esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Ablation Study  
In this section, we aim to isolate and understand the individual contributions of each component in the MGA-BI model by conducting ablation experiments. The deep residual network serves as the base model, and we made the following modifications: (1) No BI: We removed the bilinear interaction (BI) [37] layer from the model. (2) No mask-gating attention (MGA) [36] mechanism: We removed the mask-gating attention layer from the model. (3) No residual mechanism [40]: We removed the residual mechanism from the model. By performing these ablations, we can assess the relative importance of each component in the overall performance of the MGA-BI model. This analysis allows us to gain a better understanding of the role played by each component in achieving the reported empirical results. The experimental results are shown in Tables 8-10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Steganalysis Method** & **Inference Time (ms)** \\ \hline DMSM & 0.00073 \\ FSMDP & 0.00080 \\ Stegformer & 0.00092 \\ Our Method & 0.00098 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average inference time for each frame.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Network Description** & **Accuracy** \\ \hline \#1 & No MGA mechanism & 0.7387 \\ \#2 & No BI mechanism & 0.7340 \\ \#3 & No residual mechanism & 0.7412 \\ \#4 & The complete & 0.7446 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study comparing the performance of different components in the CNV method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Network Description** & **Accuracy** \\ \hline \#1 & No MGA mechanism & 0.7387 \\ \#2 & No BI mechanism & 0.7340 \\ \#3 & No residual mechanism & 0.7412 \\ \#3 & The complete & 0.7446 \\ \#4 & The complete & 0.8692 \\  & proposed model & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study comparing the performance of different components in the ACL method.  
From the results presented in Tables 8-10, the following observations can be made. First, removing the mask-gating attention [36] and bilinear interaction [37] layers from our model degraded performance. This suggests that these two components are effective and contribute significantly to the overall performance of our method. Second, the experimental results indicate that the bilinear interaction layer is just as crucial as the MGA layer for achieving high performance. This highlights the importance of capturing second-order feature interactions in the model. Lastly, the ablation experiments suggest that the residual mechanism is useful for our method. By preserving the input information, the residual mechanism allows the model to learn from both the raw features and the transformed features provided by the mask-gating attention and bilinear interaction layers, ultimately improving performance. In summary, these ablation experiments provide insight into the individual contributions of the different components of our MGA-BI model and highlight the importance of each component in achieving high performance in the task of interest.
## 3 Model Analysis under the Static Reward and Punishment Mechanism
### Stability Analysis of Shared Platform Strategy  
The value perception of \"positive regulation\" and \"negative regulation\" in the shared platform are \(T_{1g}\) and \(T_{2g}\), respectively.  
\[\begin{array}{l}T_{1g}=T(\Delta\pi)w(\epsilon)=w(p)[w(\epsilon)(P(Hc)-C(C_{ gh}+R))+w(1-e)(P(H_{c})-C(C_{gh}+\alpha L+R))]\\ +w(1-p)[w(e)(P(Hc+F)-C(C_{gh}+\alpha L))+w(1-e)(P(H_{c}+F)-C(C_{gh}+L))]\\ \\ T_{2g}=T(\Delta\pi)w(\epsilon)=w(p)[w(1-e)(-C(\alpha L))]+w(1-p)[w(e)(-C( \alpha L))+w(1-e)(-C(L))]\\ \\ \end{array} \tag{11}\]  
The average value perception of demand side is \(\overline{T_{g}}\).  
\[\overline{T_{g}}=gT_{1g}+(1-g)T_{2g} \tag{12}\]  
The dynamic equation of shared platform replication is \(F(g)\).  
\[\begin{array}{l}F(g)=dg/dt=\)\(g(T_{1g}-\overline{T_{g}})=g(1-g)(T_{1g}-T_{2g})=g(1-g)\\ \left\{\begin{array}{l}w(p)[w(e)(P(H_{c})-C(C_{gh}+R))+w(1-e)(P(H_{c})-C(C_ {gh}+\alpha L+R))]-\\ w(p)[w(1-e)(-C(\alpha L))]+w(1-p)[w(e)(P(H_{c}+F)-C(C_{gh}+\alpha L))+\\ w(1-e)(P(H_{c}+F)-C(C_{gh}+L))]-w(1-p)[w(e)(-C(\alpha L))+w(1-e)(-C(L))]\\ \end{array}\right\}\end{array} \tag{13}\]  
For the convenience of calculation, the simplified formula is:  
\[F(g)=g(1-g)[w(p)M+w(1-p)N] \tag{14}\]  
The parameter \(M\) represents the difference between the platform's active supervision value function and the passive supervision value function when the quality of the supply side is improved; the parameter \(N\) represents the difference between the platform's active supervision value function and the passive supervision value function when the supply side does not improve quality.  
To find the partial derivative of the shared platform replica dynamic equation, there are the following relationships:  
\[\frac{\partial F(g)}{\partial H_{c}}\textgreater 0,\ \frac{\partial F(g)}{ \partial\alpha}\textgreater 0,\ \frac{\partial F(g)}{\partial C_{gh}}\textless 0,\ \frac{\partial F(g)}{ \partial L}\textgreater 0,\ \frac{\partial F(g)}{\partial F}\textgreater 0.\]According to Proposition 3, the probability of active supervision of a shared platform is positively correlated with the perceived benefit of active supervision, the spillover coefficient of quality risk, the risk of quality accidents, and the severity of punishment, and negatively correlated with the perceived labor cost of active supervision. In the shared manufacturing mode, the final stability strategy of the shared platform is active supervision, which improves the perception of benefits, quality risk spillover coefficient, quality accident risk, and punishment of active supervision while decreasing the perception of labor costs of active supervision.
## 4 Local Transmission Control: Redundancy-Based Transmission
### Local Route Establishment
#### 4.1.1 Local Route Setup  
A hub node starts the route setup process by sending a route-setup (RS) packet to its target hub node. Figure 3 illustrates how an RS packet is sent between hub nodes \(H_{1}\) and \(H_{11}\). In the figure, the bold numbers indicate the degree of redundancy and the number of adjacent nodes that are mobility synchronized. The RS packets convey information about the degree of redundancy and the hop distance of the nodes that the packet passes. In the figure, two RS packets reach \(H_{11}\). One RS packet is forwarded with \(H_{1}\)-\(N_{2}\)-\(N_{4}\)-\(N_{8}\)-\(H_{11}\). The other RS packet is forwarded along the path \(H_{1}\)-\(N_{3}\)-\(N_{6}\)-\(N_{12}\)-\(N_{13}\)-\(H_{11}\). Equation (19) shows how it chooses the best path:  
\[\arg\max_{P\in\Pi(H_{i},H_{j})}\left\{\sum_{n\in P}\min\left\{R(n),UB\right\} \right\}, \tag{19}\]  
where \(\Pi(H_{i},H_{j})\) denotes the set of routes from hub \(H_{i}\) to hub \(H_{j}\), and \(R(n)\) denotes the number of synchronized neighbor nodes of the node \(n\) in path \(P\). The proposed control chooses the path with the highest sum of the degree of redundancy of the intermediate nodes included in the path. However, the degree of redundancy of the paths can be affected by certain links with a very high degree of redundancy. To solve this problem, we limit the degree of redundancy of each link to a specific upper bound, \(UB\). In the figure, the first and second path have one and seven degrees of redundancy, respectively. Therefore, the destination hub selects the second path as the best path. In this procedure, every node that receives the RS packet follows Algorithm 3.  
```
1:Case 1: If the receiving node is an intermediate node and the same RS packet has not been received before, the receiving node saves the address of the node that sent the packet as an upstream node. The saved node address can be used later to create a primary path. The receiving node then adds its degree of redundancy to the degree of redundancy in the receive RS packet and broadcasts the updated RS packet to its neighbors.
2:Case 2: If the receiving node is an intermediate node and the same RS packet has already been received, the receiving node saves the address of the node that sent the packet as a redundant upstream node and then drops that packet. The saved node address can be used later to create a redundant path.
3:Case 3: If the receiving node is the destination node, it saves the intermediate forwarding node addresses, final hop count, and path redundancy of the received packet.
```  
**Algorithm 3** Local route setup process.
## 4 Results and Discussion  
During the testing period (DOY 273-278, 2018), the orbits of GRCC and CS01 were generated every 2 h and there were 67 sets of results. Because there were no external measurements for CS01, post-fit phase residuals and orbit overlap differences were used to evaluate the CS01 orbit's consistency. In addition to the above evaluation methods, Jet Propulsion Laboratory (JPL) orbit comparison was adopted for GRCC. The overlap time between two adjacent arcs was 10 h, and to avoid the effect of edge, the central 8 h arcs were used for analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Description** \\ \hline \hline
**Measurement model** & \\ \hline
**Observation** & Non-differentiated ionosphere-free linear combination \\ \hline
**Arc length and interval** & 12 h, 10 s \\ \hline
**Weighting strategy** & PC (a priori sigma of 1 m), LC (a priori sigma of 1 cm) \\ \hline
**GPS products** & CODE final products [34] \\ \hline
**Elevation cut-off angle** & 5\({}^{\circ}\) \\ \hline
**GNSS PCO and PCV** & igs14.atx [35] \\ \hline
**LEO attitude** & Discussed \\ \hline
**Phase wind-up** & Discussed \\ \hline
**LEO PCO and PCV** & Nominal values; Discussed \\ \hline
**Dynamic model** & \\ \hline
**Earth gravity** & EIGEN\_GL04C, 120 \(\times\) 120 [36] \\ \hline
**N-body** & JPL DE405 [37] \\ \hline
**Relativity** & IERS 2010 [38] \\ \hline
**Solid earth tide and pole tide** & IERS 2010 [38] \\ \hline
**Ocean tides** & FES 2004, 30 \(\times\) 30 [39] \\ \hline
**Solar radiation pressure** & Cannonball model [40] \\ \hline
**Atmospheric drag** & NRLMSISE-00 [41], piecewise periodical estimation \\ \hline
**Empirical forces** & Piecewise periodical estimation of the sin and cos \\  & coefficients in the track and normal directions \\ \hline
**Estimated parameters** & \\ \hline
**LEO initial state** & Position and velocity at the initial state \\ \hline
**Receiver clock** & Epoch-wise estimated \\ \hline
**Ambiguities** & Floated solution \\ \hline
**Solar coefficients** & One per 3 h \\ \hline
**Drag coefficients** & One per 3 h \\ \hline
**Empirical coefficients** & One per 3 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Processing strategies for testing spacecrafts.
###### Abstract  
In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.  
urban rail transit; disruption; signal interruption; AFC data Article
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 5 Simulation Results  
To validate the performance of the FDI algorithm, the simulation environment was arranged with a PEM fuel cell model for simulation and FDI algorithm implementation. Faults in sensors and actuators were defined for the simulation.  
Faults in sensors were defined as additive faults resulting in an abnormal dc offset. In actuators, faults were defined as follows: additive faults in the fuel flow valve caused by weakening in the spring tension, additive faults in the air shut-off valve caused by overheating, burn out coils and multiplicative faults of minus 10 percent caused by demagnetization in overheated permanent magnets. The result of the air pressure sensor fault is shown in Figure 10. At t = 70 s, the air pressure sensor fault was set to \(-3000\) Pa (\(fyp_{sm}=-3000\)). Through the FDI algorithm in the decision-making process, the fault was classified as an air pressure sensor fault and is shown on fault indicators in Figure 11. The proposed FDI algorithm shows the capability of diagnosing faults in the case of pressure sensor faults and in every other fault case.  
Figure 9: Fault detection and isolation algorithm.  
Figure 10: Residuals in case of air pressure sensor fault.  
As mentioned in the fault analysis section, residual 9 in Figure 10 goes below the threshold value in the presence of a fault because of the compressor map characteristic. Therefore, the fault diagnostic algorithm must be designed to not misjudge the state of the system.
## 2 System Structure and Operation Principle  
The schematic of the proposed high linearity microwave photonic up-conversion system is shown in Figure 1. The optical carrier emitted by the laser diode (LD) is divided into two paths by the variable optical coupler (VOC) and sent to two DD-MZMs, respectively. The upper path is the main frequency conversion path, and the lower path is the IMD3 cancellation path. The IF signal is divided into two paths by the variable electric coupler (VEC) and loaded to the RF input port of the two DD-MZMs together with the LO signal equally divided by a 3-dB coupler. The two DD-MZMs work at the minimum DC bias point and output CS-DSB signals. After being amplified by EDFA, the optically carried signals are input to the BPD for optoelectrical conversion to realize the frequency up-conversion. In order to analyze the conditions for suppressing the IMD3 components in the process of frequency conversion, a two-tone IF signal is input to the two DD-MZMs. The two modulators work at the CS-DSB modulation state. For the convenience of analysis, supposing the power distribution ratio of the IF signal input to the modulator in the upper path and the modulator in the lower path is a\({}^{2}\):1, and the power distribution ratio of the optical carrier is b\({}^{2}\):1, the optical signal output from the modulator in the upper path can be expressed as  
\[E_{1}=bAe^{i\omega_{c}t}\left[e^{i(\frac{\pi V_{DD}\cos\omega_{1}}{\sqrt{ \pi}}+\frac{\pi mV_{11}}{\sqrt{\pi}}\cos\omega_{iI1}t+\frac{\pi mV_{F2}}{\sqrt {\pi}}\cos\omega_{iI2}t)}+e^{i(\frac{\pi V_{LO}}{\sqrt{\pi}}\cos\omega_{iLO} t)}\right] \tag{1}\]By using the Jacobi-Anger expansions, Equation (1) can be expressed as  
\[ \begin{array}{ll}E_{1}&=bAe^{i\omega_{c}t}[e^{i\varphi}e^{im_{1}\cos\omega_{IF1 }t}e^{im_{1}\cos\omega_{IF2}t}+e^{im_{2}\cos\omega_{LO}t}]\\ &=bAe^{i(\omega_{c}+\omega_{IF1}+\omega_{IF2})t+i\varphi}\cdot\sum\limits_{n=- \infty}^{+\infty}\sum\limits_{m=-\infty}^{+\infty}j^{m+n}J_{n}(am_{1})J_{m}(am_ {1})\\ &+bAe^{i(\omega_{c}+\omega_{LO})t}\cdot\sum\limits_{p=-\infty}^{+\infty}j^{p}J_ {p}(m_{2})\end{array}  \tag{2}\]  
where \(J_{n}\), \(J_{m}\) and \(J_{m}\) are the Bessel functions of the first kind, \(m_{1}=\pi V_{IF_{1}}/V_{\pi}=\pi V_{IF_{2}}/V_{\pi}\) and \(m_{2}=\pi V_{LO}/V_{\pi}\) are the modulation indexes of IF signals and the LO signal and \(A\) is the amplitude of the optical carrier. \(\varphi=\pi V_{DC}/V_{\pi}\) is the phase shift caused by the DC bias voltage. Similarly, the output optical signal from the modulator in the lower path can be expressed as:  
\[ \begin{array}{ll}E_{2}&=Ae^{i\omega_{c}t}[e^{i\varphi}e^{im_{1}\cos\omega_{ IF1}t}e^{im_{1}\cos\omega_{IF2}t}+e^{im_{2}\cos\omega_{LO}t}]\\ &=Ae^{i(\omega_{c}+\omega_{IF1}+\omega_{IF2})t+i\varphi}\cdot\sum\limits_{n=- \infty}^{+\infty}\sum\limits_{m=-\infty}^{+\infty}j^{m+n}J_{n}(m_{1})J_{m}(m_ {1})+Ae^{i(\omega_{c}+\omega_{LO})t}\cdot\sum\limits_{p=-\infty}^{+\infty}j^{p }J_{p}(m_{2})\end{array}  \tag{3}\]  
When \(m=n=p=1\), the output fundamental frequency signal can be obtained, and when \(m=\pm 2\), \(n=-1\), \(p=1\), the IMD3 component can be obtained.
## 3 Discussion
### Antibacterial Activity  
Extracts exhibiting MIC values below 100 \(\upmu\)g.mL\({}^{-1}\) are considered to have very strong antimicrobial activity; when MIC values are between 100 and 500 \(\upmu\)g.mL\({}^{-1}\), the antimicrobial activity is moderate; from 500 to 1000 \(\upmu\)g.mL\({}^{-1}\), the antimicrobial activity is weak; and above 1000 \(\upmu\)g.mL\({}^{-1}\), the extract is considered inactive [30; 42].  
Studies on volatile essential oils from _B. campestris_ flowers and leaf extracts of _B. laevifolia_ and _B. oxyclada_ have demonstrated moderate to very strong antimicrobial activity against _S. aureus_, particularly for oral diseases like caries and periodontitis [43]. The ethanolic leaf extract of _B. anisandra_ showed significant inhibition against _S. aureus_[23; 44].  
In contrast, investigations into inhibition activity against _S. aureus_ and _P. aeruginosa_ did not result in microbial growth inhibition for _B. anisandra_ extracts [45]. Research on extracts from _Byrsonima crassifolia_ fruit parts found that the greatest antimicrobial potential was observed against gram-positive bacteria (_S. aureus_), particularly in ethanolic extracts (pulp and peel), while ether extracts did not inhibit the same bacterial growth [46]. Additionally, methanolic extracts from stems and roots of _Byrsonima_ species also demonstrated significant inhibition against _S. aureus_, _E. coli_, and other bacteria [47].  
Considering that _S. aureus_ is a gram-positive bacterium, this observation can be related to the structural characteristics of the bacterial types involved, since _E. coli_ and _P. aeruginosa_ are gram-negative. It is well known that the structural characteristics of gram-negative and gram-positive bacteria provide differences in their antimicrobial activities in relation to plant extracts and other agents [48].  
The absence of antibacterial activity, or the lower sensitivity of microorganisms, may also be due to the type of extract used and the quality of the plant material itself, which can be affected by seasonal conditions, soil, and bioactive content [48].
# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 2 Constructing a Knowledge Graph for Comprehensive Line Loss Evaluation Management
### Expert Integer Scores for the DEMATEL Method  
This study incorporates the perspectives of numerous scholars, utilizing the line loss management index system developed in pertinent literature to guarantee the comprehenseness and integrity of the preliminary set. In order to construct the risk assessment influencing factors survey for line loss management in this study, a Delphi method was employed, based on the evaluation dimensions and index selection principles. The construction of the index is based on the following steps:  
1. Experts with knowledge and experience in related fields were selected, primarily comprising power grid engineers, operation specialists from municipal network companies, and university teachers.
2. Delphi Survey: The experts were provided with background materials on the indicators, which included a list of potential evaluation indicators, such as equipment performance, operational efficiency, planning strategies, and so forth. They were then asked to score the importance of each indicator. Additionally, experts were permitted to propose indicators they deemed important but not listed. A survey form was distributed to solicit expert opinions anonymously.
3. Expert opinions were analyzed and summarized to obtain the results of the first round of indicator surveys.
4. Following a series of anonymous consultations and feedback sessions, the final analysis was presented.  
The number of dimension indicators is set to be approximately equivalent, which effectively reduces differences and allows for a comparison between municipal network companies. The correlation between indicator factors is quantified using a 0-4 scale method. The evaluation scale is set as follows: a value of 0 indicates that there is no discernible impact between the two indicators. A value of 1 implies a minor influence between the two indicators. A value of 2 signifies an average influence between the two indicators. A value of 3 denotes a notable influence between the two indicators, while a value of 4 represents a pronounced influence between the two indicators. Based on the results of the experts'assignments, an initial direct impact matrix \(M\) was established, as follows: \(x_{ij}\) indicates the degree of impact of indicator \(F_{i}\) on indicator \(F_{j}\).  
\[M=\begin{bmatrix}0&x_{12}&\cdots&x_{1n}\\ x_{21}&0&\cdots&x_{2n}\\ \vdots&\vdots&0&\vdots\\ x_{n1}&x_{n2}&\cdots&0\end{bmatrix} \tag{3}\]