esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 4 Design of Control Strategies
### Dynamic Control  
Robots require high-performance controls to ensure speed and precision. Advanced materials used in manufacturing allow robots to improve movement significantly. However, the dynamic characteristics of robots, such as inertia and friction, often unknown, complicate the task of aligning the actual trajectory with the desired one. Dynamic control aims to ensure that the robot follows the desired trajectory by analyzing its dynamics. Typically, this control is performed in the joint space to manage joint trajectories. If the robot interacts with the environment, Cartesian (task) space control is preferable, controlling the end-effector's trajectory.  
Advanced control techniques include the following:  
* Adaptive Control: adapts to changes in the robot's dynamics during operation.
* Fuzzy Control: addresses difficulties in creating mathematical models.
* Predictive Control: uses process models to generate control signals, typically requiring high computational effort but useful for multi-input, multi-output systems.  
A basic problem in robot control is to make it follow a planned trajectory. There are many issues associated with path planning, such as obstacle avoidance and ensuring that the planned trajectory does not exceed the voltage and torque limitations of the robot's actuators. Computed Torque Control is a classic and fundamental control technique that produces a family of easily understandable control schemes that work well in practice. In addition, the robot is a rigid and well-known system, which makes the design of the controllers based on an established model feasible. Simulating the robot controllers on a computer allows for verifying the effectiveness of the control scheme for any proposal before actual implementation, since in most practical situations, the controllers are implemented in microprocessors associated with the system's electronics. One way to classify the robot control schemes is to divide them into \"similar to Computed Torque Control\" or \"similar to Non-Computed Torque\". Computed Torque type controls are mentioned in various bibliographies on robust control, adaptive control, learning control, and others [1]. Table 4 shows a comparison of the controllers mentioned above.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & **Weaknesses** & **Strengths** & **Complementary Characteristics** \\ \hline \multirow{6}{*}{Computed Torque (Adaptive Control)} & \multirow{6}{*}{None observed.} & Guarantees global asymptotic stability and, in practical terms, calculates the exact amount of energy required by servomotors to execute a planned trajectory. & \multirow{6}{*}{Applicable to any robot with \(n\) DoF.} \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Controller comparison [13; 28; 29].
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 2 Insulator Identification and Location Based on Mask R-CNN  
In general, catenary insulators occupy a small area in the image, which puts forward high requirements for the accuracy of deep learning algorithms. Table 1 compares and analyzes the existing state-of-the-art deep learning algorithms, including the single-stage object detection algorithms SSD (Single Shot MultiBox Detector) and YOLO (You Only Look Once) [17], and the two-stage object detection algorithms Faster R-CNN and Mask R-CNN.  
The SSD algorithm and YOLO algorithm have relatively low accuracy in small target detection [18], while the Faster R-CNN algorithm adopts the quantitative rounding method in an ROI Pooling layer, which will produce feature dislocation and errors in small-target detection that needs to be accurate to pixel level. Therefore, considering these aspects, the Mask R-CNN algorithm is selected in this paper.  
The Mask R-CNN algorithm was first proposed in 2017 [19] and is mainly used in the fields of object detection and instance segmentation. It is improved from the previously proposed Faster R-CNN algorithm. On the one hand, bilinear interpolation is used in the ROI Align layer to solve the problem of misalignment. On the other hand, a pixel-level instance segmentation mask branch is added to achieve pixel-level object detection. Finally, the network loss is unified in target detection, thereby improving the accuracy, so it has certain advantages in small target detection [20]. The Mask R-CNN algorithm is used to propose the region of the insulator in the image, which can narrow the target range with high precision before detecting the damaged state of the insulator, and removes the interference caused by other catenary structures to the subsequent Zernike moment feature calculation.
# Characteristics of Electric Field Induced by Oscillating Metal Underwater Vehicle
## 3 Analysis of the Characteristics of the Induce Electric Field in Metal Vehicle Motion  
Based on the theoretical model in Section 2, it can be obtained that the interference electric field output in the case of uniform motion or shaking of the measurement system has the following characteristics:  
When moving at a constant speed, it can be seen from Equations (3)-(5) that the induced electric field interference output by the measurement system is a static signal. The magnitude of the static electric field is directly proportional to the speed of movement and the size of the geomagnetic field, and the magnitude of each component is related to the direction of movement and the size of the geomagnetic field component.  
The three components of the induced electric field simulated by taking the geomagnetic field intensity \(F=45,000\) nT, magnetic inclination angle \(I=\pi/3\), magnetic declination \(\gamma=\pi/18\), and motion speed \(\mathbf{v}=(5,0,0)\), \(\mathbf{v}=(10,0,0)\), \(\mathbf{v}=(20,0,0)\) and \(\mathbf{v}=(50,0,0)\), respectively, are shown in the figure below. According to Figure 4, the induced electric field at this speed is a static electric field, and its magnitude will change with the change in speed and geomagnetic field in the navigation area. When the speed reaches \(50\) m/s, the magnitude can reach \(2\) mV/m.  
Taking the geomagnetic field intensity \(F=45,000\) nT, the magnetic inclination angle \(I=\pi/3\), and the magnetic declination angle \(\gamma=\pi/18\) and considering the actual situation, the maximum angle of shaking of the high-speed projectile after stability is about \(0.1^{\circ}\), and the shaking speed is \(0.005^{\circ}\)/s [18]. The shaking speed and angle of pitch, roll, and heading are consistent. When the shaking angle of the navigation body is \(0.1^{\circ}\), the simulation results of the induced electric field are shown in Figure 5, itcan be seen from Figure 5 that the interference caused by the high-speed projectile shaking is in the nV/m range and thefrequency is extremely low. At this shaking intensity, it will not affect the detection of motion targets.  
Assuming in the ultimate shaking state [20], where the shaking speed is 0.5\({}^{\circ}\)/s and the maximum angle is still 1\({}^{\circ}\), and the simulation results are shown in Figure 6. From the figure, it can be seen that the maximum peak-to-peak noise caused by the shaking of the high-speed aircraft is about 180 nV/m, and the frequency is related to the shaking angular velocity and amplitude. At this shaking intensity, it still does not affect the detection of the vehicle target.
# Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
## 2 Materials and Methods
### Adaptive Weighting Schemes  
A third way to deal with severe time warping is to select transition weights adaptively during the run time rather than using a fixed set of transition weights tuned on a training set. Let \(x_{0}\), \(x_{1}\),..., \(x_{N-1}\) and \(y_{0}\), \(y_{1}\),..., \(y_{M-1}\) be the two sequences that we would like to align, where \(N\leq M\). If we use standard DTW with allowable \((\Delta x,\,\Delta y)\) transitions \(\{(0,1),\,(1,0)\}\) and corresponding weights \(\{w_{1},\,w_{2}\}\), then we can adaptively set \(w_{1}=1\) and \(w_{2}=\frac{M}{N}\) for each pair of sequences to be aligned. This weighting scheme ensures that both axes contribute the same weighted Manhattan distance cost from \((0,0)\) to \((N-1,M-1)\), regardless of the values of \(N\) and \(M\). (Note that if \(w_{1}=w_{2}=1\), the axis along the shorter sequence contributes less to the total path cost simply because it is shorter in length.) This prevents one axis from dominating the total path cost. Similarly, we can use standard DTW with allowable \((\Delta x,\,\Delta y)\) transitions \(\{(0,1),\,(1,0),\,(1,1)\}\) and corresponding weights \(\{w_{1},\,w_{2},\,w_{3}\}\), where we can adaptively set \(w_{1}=1\), \(w_{2}=\frac{M}{N}\), and \(w_{3}=1+\frac{M}{N}\). This preserves the property that both axes contribute equally to the total path cost, and it allows \((1,1)\) transitions as well.
## 3 Results and Discussion  
### Characteristics of GO/AMT Nanohybrids  
The morphology of GO nanosheets was characterized by AFM and TEM (Figure 2). It can be seen from the AFM image that the thickness of the GO layer is about 1 nm, which is consistent with the previous results [18]. The TEM image further demonstrates the layered structure of GO.  
Figure 2: Morphological characterizations of GO and GO/AMT: **(a)** atomic force microscopy (AFM) height image of GO, **(b)** transmission electron microscopy (TEM) image of GO, and **(c)** TEM image of GO/AMT.  
Subsequently, FT-IR and Raman spectroscopy were utilized to compare the difference between GO and GO/AMT nanohybrids (Figure 3). Figure 3a shows the typical FT-IR spectra of GO, GO/AMT, and AMT. In the spectrum of GO, the peak at 3420 cm\({}^{-1}\) is related to the -OH vibration stretching. The peaks at 1720, 1620, and 1142 cm\({}^{-1}\) are corresponding to the carboxyl C=O stretching, C=C stretching of the aromatic ring, epoxy C-O stretching, and epoxy/alkoxy C-O stretching vibration, respectively [43; 44]. The spectrum of GO shows a peak at 1720 cm\({}^{-1}\) that is assigned to the carboxyl C=O stretching, which nearly disappears in the spectrum of GO/AMT. In addition, the new peaks of GO/AMT spectrum at 1580, 1101, and 803 cm\({}^{-1}\) correspond to N-H bending, C-N stretching vibrations, and C-H vibrations, respectively [45]. It can be seen that the peaks related to AMT still exist in the spectrum of GO/AMT.  
Figure 3b shows the Raman spectra of GO and GO/AMT, in which a significant increase in the ratio of \(\rm{I_{D}}\) to \(\rm{I_{G}}\) band intensity can be seen. We suggest that the ratio increase was due to the distortion of the graphene structure caused by the special structure of AMT. Based on the above FT-IR and Raman characterizations, it can be concluded that AMT was successfully attached onto the surface of GO through the diimide-activated amidation reaction.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 6 Locomotion: Mobility Design
### Swimming Robot  
Recently, a swimming robot using DEA for their locomotion actuator was also suggested, which was thought of as challenging because DEAs usually use high operating voltage. Therefore, operating in the water requires a reliable insulation method. The first swimming robot using a DEA was suggested in 2016, imitating the movement of a jellyfish [113]. The robot has an air chamber inside the bell-shaped structure, which is composed of a 3D-printed cylinder and dielectric elastomer membrane (VHB 4910). So, when a voltage is applied to the DEA, the air chamber is inflated because of the expansion of the DE membrane. This actuation ejects the water inside the bell, which propel the robot while simultaneously creating buoyancy force that is greater than the weight of the robot.  
Another research group fabricated a jellyfish robot using a unimorph DEA configuration, and a fish robot with a bimorph DEA [114]. In these robots, reliable operation is guaranteed by insulating a high-voltage electrode with a silicone layer (CF19-2186) which also acts as the DE layer. A speed of 8 mm/s is accomplished with the fish robot of 120 mm length, and a speed of 1.5 mm/s is accomplished with the jellyfish robot of 800 mm length. The same research group also analyzed the movement of the fish robot based on the Euler-Bernoulli beam theory [115]. The fabricated fish robot is shown in Figure 11a. Using the model, the natural frequency of the robot is calculated with a fixed head  
Figure 10: DEA driven crawling robot, flapping robot and jumping robot: (**a**) Crawling robot [106]; (**b**) Flapping robot [108]; (**c**) Jumping robot [110].  
boundary condition and compared with the experimental result. The bimorph configuration of a DEA is also used in an autonomous underwater vehicle (AUV) [116]. In this robot, all components such as a battery, power converter, and controller are included in the robot so that the robot is untethered and stand-alone.  
A frog-inspired swimming robot was developed using a dielectric elastomer minimum energy structure (DEMES) [117]. For propulsion, a frog utilizes the rotation and changing surface area of their webbed feet. This movement of a frog is imitated by two segmented DEMES actuators: one segment for rotational movement and the other for the area change of the feet, which is shown as the robot leg and robot feet in Figure 10(b), respectively. With these actuators, synchronously operating, maintaining, and relaxing the actuation of two segments causes the propulsion phase, glide phase, and recovery phase, respectively.  
DEA based locomotion robots including insect-like walking robots, worm-like crawling robots, flapping robots, and swimming robots have been discussed in this section. However, DEA based locomotion robots lack controllability since most are controlled in open loop without sensors. As a future work, flexible sensor integrated locomotion robots with feedback scheme could be investigated for ensuring precise movement control.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 4 Experimental Results
### Error Metrics  
We use the following two inter-related and key metrics to assess the usefulness of our algorithms and software:  
1. **NRMSE.** Since the XGC dataset represents the velocity of electrons, the scale of the data is extremely large (\(\sim\)10\({}^{17}\)). Therefore, instead of using the Root Mean Squared Error (RMSE), we measure the error using the Normalized Root Mean Square Error (NRMSE), defined as follows: \[\text{NRMSE}(x,\hat{x})=\frac{\sqrt{\sum_{i=0}^{N}\left(x_{i}-\hat{x}_{i} \right)^{2}/N}}{\max(x)-\min(x)},\] (30) where \(x\) is the primary or original data (PD), \(\hat{x}\) is the reconstructed data, and \(N\) is the number of nodes. We will ensure that error bounds for PD are met. Some of these may be tight error bounds and some of them may be probabilistic and vary within the PD.
2. **Amount of Data Reduction.** The key feature of the data reduction algorithms is the amount of reduction achieved. We measure the storage sizes of the compressed data and residuals, and Lagrange multipliers as well as the size of the models, Tucker bases, and the dictionaries of product quantizers. Once the machine architecture is fixed (after training), the decoder size becomes a fixed compression cost in terms of the size, and it should be included in the overall size when computing the compression levels. Tucker bases are fixed compression costs, since the decoded coefficients need to be projected back into the original data space. The product quantizer's dictionaries for both compressed data and Lagrange multipliers are also fixed compression costs which need to be included in all calculations. Considering all the costs that are needed for the reconstruction, we compute the compression ratio (CR) defined as shown below: \[\text{CR}=\frac{\text{Sizeof}(\text{Primary Data})}{\text{Sizeof}(\text{ Encoded Data})+\text{Sizeof}(\text{Model})}.\] (31)The Encoded Data in Equation (31) includes encoded coefficients of the AE, compressed residuals and quantized Lagrange multipliers. The Model encompasses the models of AE and MGARD, Tucker bases, and PQ dictionaries for the AE and Lagrange multipliers.  
We discuss trade-offs of combinations of these two metrics for the XGC dataset so that XGC scientists can choose parameters based on their requirements.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### Biodegradation of DR23 Dye  
#### 3.3.1 Effect of Dye Concentration  
The DR23 dye degradation using the Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccases was studied at different concentrations of the dye, ranging from 10.0 mg/mL to 50.0 mg/mL, as shown in Figure 4a. The study of dye degradation at different concentrations of the dye was performed at optimum conditions, including a pH of 5.0 and a temperature of 50.0 \({}^{\circ}\)C, using a nanobiocatalyst dosage of 300.0 mg/mL. According to Figure 4a, the increased dye concentration led to a decrease in the biodegradation of the dye in the case of both nanobiocatalysts. The high dye concentration may lead to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **V\({}_{\text{max}}\) (U/mL)** & **K\({}_{\text{m}}\) (mg/mL)** & **R\({}^{2}\)** \\ \hline Laccase@CuFe\({}_{2}\)O\({}_{4}\) & 34.80 & 3.64 & 0.95 \\ Laccase@Fe\({}_{3}\)O\({}_{4}\) & 30.93 & 3.76 & 0.97 \\ Free laccase & 33.60 & 3.65 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kinetic parameters for free laccase and nanoparticle-immobilized laccase.  
failure to get the nanobiocatalyst/dye concentration to equilibrium state or, in some cases, the poisonousness of the nanobiocatalyst that causes decreased dye biodegradation with increased dye concentration [38]. Additionally, the low redox potential of the dye DR23 is responsible for the high affinity between the dye and both the nanobiocatalysts [39]. The CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized increase showed a higher extent for DR23 degradation than the Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized increase, which could be attributed to the presence of copper ions. As discussed previously, the copper ions increase the catalytic properties of increase, by binding to increase through the copper-binding sites.
## 4 Simulation Results and Discussions
We observed that as the SNR increases, the performance of our algorithm naturally increased, and it clearly performed better than GESPAR in terms of noise robustness under most SNR values. Figure 3 shows the recovery probability under different channel sparsity levels under different SNR environments. We observed an almost 100% successful recovery with \(\text{SNR}>10\) before the sparsity \(k=10\). The recovery probability at \(\text{SNR}=10\) also had a very high probability before the sparsity level \(k<8\), which showed that the effectiveness of the algorithm is guaranteed even under low-SNR conditions. As the SNR increased, the effectiveness of the algorithm gradually increased. Hence Figure 3 implies that our proposed algorithm performed well under different SNRs. Figure 2: Normalized mean square error (NMSE) versus sparsity at different signal-to-noise ratio (SNR) values. GARLM is described in Section 3, and GESPAR is the method presented in [44].
## 6 Conclusions  
In the context of the rapid development of RE and EVs, the collaborative optimization problem of both is studied. In order to effectively coordinate and manage the RE company and the BSS when they belong to different investment subjects and do not want to form a joint system, a new cooperative mechanism based on the Stackelberg game is proposed. The proposed model can motivate the enthusiasm of RE company and BSS to cooperate and to achieve win-win outcomes. The main conclusions are as follows:  
1. A Stackelberg game model with the RE company as leader and BSS as follower is proposed, which can effectively simulate the interaction between the RE company and BSS. On the one hand, the RE company does not need to directly control the charge/discharge behavior of the BSS, but only needs to optimize the charge/discharge price of each period to guide the charge/discharge behavior of the BSS. On the other hand, the BSS is not only a passive recipient of the price. The BSS could affect the revenue of the RE company by adjusting its own charge/discharge strategy, so as to urge the RE company to adjust the charge/discharge price.
2. The proposed model no longer requires that the RE company and BSS belong to a same interest subject, and there is no need for unified coordinated control between them. While pursuing the maximization of their own interests, the RE company and the BSS could automatically realize the optimal allocation of resources, and a win-win situation is achieve as well.
3. The comparative analysis of the two Stackelberg equilibriums shows that the weak Stackelberg equilibrium is more in line with the cooperation mechanism proposed in this paper. In addition, the simulation results show that the revenue of the RE company and BSS in the model proposed in this paper is higher than their revenue when they operate alone, which proves that both of them have the motivation to participate in the game.  
In this paper, the randomness of battery-swapping demand is only roughly considered by a pattern of fixed reserve ratio. Through simulation analysis, it is found that the reserve ratio has an impact on the revenue both of the BSS and the RE company. Therefore, in order to consider the randomness of battery swapping demand and RE power precisely, it is necessary to extend the proposed pure-strategy game model to a mixed-strategy game model. This will be our next research direction.  
**Author Contributions:** [PERSON] completed major research tasks and wrote the paper. [PERSON] gave important suggestions and participated in the writing work as well. [PERSON], [PERSON] and [PERSON] contributed to the simulation analysis. [PERSON] and [PERSON] revised the paper.  
**Funding:** This research was funded by State Grid Shandong Electric Power Research Institute. The APC was funded by State Grid Shandong Electric Power Research Institute.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(N_{t}\)Number of time intervals  
\(\Delta t\)Length of a single time interval  
\(p_{\text{RE}}^{t}\)Active power output of renewable energy at time