice, the frequency of the position interference will not be high. Meanwhile, at low frequencies (less than 30 rad/s), the  
Figure 4: Frequency characteristics of equivalent model set.  
amount of \(F_{\text{d}}^{\text{(i)}}\) varies a little, about 1 dB for the models in \(P_{0}(s)\), which means model perturbation has a small effect on the disturbance force of our interest.  
Despite this disturbance force \(F_{\text{d}}\) is helpful when the direction of \(\dot{x}_{\text{M}}\) and \(F_{\text{L}}\) is opposite. However, generally, such a disturbance force will mostly increase the force error between the input and output, and it affects force-tracking accuracy. From the perspective of system control, this force error affects the closed-loop bandwidth of the loading system, and with the increase of the disturbance force, that bandwidth is seriously narrowed. It is difficult to achieve an ideal loading performance before this disturbance force has been effectively suppressed. Thus, the problem of disturbance force suppression should be solved firstly in the loading system; otherwise, the closed-loop bandwidth and the force-tracking accuracy cannot be guaranteed.  
Generally, the motion amplitude and frequency of the booster are measurable and bounded; thus, the magnitude and frequency of \(F_{\text{d}}\) must be bounded and measurable. In a helicopter maneuvering system, the frequency of \(x_{\text{M}}\) is low, so \(F_{\text{d}}\) can be seen as a low-frequency disturbance.
## 3 Experiment and Discussion
### Effect of Elastic Recovery
The uncertainty with a coverage factor of \(k\) = 2 (95% confidence) in the measurement of the diamond tool cutting edge radius was evaluated to be 1.97 nm, which was smaller than that obtained in our previous research [22]. The measurement uncertainties under various indentation depths are shown in Figure 3:0 The standard deviation of the evaluated uncertainties was calculated to be 0.005 nm. The results further demonstrated that elastic recovery of the reverse cutting edge did not influence the measurement accuracy of the diamond tool cutting edge radius based on the proposed edge reversal method and the designed nanoindentation system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Uncertainty Sources** & **Symbol** & **Uncertainty Value** & **Distribution** & **Standard Uncertainty** \\ \hline Lateral imaging resolution & \(u_{\text{r}}\) & 0.97 nm & Rectangular & 0.56 nm \\ Lateral positioning resolution & \(u_{\text{L\_n}}\) & 1.2 nm & Normal & 1.2 nm \\ Vertical positioning resolution & \(u_{\text{V\_n}}\) & 0.2 nm & Normal & 0.2 nm \\ Thermal resolution & \(u_{\text{t\_t}}\) & \(3.3\times 10^{-5}\) nm & Rectangular & \(1.9\times 10^{-5}\) nm \\ Measurement resolution & \(u_{\text{t\_d}}\) & 1.457 nm & - & 0.46 nm \\
**Uncertainty in \(R_{\text{tool\_m}}\)** & \(u_{\text{tool\_m}}\) & - & - & 1.36 nm \\ Lateral imaging resolution & \(u_{\text{r}}\) & 0.97 nm & Rectangular & 0.56 nm \\ Lateral positioning resolution & \(u_{\text{L\_n}}\) & 1.2 nm & Normal & 1.2 nm \\ Vertical positioning resolution & \(u_{\text{V\_n}}\) & 0.2 nm & Normal & 0.2 nm \\ Thermal resolution & \(u_{\text{m\_t}}\) & \(4.9\times 10^{-5}\) nm & Rectangular & \(2.8\times 10^{-5}\) nm \\ Measurement resolution & \(u_{\text{m\_d}}\) & 0.385 nm & - & 0.12 nm \\ Elastic recovery & \(u_{\text{m\_e}}\) & 0.358 nm & Rectangular & 0.21 nm \\ Indentation force & \(u_{\text{m\_f}}\) & 0.001 nm & Rectangular & \(5.7\times 10^{-4}\) nm \\
**Uncertainty in \(R_{\text{mark\_m}}\)** & \(u_{\text{mark\_m}}\) & - & - & 1.42 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Uncertainty analysis in the measurement of a diamond tool with a nose radius of 1 mm. Figure 10: The theoretical elastic recovery under various indentation depths.
## 3 Generative Adversarial Networks (GANs)  
### The Generator and the Discriminator  
The popularity of GANs has continued to increase because they have many addressable applications. [PERSON] et al. [19] introduced GAN in 2014. A GAN is composed of two separate networks: the generator and the discriminator. During the training process, the generator learns how to generate data that mimics real data, whereas the discriminator learns how to differentiate real data from fake data produced by the generator. The training is operated jointly so that both are competing and playing an adversarial zero-sum game.  
During the training, the generator is trying continuously to generate fake data that deceives the discriminator so that it classifies them as real. However, the discriminator is trying continuously to detect the fake data and not to classify them as real. To solve the adversarial zero-sum game during the training, game theory theorems are used. Figure 2 indicates the standard architecture of the GAN network.  
Figure 2: Standard architecture of the GAN.  
During training, both networks engage in competition until a Nash equilibrium is reached. In game theory, Nash equilibrium is a status where no player is able improve or deviate his payoff [40].  
Equation (1) shows the objective function of GAN:  
\[min_{G}max_{D}V(Dis,Gen)=\mathbb{E}_{X\sim P_{real\_data}(X)}[logDis(X)]+ \mathbb{E}_{z\sim P_{z}(z)}[log(1-Dis(Gen(z)))] \tag{1}\]  
where \(Gen\) denotes the generator cost function which is trained through the maximization of \(Dis(Gen(z))\). On the other hand, \(Dis\) represents the discriminator cost function which is trained through the minimization of \(Dis(Gen(z))\). While \(X\) is the image that is obtained from the distribution of real data \(p_{real\_data}\). The noise vector obtained from distribution \(p_{z}\) is denoted by \(z\) whereas \(Gen(z)\) indicates the fake image that is produced by the generator. Additionally, \(\mathbb{E}_{X\sim P_{data}(X)}\) represents the expectation on \(X\), which is acquired by the distribution \(P_{real\_data}(X)\). Both Dis and Gen play the two-player minimax game using the value function \(V(Gen,Dis)\)[19].  
Generative adversarial networks have many applications and implementations [41] with image-to-image translation being the application that may be the most attractive to be used in the domain adaptation's context. The use of GANs in the area of image-to-image translation is further discussed in the following subsection.