esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulation of OPF Problem
### Cost Function Formulation for Emission and Carbon Tax  
During the operation of thermal generators, harmful emissions are released into the atmosphere due to consumption of fossil fuels. The release of these emissions increases  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Quantity** & **Description/Ranges** \\ \hline Branches & 41 & [47] \\ Buses & 30 & [47] \\ Thermal generators & 3 & At bus \#1 (slack), \#2 and \#8 \\ SPV plant & 1 & At bus \#13 \\ WTs & 2 & At bus \#5 and \#11 \\ Load connected & - & 283.4 MW and 126.3 MVAr \\ Shunt compensators & 2 & At bus\# 5(0.19 MVAr) and at bus 24(0.04 MVAr) \\ Load buses & 24 & (0.95–1.05) p.u. \\ Voltages of voltage generation buses & 6 & (0.95–1.10) p.u. \\ Control variables & 11 & Schedule active power for 5 sources (except slack). \\ Generation buses voltages. \\ \hline \end{tabular}
\end{table}
Table 1: Adopted test system details.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Bus \#** & \(P_{TGi}^{\min}\) & \(P_{TGi}^{\max}\) & \(a\) & \(b\) & \(c\) & \(d\) & \(e\) & \(\gamma\) & \(\beta\) & \(\alpha\) & \(\omega\) & \(\mu^{T}\) \\ \hline
1 & 50 & 200 & 0 & 2 & 0.00375 & 18 & 0.037 & 6.49 & \(-\)5.554 & 4.091 & 6.667 & 0.0002 \\
2 & 20 & 80 & 0 & 1.75 & 0.0175 & 16 & 0.038 & 5.638 & \(-\)6.047 & 2.543 & 3.333 & 0.0005 \\
8 & 10 & 35 & 0 & 3.25 & 0.00834 & 12 & 0.045 & 3.38 & \(-\)3.55 & 5.326 & 2 & 0.002 \\ \hline \end{tabular}
\end{table}
Table 2: Details of thermal generators cost and emission coefficients.  
with the escalation of output power from thermal generators. The expression for calculating the emission in ton/h is given below:  
\[E_{emi}=\left[\sum_{i=1}^{nT}\left(\alpha_{i}+\beta_{i}P_{TGi}+\gamma_{i}P_{TGi }^{2}\right)\times 0.01+\omega_{i}e^{(nT;P_{TGi})}\right] \tag{3}\]  
Emission coefficient values for thermal generators are given in Table 2 and are the same as in reference [8]. To control greenhouse gas emission, various countries are now putting immense pressure on power production sectors due the greenhouse effect [48]. As a result, the world has seen a shift towards solar photovoltaics, due to its tremendous potential across the globe and green renewable nature [49]. To reduce harmful emissions and encourage the integration of environmentally friendly and cost-effective RES such as SPV and wind, carbon tax is applied on a per unit quantity of produced emissions. The emission cost ($/h) for thermal generators is formulated as below:  
\[C_{emi}=C_{t}\times E_{emi} \tag{4}\]
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 3 Results
### Anomaly Detection Model Comparison
#### 3.2.1 Evaluation Based on Synthetic Data  
The ROC curves of different models for the synthetic data set 1 are shown in Figure 10. Again, as described in Section 3.1.1 the plot would ideally show dots for the models at the upper left corner. Most of the models show good results except the OCSVM for which parameter combinations exist that yield poor classification performance. This means that all models are capable of identifying anomalies reliably and with a low false alarm rate in the case of high anomaly amplitudes and low noise level. In contrast, the models' overall performance regarding the synthetic data set 2 is rather poor. Figure 11 summarises the ROC curves for this data set. No perfect classifier was found for all models and the distance of the models' ROC curves to the upper left corner is large. Here, it can be concluded that the models struggle to detect anomalies at high noise levels and low anomaly amplitudes. This fact will also be discussed in Section 4. The AUC values for all models and both data sets are provided in Table 3. The individual ROC curves of all models for both data sets  
Figure 9: Results of the trend detection models based on accelerated wear test data.  
are presented in Figures A1 and A2. The best overall performance show the LSTM, STD and LOF models based on their average AUC values. Hence, it was decided to use the LSTM, STD and the LOF model on the accelerated wear test data.
## 3 Construction of Integrated Control Problems Based on Heterogeneous FACMAC
### FACMAC-Based Combined VSL and LCC Controller
#### 3.3.1 Agents  
In different merging areas of the highway, the VSL agent class \(AC_{VSL}\) and the LCC agent class \(AC_{LCC}\) are constructed with instance objects \(BN_{i}^{\textit{vsl}}\) and \(BN_{i}^{\textit{lcc}}\), where \(i\) represents the location number of the entrance ramp merging area. \(BN_{i}^{\textit{vsl}}\) mainly observes and estimates the traffic conditions in the speed control area upstream of the ramp merging bottleneck and adjusts the speed limits accordingly. This adjustment aims to alleviate congestion caused by excessive traffic density in the merging area. \(BN_{i}^{\textit{lcc}}\), on the other hand, adjusts the traffic flow on different lanes within the current ramp merging area to optimize lane density distribution. The integrated control group \(G_{i}=BN_{i}^{\textit{vsl}}+BN_{i}^{\textit{lcc}}\) for ramp \(G_{i}\) is responsible for the integrated control of the entire merging bottleneck, enabling vehicles from both the mainline and the ramp to merge more efficiently.  
```
1:Initialize policy networks \(\pi_{\theta}\) (actor), value networks \(Q_{\phi}\) (critic), and mixer network \(M\) (centralized \(Q\) computation)
2:Initialize target networks \(\pi_{\theta^{\prime}},\ Q_{\phi^{\prime}}\) and \(M^{\prime}\) by copying parameters from \(\pi_{\theta^{\prime}},\ Q_{\phi}\) and \(M\)
3:Initialize replay buffer \(D\) and hyperparameters (learning rates \(a_{actor},\ a_{critic},\ \text{discount factor}\ \gamma,\ \text{and soft update factor}\ \tau\))
4:while training steps < maximum training steps do
5: Reset the environment, initialize state \(s_{0}\)
6: Reset exploration noise
7:for\(t=1\) to \(T\)do
8:for each agent \(i=1\) to \(N\)do
9: Observe state \(s_{t}^{i}\) (local observation and history)
10: Generate action \(a_{t}^{i}=\pi_{\theta}\big{(}s_{t}^{i}\big{)}\) + noise
11:endfor
12: Execute joint actions \(a_{t}=\big{\{}a_{t}^{1},...,a_{t}^{N}\big{\}},\ \text{receive reward}\ \gamma_{t},\ \text{next state}\ s_{t+1},\ \text{and done flag}\ d_{t}\)
13: Store \((s_{t},\ a_{t},\ \gamma_{t},\ s_{t+1},\ d_{t})\) in replay buffer \(D\)
14:endfor
15:if\(D.size\geq batch\_size\)then
16: Sample a batch of transitions from \(D\)
17: Compute target Q - values using target networks \(\pi_{\theta^{\prime}},\ Q_{\phi^{\prime}}\) and \(M^{\prime}\)
18: Update critic networks \(Q_{\phi}\) and mixer network \(M\) using Adam optimizer to minimize the TD error
19: Update actor networks \(\pi_{\theta}\) using Adam optimizer to maximize the expected return
20: Perform soft updates for target networks \(\pi_{\theta^{\prime}},\ Q_{\phi^{\prime}}\) and \(M^{\prime}\)
21:endif
22:endwhile
```  
**Algorithm 1** FACMAC-based combined control of VSL and LCC
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Deployable Antenna
#### 3.1.2 Perimeter Truss Deployable Antennas  
Perimeter truss deployable reflector antennas, or ring truss antennas, consist of a circular truss and a flexible cable-net reflector. Deployment arms drive the antenna into position, where the truss expands to tension the mesh and form the reflector surface [40]. The structure includes quadrilateral units with telescopic diagonals, and cables threaded through the diagonals contract them synchronously during deployment [41; 42]. The deployment process of the perimeter truss deployable antenna is illustrated in Figure 8a. This configuration offers a high deployment ratio, good structural precision, and relatively uniform weight distribution. Compared to framework antennas, it is simpler and lighter, making it well suited for spaceborne SAR systems requiring compact stowage and reliable deployment.  
For example, the Acadia satellite series, representing Capella Space's latest generation of X-band SAR satellites, employs an 8 m\({}^{2}\) perimeter truss deployable antenna as part of its new constellation launched in 2023 [19]. Germany's Tandem-L mission, proposed by the German Aerospace Center (DLR), also utilizes a perimeter truss reflector to enable high-resolution, wide-swath imaging. Tandem-L is an L-band fully polarimetric bistatic SAR constellation designed for real-time monitoring of dynamic Earth processes, including global biomass mapping, surface deformation, and disaster response [43; 44; 45; 46; 47; 48; 49; 50]. The NASA  
Figure 7: Framework deployable antenna: (**a**) HJ-1-C [38]; (**b**) Kondor-FKA 1 illustration [39].  
ISRO Synthetic Aperture Radar (NISAR) mission, a joint initiative between NASA and ISRO, consists of two fully polarimetric SAR satellites operating in the L- and S-bands, respectively [16; 51]. NISAR features a 12-m diameter perimeter truss deployable reflector, as illustrated in Figure 8b. Another example is EOS SAR, developed by EOS in the United States, which operates in the X- and S-bands and integrates a lightweight perimeter truss reflector antenna with a total SAR payload mass of 50 kg. Meanwhile, the Tandem-L in Germany also adopts a peripheral truss deployable antenna, as illustrated in Figure 8c.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 6 Security Objectives of WSN-IoT
### Integrity  
Integrity is one security idea that empowers legitimate and authorized access to modify data according to requirements under limited conditions. Integrity can forestall inner attacks, the most hazardous issue in the network framework, as all users must be validated and authorized with access rights. Notwithstanding, cybercriminals may change information during network communication. Integrity may preempt outside attacks to get to or alter sensitive information.
## 3 Real-Time Implementation of FCS-MPC Techniques
### FCS-MPC: Variable Switching Frequency Operation with Switching Frequency Minimization  
Power converters rely on semiconductor devices as key design components. The type, quality, and control techniques of these semiconductors greatly impact the converter's efficiency. In coordinating the 2L-VSI, semiconductors are expected to function like ideal switches, a condition easily tested with electrical simulation software. This helps designers minimize analysis variables.  
However, in practice, semiconductors have inherent characteristics, such as switching losses from energy transfer in the linear region of the semiconductor device during state transitions [8]. In some applications, minimizing the switching frequency of semiconductor devices reduces energy loss and prevents overheating in power converters. Uncontrolled heat can cause premature wear and damage [58]. The above necessity gives space for the integration of new control objectives that can be combined with the main ones. This is realized by adding new terms to the cost function to minimize [34; 47]. This need drives modern power converter design, which involves the development of PWM strategies to control the output while reducing THD and switching losses, often by maintaining a low switching frequency without sacrificing performance [9].  
Then, to the cost function established in Equation (12), the added term is given by the following expression given in Equation (13):  
\[\begin{split} F_{m}&=(S_{a1}(k)-S_{a1}(k-1))^{2}\\ &+(S_{b1}(k)-S_{b1}(k-1))^{2}\\ &+(S_{c1}(k)-S_{c1}(k-1))^{2}\end{split} \tag{13}\]  
\[g_{min}=(v_{c\alpha}^{*}-v_{c\alpha}(k+2))^{2}+(v_{c\beta}^{*}-v_{c\beta}(k+2))^{2 }+\lambda_{min}\times F_{min} \tag{15}\]  
In Equation (15), the \(F_{min}\) term can be introduced as a weighted sum using the weighting factor (\(\lambda_{min}\)), whose value can be modified to generate a greater or lesser influence on the control action of the predictive algorithm applied to the three-phase, two-level VSI [47].  
This weighting factor (\(\lambda_{min}\)) affects the cost function evaluated by the predictive control algorithm, depending on the value that \(\lambda_{min}\) takes. If its value is higher, there is a point where the main control objective, i.e., the capacitor voltage (\(v_{c}\)), is distorted beyond acceptable values.  
The procedure for implementing control of the three-phase, two-level VSI is primarily focused on maintaining the voltage across the capacitor (\(v_{c}\)) of the \(LC\) filter. In addition, it aims to minimize the commutations of transistors in the power converter as a secondary objective. These steps are clearly illustrated in Figure 4. However, instead of minimizing the cost function (\(g\)) as described in Equation (12), the focus shifts to the cost function (\(g_{min}\)) specified in Equation (15).  
In this study, the parameter value referenced in \(\lambda_{min}\) varied between 0, 0.5, 1.5, and 2. To obtain the waveforms of the control schemes, real-time analyses were implemented using the OPAL-RT platform.
## 5 Discussion
The main current limitation of the proposed Kalman filter approach is the relatively high computational cost due to the FIR model used, which can become a problem when processing many hours of recordings (as in ambulatory monitoring) in numerous subjects. Future efforts can be devoted to optimizing the approach via more efficient methods, using for example an autoregressive model in the construction of the state space model. Also, an optimal tuning of process and observation covariance matrix can be explored to improve the estimation. Other variations in the construction, e.g., addition of a random walk term or an extended Kalman filter could be investigated as well to encompass non-linear implementations of the accelerometer signal to glottal airflow signal transformation. Flowestimation can also be improved by considering the backward Kalman smoother algorithm, at the expense of an increase in the computational burden and the memory requirements. New model strategies suitable for Kalman filter and smoother would be explored in the future [66].
## 3 Results
### Empirical Observations and Analysis  
The R37 slopes exhibit inconsistent outcropping thickness and geometry with homogeneous and heterogeneous slopes at various locations. Slopes along the R37 were divided into sections A and B, as observed from Figures 6 and 7. The empirical observation at Figure 6a,b envisages a tectonically deformed environment, as exhibited by the folding, representing compressional stress. In Figure 5, the deformed rock slope appears intact and homogeneous without any notable failure occurrence. On the other hand, the folded rock slope in Figure 6b exhibits a brittle nature, with notable fragmentation at the hinge points of the fold.  
Figure 6: Field observation along Section 1: (**a**) intact folding strata, (**b**) folding strata with brittle-like behaviour, (**c**) slope with a dyke intrusion and debris evidence of failure, and (**d**) stratified rock layers.  
Figure 6c depicts a heterogeneous slope with an intrusion of a dyke. The literature has revealed that dyke intrusions follow paths of lower mechanical integrity (strength) on the rock mass [32]. Evidently, the region of contact between the dyke and sandstone exhibits some debris of rock slope failure regressing away from the road. Evidence of stratification is clearly observed in Figure 6d; the various rock layers exhibit variations in thickness. Moreover, the contact between the layers is infilled with various materials, ranging from unconsolidated soil to quartz. It was also observed that the strata dipped inwards from the slope face, an observation linked to toppling susceptibility in the literature. The dynamic of this observed failure will further be validated with limit equilibrium and kinematic methods.  
Figure 7 represents the second section of the engineered road slopes along the R37 road. This section, contrary to the initial section described above, does not consist of any intrusions or tectonically induced deformation. However, sedimentary stratification is noted across the entire section, as represented by Figure 7c. In Figure 7a, a vertical slope can be observed with boulders and fragments of rocks on its toe, suggesting a fall mode of failure. In Figure 7b, a sliding mode of failure is observed, with the slope exhibiting one stable zone and another unstable zone. Furthermore, the slope angle of the stable section is vertical, while the unstable zone exhibits only a subvertical angle, allowing for the material to slide down instead of falling. Such a sliding failure is observed in the sub-vertical slope in Figure 7d. A progressive minor occurrence of planar failure is noted, sliding in the direction of the slope face towards the road.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 3 Performance Analysis  
Theoretically, the amplitude of the command signal is the desired predefined value, while the phase of the command signal is unconcerned. However, in practice, for profiling the disturbance signal, while meeting the requirement for limiting the control effort simultaneously, the predefined command signal should be set to be in phase with the disturbance signal. Therefore, the PSC-FXLMS algorithm uses the FFT to estimate the phase of the modeled disturbance signal as an essential parameter for the command signal. In practical applications, an imperfectly modeled disturbance signal may lead to phase error between the command signal and the real disturbance signal. Therefore, the influence of the phase error between the command and disturbance signals on the convergence is explained in detail in the following section.
## 3 Results and Discussion
### Effects of VIDD on Vehicle Speeds in Middle of Long Downhill Tunnel  
#### 3.2.1 Effect on Vehicle Speeds at Different Observation Points  
The maximum and minimum speeds of vehicles controlled by different types of VIDD are shown in Figures 14 and 15, respectively. Figure 14 shows that the slash-type VIDD had no obvious effect on the maximum vehicle speeds in the middle of the tunnel, especially for the 15\({}^{\circ}\) and 40\({}^{\circ}\) slash lines; the maximum vehicle speeds were almost the same as those without the VIDD. For the fold-line VIDD, 90\({}^{\circ}\) fold lines decreased the maximum vehicle speeds significantly from point 2 to point 5. However, the 60\({}^{\circ}\) and 120\({}^{\circ}\) fold lines actually increased the maximum vehicle speeds, which shows that these two types of VIDD are not suitable for controlling the maximum vehicle speeds in the middle of the tunnel. For the alternating yellow and white VIDD, all three types generally reduced the maximum vehicle speeds, with the 4Y4W VIDD decreasing the maximum vehicle speeds at all observation points. Comparison shows that the 90\({}^{\circ}\) fold lines reduced the maximum vehicle speeds by more than did the 4Y4W VIDD.  
From Figure 15, each type of VIDD resulted in some decrease in the minimum vehicle speeds in the middle of the tunnel. This is not what we expected, and speeds that are too low may cause a large difference between the maximum and minimum speeds of vehicles, as well as decrease the efficiency of vehicles passing through the tunnel. However, with the 90\({}^{\circ}\) fold-line VIDD, the vehicles tended to accelerate and recover their speed from point 4 to point 5.  
The differences between the maximum and minimum vehicle speeds with and without a VIDD are given in Table 2. The smaller the difference between the maximum and minimum vehicle speeds, the better the traffic safety in the tunnel. Table 2 shows that the difference between the maximum and minimum vehicle speeds with no VIDD was 47.8 km/h. Among these nine types of VIDD, only the differences between the maximum and minimum speeds of vehicles controlled by the 90\({}^{\circ}\) fold-line and 4Y4W types of VIDD are smaller than that of vehicles with no VIDD, these being reduced by 3.6 and 0.8 km/h, respectively. Therefore, the 90\({}^{\circ}\) fold-line and 4Y4W types can decrease the dispersion of the vehicle speeds.  
The average speeds of vehicles at every point controlled by different types of VIDD are shown in Figure 16. As can be seen, almost all of the average speeds of vehicles controlled by these nine types of VIDD are smaller than that of vehicles with no VIDD, and this tendency shows that a VIDD acts to reduce the speeds, although it can, on occasion, increase the maximum speeds somewhat. Except for the 55\({}^{\circ}\) slash-line type of VIDD, all the other types resulted in decreased standard deviation of speeds at every point, indicating enhanced driving stability.  
Figure 14: Maximum speeds of vehicles controlled by VIDD: (**a**) slash lines; (**b**) fold lines; (**c**) alternating yellow and white lines.  
Figure 15: _Cont._
## 3 Traffic Management Model
### Bat Algorithm (BA)  
The Bat algorithm emulates bat echolocation, whereby bats use echolocation to distinguish between prey and physical boundaries. Bats, additionally, identify differences in other ways, such as by flying in an arbitrary motion with speed \(v_{i}\) at position \(x_{i}\) with a recurrence of \(f_{min}\), turbidity \(A_{0}\), and shifting wavelength \(\lambda\), in order to look for prey.  
Figure 2: Deep neural network (DNN) architecture.  
The bats alter the recurrence naturally, based on the radiated pulse, and this changes the pulse discharge rate \(r\in[0,1]\) based on the closeness of the fitness function. In general, the loudness varies between large \(A_{0}\) (positive value) and a minimum \(A_{min}\) (constant value).  
IoT-BA operations are defined in terms of their frequency, velocity, position, emission pulse rate, and loudness. These terms help the bats to search for prey in a D-dimensional space.  
_Virtual Bat Movement_ is obtained after the random initialization and the new position and velocity are updated in a regular time-step \(t\), as below  
\[f_{i}=f_{\text{min}}+(f_{\text{max}}-f_{\text{min}})\times\beta \tag{5}\]  
\[v_{i}^{t+1}=v_{i}^{t}+(x_{i}^{t}-x_{*})\times f_{i} \tag{6}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{7}\]  
where:  
\(f_{i}\) is the frequency;  
\(v_{i}^{t}\) is the velocity;  
\(x_{i}^{t}\) is the position;  
\(A_{i}^{t}\) is the loudness;  
\(r_{i}^{t}\) is the emission pulse rate;  
\(x_{*}\) is the global best;  
\(F_{min}\) is the minimum frequency;  
\(f_{max}\) is the maximum frequency.  
For a local search, the solution is chosen based on the current best solution, and then the updated solution is locally produced for each bat based on the following condition  
\[x_{new}=x_{old}+\varepsilon*A^{t} \tag{8}\]  
where:  
\(\varepsilon\) is the arbitrary vector;  
\(A^{t}\) is the normal commotion at step \(t\).  
The global solution is updated with the best fitness function obtained from \(N\) bats when it is better than the previous best\(f\) (\(x^{*}\)).  
_Loudness and Pulse Emission_: \(A_{i}^{t}\) and \(r_{i}^{t}\), respectively, tend to reduce and increase based on the rationalization of a new solution. This means the bats are moving in the correct direction towards their prey. This is formulated as shown below  
\[If(rand(0,1)<A_{i}^{t}\&\&f(x_{i})<f(x)) \tag{9}\]  
\[f(x)=f(x_{i}) \tag{10}\]  
\[A_{i}^{t+1}=\alpha A_{i}^  
\(a_{i}\) is the actual value;  
\(d_{i}\) is the estimated value.  
The lower the MAPE, the better the optimization of parameters of DNN.  
BA predicts the value of critical strength parameters in the VANET congestion detection process based on its operating of parameters and conditions. DNN uses the trained BA prediction to determine the values of process parameters that result in the optimal value of the strength parameters in various operating conditions.