esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Setpoint Tracking Performance  
An experiment was conducted to evaluate the ANPID controller's setpoint tracking performance. Starting from a stable equilibrium point [0.144, 0.886]\({}^{T}\), a square wave signal, alternating between 0.886 and 4, was used as the setpoint until \(t\) reached 250. The resulting responses, along with those obtained by the APID controller, are presented in Figure 9. Figure 10 shows the system's output responses to a series of downward and upward setpoint changes starting from another stable equilibrium point [0.765, 4.705]\({}^{T}\), alternating between 1 and 4.705. The results demonstrate that both adaptive controllers, even starting with the challenging initial response, effectively stabilize the system response around the setpoint, achieving good control performance. The ANPID controller exhibits better setpoint tracking performance with less overshoot compared to the APID controller.  
Figure 10: Output responses to a series of downward and upward setpoint changes.  
Figure 9: Output responses to a series of upward and downward setpoint changes.  
Both methods demonstrate progressively improved responses over time, confirming the effectiveness of their respective adaptation mechanisms. However, comparative analysis reveals that the proposed method achieves superior performance with reduced overshoot and faster transient decay. The adaptation process of the ANPID controller is shown in Figure 11. Figure 11 illustrates the saturated control input, Figure 11 depicts the sensitivity of the output to changes in the control input, and Figure 11 shows the adaptation of the NPID controller gains.  
To quantify the performance of the two methods, Tables 2 and 3 summarize the aforementioned performance indices for the responses to the last two setpoint changes. Additionally, the total performance of both methods was assessed using the IAE calculated over the entire simulation period for two scenarios. For a series of upward and downward setpoint changes, the IAEs of the ANPID and APID controllers are 19.213 and 30.725, respectively. For the other scenario, their IAEs are 19.883 and 32.117, respectively. The superiority of the proposed method can be confirmed again through quantitative comparison.  
When the output of the process stayed at \(y_{r}=4\), stepwise disturbances of \(d_{1}=-0.2\) and \(d_{2}=-0.2\) were injected to the control input at \(t=50\). The responses are plotted in Figure 15. As can be seen from the two figures, the outputs of both methods exhibit rapid recovery from disturbances and remains at the setpoint.  
Table 4 summarizes the calculated values of \(M_{peak}\), \(t_{ry}\), and \(IAE\). A quantitative analysis of these results reveals the effectiveness of both methods in rejecting disturbances.
## 3 Results and Discussion
### Characterization of Nanocontainers
#### 3.1.2 Thermogravimetric Analysis and Differential Scanning Calorimetry  
The TGA-DSC diagrams of Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT are depicted in Figure 2. Regarding the TGA diagram, it may be remarked that there was a weight loss of \(\sim\)8% from 50 to 180 \({}^{\circ}\)C that is assigned to the desorption of acetone, ethanol and physically adsorbed water (free and physisorbed water) [30]. The second weight loss \(\sim\)10% between 180 and 260 \({}^{\circ}\)C is ascribed to oxidative degradation of the ATT that is on the nanocontainers' shell. The third sharp weight loss \(\sim\)22% from 260 to 520 \({}^{\circ}\)C is attributed to the oxidative degradation of the ATT that was inside the nanocontainers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **C** & **O** & **Na** & **Si** & **S** & **K** & **Ti** & **Ce** \\ \hline (wt.\%) & 24.11 & 2.93 & 0.24 & 0.48 & 4.16 & 0.28 & 60.17 & 7.63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tabulated values of the EDS analysis of the Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT.  
Figure 1: TEM images of Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers loaded with ATT.  
The broad exothermic peak from 260 to 520 \({}^{\circ}\)C in the DSC diagram corresponds to the oxidative degradation of the ATT. Taking into account the aforementioned results, it should be mentioned that the Ce\({}_{x}\)Ti\({}_{y}\)O\({}_{z}\) nanocontainers were \(\sim\)32 wt.% loaded with ATT.
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Performance Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Achieved Apoapsis Altitude Bin** & **Percentage** \\ \hline \(\pm\)400 km of target & 88.1\% \\ \(\pm\)600 km of target & 96.8\% \\ \(\pm\)800 km of target & 99.5\% \\ \(\pm\)1000 km of target & 99.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics for Mars aerocapture orbit apoapsis altitude.
# Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols
## 3 Analysis and Results under the Simulation Tool
### Extrapolation of the Experimental Scenario  
The experiments carried out with 22 sensors were then expanded to 100 sensors and 200 sensors. This is in order to predict the results of the energy model by means of an event-based simulator, programmed in C ++ and previously tested in [10].  
Next, it is interesting to analyze how the energy model behaves when the network begins to grow in its number of nodes. Table 7 shows three representative protocols out of the six we have studied in this work. Two predominant types of energies were analyzed based on the scalability of the network, from 22 to 300 nodes. Simulations were run for a full day and for nodes which parameters are detailed in Table 3.  
Results in Table 7 show the two predominant energies for each network and for each of the three protocols studied. Here, we highlight the utility of the proposed model, which breaks down the total energy into the basic energy types of a node. This information becomes relevant when analyzing the skeleton of what is happening in the node with respect to performance metrics such as overhead, collisions, packet loss, interference, complexity of handling neighbor or routing tables, etc.  
Analyzing the data, we observe that, when the network is large (about 300 nodes), the AODV and PEGASIS protocols present predominantly CSMA and TX energies. This may suggest that PEGASIS is not a protocol for networks that are too large because the route chain to reach the destination node becomes complex and packets can be lost. Below the 200 nodes, the three protocols under evaluation present the energies of RX and TX as preponderant. Visualizing in detail the network of 100 nodes, the difference between AODV and MPH is 67 % in favor of MPH, and the difference between MPH and PEGASIS is 58% in favor of MPH for the reception energy. This is an example that shows that MPH has a hierarchical topology and that the neighbor tables of the nodes become more manageable thanks to the periodicity of their update. The AODV protocol has the highest energy expenditure and this may be due to the fact that it has a greater number of control packets and if the routes expire or become obsolete, the nodes must start the entire route request process again. In this case, when the networks increase in size, the proposed energy model is very useful because, according to the characteristics of the network, it can establish weak points of behavior and detect possible gaps in energy loss. For example, if CSMA power is increased too much, this may be showing that the communication channel is continuously busy or that this is combined with multiple packet retransmissions, which implies that the processing time increases and the performance of the device is impaired. Then, the breakdown of energies performed by the model can detect abnormal increases or decreases in the behavior of the nodes, as occurs, for example in the PEGASIS protocol when the network is very large.
# Efficiency Improvement Strategy for Multiple Operating Points in Doubly Fed Magnetic Geared Motor
## 4 Efficiency Improvement Strategy  
As presented in Section 3, in the DFMGM, the iron loss characteristics are varied with the frequencies of the inner and outer windings. Thus, the efficiency, which depends on the iron loss, is also changed, and the DFMGM is operated with the frequency combinations which obtains the maximum efficiency. Furthermore, at the multiple operating points, if the DFMGM is operated with the frequency combinations with the maximum efficiency, the overall operating efficiency is improved as compared with that of the case with only one frequency control sequence. Figure 8a shows the minimum iron losses at each operating point using all frequency control sequences of Types I, II, and III, which means that the DFMGM is operated with reduced iron losses using the frequency combination selection according to the operating point. In addition to the iron loss, the copper loss map is shown in Figure 8b, and the maximum efficiency map is presented by the minimum iron loss map, copper loss map, and the output as shown in Figure 8c. In the high-speed and high-torque region, the maximum efficiency is 93.1%, which shows that the efficiency increased as compared with 93.0% and 92.7% of the maximum efficiencies in Type II and Type III, respectively. To see the efficiency improvement over the whole operating region, the difference of the efficiency maps between Type III and Type I is presented in Figure 8d. In high-speed and low-torque region, maximally, there is a difference of 3.5%, and Type III has higher efficiency as compared with those of Type I. In contrast, in the high-speed and high-torque region, the efficiencies of Type I are higher than those of Type III, and, maximally, there is a efficiency difference of 1.2%. To sum up, these tendencies imply that efficiency improvement can be obtained by solely changing the frequency of the inner and outer windings, which are inherent characteristics of the DFMGM and a distinct difference from the general synchronous machine.  
Figure 8: Loss and efficiency maps in the constant torque region according to the winding frequency when the amplitude of the inner winding current is adjusted and the outer winding current is fixed at the rated current (168 Arms). (**a**) Minimum iron loss map; (**b**) Copper loss map; (**c**) Maximum efficiency map; (**d**) Efficiency difference between Type III and Type I.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Application Direction of Blockchain-Enabled Federated Learning
#### 4.2.6 Transmission Mechanisms  
Transmission mechanisms refer to the system of network transmission and network resource allocation. Due to the limited communication bandwidth and the participation of a large number of users, federated learning can cause transmission barriers. However, blockchain-based federated learning can solve these two problems. Blockchain architecture can ensure the security and efficiency of data transmission over the network. Therefore, the transmission performance of federated learning based on blockchain is greatly improved.  
One transmission obstacle problem is network overload, and the method to minimize network load is to make the model converge quickly and stably [51]. By virtue of its decentralized nature, blockchain stores data in a decentralized manner while reducing network overload. Thus, the BFL framework formed by federated learning under the support of blockchain can accelerate the model convergence and reduce the communication cost. [PERSON] et al. [113] designed a CREAT algorithm and applied the auxiliary compression algorithm to content cache or predictive cache files to alleviate the network overload problem. Furthermore, the blockchain stores all learning models for complete transmission, so local models cannot be adjusted, and network overload can be mitigated by accurate node identification of the blockchain [114].  
In addition, to solve the network resource allocation problem, digital bilateral networks can be utilized to solve the optimal unified time problem of collaborative federated learning and local model update verification. Therefore, with the support of BFL, federated learning can utilize the bilateral network to update the local model to reduce the waste and insufficiency of network resources.  
It is worth noting that the advantages of edge computing can be applied to BFL, combined with the outstanding features of blockchain, to better empower federated learning. [PERSON] et al. [22] combined edge computing and blockchain to propose a secure data transfer method. [PERSON] et al. [115] proposed a digital twin wireless network (DTWN), and comprehensively considered the problems of digital twin association, datasets, and bandwidth; balanced the training accuracy and time cost of the scheme; and optimized the model.
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 2 Materials and Methods
### Design of the Specific Process for Seeding Picking and Throwing  
The specific process of seedling extraction and planting is illustrated in Figure 3 and is described as follows: (a) The plug seedling reaches the predetermined extraction position after being lifted. At this stage, the electric push rod guides the seedling extraction arm to approach the extraction point. The distance between adjacent sliders on the variable-distance slide table is set to 42 mm. The air control system engages the flexible clamping fingers of the air claw to securely grip the plug seedling. (b) The air claw fingers maintain their grip, while the left and right electric rotary tables rotate synchronously, driving the extraction arm to move the plug seedling away from the tray. During this process, the position of the electric push rod and the variable-distance slide table sliders remain unchanged. (c) The electric push rod retracts horizontally while maintaining the rotary table's angle, causing the extraction arm to move backward. The variable-distance slide table sliders are controlled to spread apart, increasing the distance between adjacent clamping fingers to 80 mm, aligning with the spacing of the rotating seedling cups. (d) The electric push rod continues to retract horizontally, guiding the extraction arm to the predetermined planting position. The rotary table is controlled to rotate counterclockwise, positioning the plug seedling above the seedling cup. At this point, the six corresponding claws align precisely with the centers of the respective seedling cups. (e) In the seedling-dropping state, the clamping fingers are controlled to open, allowing the potted seedling to descend into the seedling cup under the influence of gravity. Simultaneously, the seedling delivery system transports the loosened seedlings from the lower row of the tray to the extraction position. (f) The seedling cup delivers the potted seedling to the planting device. Meanwhile, the extraction arm, driven by the rotary table, rotates clockwise back to the initial extraction angle. The clamping fingers are then controlled to close, adjusting the distance between them to 42 mm to match the spacing of the plug tray cells. Subsequently, the electric push rod moves the extraction arm horizontally toward the extraction position, completing the entire seedling extraction and planting operation.
## 3 Results and Discussion
To further investigate the reasons for the excellent electrochemical performance of poly(S-MVT), we employed DFT calculations to study the HOMO-LUMO bandgap energy changes of poly(S-MVT). As Figure 7 shows, with the increase of polysulfide chains (-S8-) connected to MVT, the bandgap values decreased gradually. In addition, the protonated N on the MVT reduced the bandgap even more, which is much lower than that of elemental sulfur, thus making the poly(S-MVT)'s rate performance better [37,38]. This can be demonstrated by the electrochemical impedance spectroscopy (EIS) results. As Figure 8 shows, Nuquist curves of fresh S cathode and poly(S-MVT) (15% MVT) cathode consist of semicircles in the high frequency region and straight lines in the low frequency region. The semicircle diameter of the Nuquist curve represents the charge transfer resistance. It is obvious that the charge transfer resistance of poly(S-MVT) (15% MVT) cathode is much lower than that of S cathode, indicating better electronic conductivity and kinetics of the poly(S-MVT) (15% MVT). Hence, the stable cycle performance and excellent rate performance of poly(S-MVT) may be ascribed to the stable chemical structure linked by C-S bond, the protonated nitrogen atoms in thiazole ring, and the porous structure. The exceptional protonated structure, low bandgap, and promising electrochemical performance provides a new choice for the design of sustainable cathode materials for Li-S batteries. Figure 7: HOMO–LUMO bandgaps of elemental sulfur (S8), MVT, and poly(S-MVT)s with different polysulfide chains (-S8-). The band gap of MVT is 4.66 eV, which decreases greatly as a result of the polysulfide chain attachments.
## 2 Cutting Test
### Analysis of the Effect of Cutting Parameters on Surface Roughness
#### 3.2.4 Effect of Ultrasound Amplitude on Surface Roughness  
The bar graph of the variation of the machined surface roughness of the workpiece with ultrasonic amplitude (\(A\)) for the parameters of Table 4 is given in Figure 13. The parameters of cutting speed of 5 m/min, feed of 24 m/rev, depth of cut of 2 m, and a tool nose radius of 0.5 mm were selected. It can be seen from the figure that the value of surface roughness of the workpiece increases with the increase of ultrasonic amplitude in both directions. The surface roughness was minimized when the ultrasonic amplitude was \(A_{y}\) = 3.0 m and \(A_{z}\) = 0.8 m, and the surface roughness was 92 nm. When the ultrasonic amplitude was increased to \(A_{y}\) = 7.5 m and \(A_{z}\) = 2.6 m, the surface roughness increased dramatically to 188 nm.  
Figure 14 shows the 3D surface roughness for four different ultrasonic amplitude parameters. When the ultrasonic amplitude is \(A_{y}\) = 3.0 m and \(A_{z}\) = 0.8 m, the surface is smooth and basically free of defects, and when it is increased to \(A_{y}\) = 3.8 m and \(A_{z}\) = 1.1 m, fine vibrational lines can be seen on the surface, and some pits appear. When increased to \(A_{y}\) = 5.6 m and \(A_{z}\) = 2.0 m, it is obvious that there are chatter marks, and the height of the residual between the two adjacent elliptical vibration cycles increases so that the surface roughness increases, and the surface quality deteriorates; with the amplitude of \(A_{y}\) = 7.5 m and \(A_{z}\) = 2.6 m, we can see dense chatter marks, the height of the residual is more obvious, and the surface roughness increases sharply.  
Figure 13: Surface roughness change with ultrasonic amplitude in UEVC.  
Ultrasonic elliptical vibratory cutting is an intermittent cutting method, and changing the ultrasonic amplitude affects the contact time between the tool and the workpiece surface. As the ultrasonic amplitude increases, the surface roughness increases at the same time, and the residual height on the workpiece machining surface between two adjacent ultrasonic elliptical vibratory cuts increases due to the increase in the amplitude in the direction of the depth of cut, resulting in a deterioration of the surface topography. When cutting nickel-based high-temperature alloys, although the ultrasonic amplitude gives a great advantage in improving the surface flatness of the workpiece, a larger ultrasonic amplitude has a greater effect on the surface buildup of the workpiece, which will cause the cutting system to be overloaded; the burr will increase, and the flatness of the surface will deteriorate, which will have a deleterious effect on the tool and the surface.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 4 Simulation Results
### Vehicle Handling  
The vehicle handling is evaluated for a steady speed that is similar to the previous frequency domain analysis. It was assumed that the vehicle handling is related to the pitch velocity of the body and it is evaluated based on the standard deviation of this state variable, as follows:  
\[J_{rh}=std(w_{sp})=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}[w_{sp}(i)-\overline{w_{sp }}]^{2}}, \tag{33}\]  
where \(N\) is the number of samples and \(\overline{w_{sp}}\) denotes a mean value of \(w_{sp}\) calculated over \(N\).  
Similarly to the road holding index, the normalized value of the vehicle handling index \(J_{rh}\) was calculated and denoted as \(J_{rh,n}\). Here, the results obtained for zero MR damper current were also  
Figure 19: Comparison of road holding for different suspension settings: passive, separate and coupled Skyhook control for vehicle longitudinal velocity of 10 ms\({}^{-1}\): (**a**) front wheel during braking, (**b**) rear wheel during acceleration.  
assumed as the reference case. It should be noted that, the lower value of the vehicle handling index, the better the vehicle handling and body pitch movements are suppressed.  
Figure 20 presents the values of normalized vehicle handling index for vehicle longitudinal velocity equal to 5 ms\({}^{-1}\) and 10 ms\({}^{-1}\). It can be noticed that low current controlling the MR damper offers little improvement in comparison to zero current. This can be explained by the fact that the increase in suspension damping counteracts the pitching movement of the body. However, a further increase of current causes excessive stiffening of the entire vehicle suspension, as shown in Section 4.3, and significantly deteriorates its vibration damping.  
Skyhook control, which is focused on the body vibration control, is also recommended for the suppression of body pitch motion. Even though Skyhook-1 DOF is dedicated to separate control of suspension parts rather than body vibration modes, as done by Skyhook-2 DOF, it offers comparable or slightly better results. For a vehicle longitudinal velocity of 5 ms\({}^{-1}\), this is a 33% improvement in vehicle handling, while the Skyhook-2 DOF offers 31%. However, it can be expected based on Figure 20 that a further increase of control parameter of the Skyhook-1 DOF and Skyhook-2 DOF would lead to the comparable results.  
Figure 20 also presents the results of vehicle handling for vehicle longitudinal velocity of 10 ms\({}^{-1}\). Here, a different order of algorithms can be noticed, as Skyhook-2 DOF offers slightly better results, i.e., 21% improvement when compared to the 20% offered by Skyhook-1 DOF.