esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimal Power Control Strategy for Grid-Following Inverters in a Synchronous Frame
## 1 Introduction
In this work, a state-space model that integrates V-I and power sharing dynamics for grid connected inverters is presented. This model is developed in a synchronous _d-q_ frame to improve active and reactive power decoupling. The grid voltage is modeled as a constant vector that is included as an external known disturbance. In addition, the active and reactive power dynamics are included in the output equation of the state-space system. This type of model allows for using modern control analysis techniques such as singular value diagrams, Nyquist diagrams, root loci, and robustness margins estimations. An optimal power sharing controller that minimizes a power tracking performance index is also presented. This tracking index measures the active and reactive power deviations and also measures the energy in the control inputs. The novelty of this work is based on how the nonlinear dynamics of the active and reactive power were linearized in the output of a state-space model in the _dq_ frame. This allowed development a controller that minimizes the tracking error in the output of this state-space model. The methodology used in this work can be extended to other control methods such as \(H\infty\) or model predictive control (MPC). Furthermore, this approach has many advantages over other approaches found in literature: First, this approach is intended to minimize the energy in the states and inputs, which means better transient response, reduced tracking error, and fewer power losses during transient responses. Second, the use of an LQR controller has many robustness properties regarding gain and phase margins as shown in [18]. Third, the controller is effective in reducing active and reactive power coupling. Finally, this approach does not use resonant filters that may affect sensitivity and robustness under parameter variations. The linear quadratic regulator with optimal reference tracking (LQR-ORT) controller was designed to meet certain robustness and performance margins under uncertainties in the LCL filter components. Hardware-in-the-loop (HIL) simulations and experimental results validate the results of this approach compared to a classical PR-Droop controller found in the literature. This work is an extension of [29] in the sense that it includes HIL simulations under component variations, experimental results in a physical setup, and deeper analysis and conclusions. Section 2 shows the mathematical model in the _dq_ frame that was used to compute the LQR-ORT controller. The LQR-ORT mathematical development is presented in Section 3. Section 4 presents the simulation and experimental results divided in three subsections: robustness and stability analysis, simulation results using HIL tools, and experimental results in a real testbench. Finally, the conclusion and future work are presented in Section 5.
## 1 Introduction
Our proposed nozzle consisted of a smooth circular hole and four circumferential screw grooves for structuring the impinging jet flow with a superimposed swirl, so that the nozzle was similar to the conventional circular hole with internal threads. The non-dimensional swirl number of the swirling jet at the nozzle exit was about 0.18, and the excited flow belonged to a low-intensity swirling flow. With 3D printing technology, the traditional circular holes will be easily replaced with these threaded holes for the leading-edge cooling of turbine blades. Among the swirling impinging jet studies in the literature, research on heat transfer performance on a concave surface has not been reported. A typical impinging jet consists of various complex flow regions, such as free jet, stagnation, streamline curvature, and wall jets, and these complexities make it difficult for Reynolds-averaged Navier-Stokes (RANS) equation-based turbulence models to accurately predict fluid flow and thermal behaviors in an impinging jet. The swirling flow in jets and the curved concave surface substantially broaden and deepen the complexity of the flow phenomena compared with the typical impinging jet. Recently, some studies on swirling impinging jets on a flat surface using large eddy simulation (LES) have been reported. [PERSON] et al. [15] investigated the swirling impinging jet issuing from a guide vane on a flat surface using LES. They found that the heat transfer performance of the nozzle with the insert was better than that of the ordinary nozzle; however, it led to higher pressure loss to some extent. It is noted that there is a lack of proper fluid flow and heat transfer data for swirling jet impingement in the stagnation region as well as the wall jet region for a curved concave target. Such data will be useful to improve the understanding of fluid flow and heat transfer behavior in these regions. The aim of the present study is to perform LES of a swirling impinging jet flow issuing from a threaded hole on a semi-cylinder concave target at \(Re\) = 16,000 and \(H/D\) = 2. In this work, we firstly presented the detailed governing equations and analyzed the simulated results of a swirling impinging jet on a flat surface using two different turbulence subgrid-scale stress models; then, we described the computational domain of the present problem, including the swirling and non-swirling impinging jet on a semi-cylinder concave target; finally, we discussed the flow features, spectral characteristics, heat transfer, and frequency characteristics obtained. This study compared flow and heat transfer performance of the swirling and non-swirling impinging jet on a semi-cylinder concave target. Using LES computations, this study may help to understand the physical phenomena involved in the formation, development, and decay of the swirling impinging jet on a concave target.
# Super-Resolution Reconstruction of an Array Lidar Range Profile
###### Abstract  
Aiming at the problem that the range profile of the current array lidar has a low resolution and contains few target details and little edge information, a super-resolution reconstruction method based on projection onto convex sets (POCS) combining the Lucas-Kanade (LK) optical flow method with a Gaussian pyramid was proposed. Firstly, the reference high-resolution range profile was obtained by the nearest neighbor interpolation of the single low-resolution range profile. Secondly, the LK optical flow method was introduced to achieve the motion estimation of low-resolution image sequences, and the Gaussian pyramid was used to perform multi-scale correction on the estimated vector, effectively improving the accuracy of motion estimation. On the basis of data consistency constraints, gradient constraints were introduced based on the distance value difference between the target edge and the background to enhance the reconstruction ability of the target edge. Finally, the residual between the estimated distance and the actual distance was calculated, and the high-resolution reference range profile was iteratively corrected by using the point spread function according to the residual. Bilinear interpolation, bicubic interpolation, POCS, POCS with adaptive correction threshold, and the proposed method were used to reconstruct the range profile of the datasets and the real datasets. The effectiveness of the proposed method was verified by the range profile reconstruction effect and objective evaluation index. The experimental results show that the index of the proposed method is improved compared to the interpolation method and the POCS method. In the redwood-3 dscan dataset experiments, compared to the traditional POCS, the average gradient (AG) of the proposed method is increased by at least 8.04%, and the edge strength (ES) is increased by at least 4.84%. In the real data experiments, compared to the traditional POCS, the AG of the proposed method is increased by at least 5.85%, and the ES is increased by at least 7.01%, which proves that the proposed method can effectively improve the resolution of the reconstructed range map and the quality of the detail edges.  
lidar; POCS; LK optical flow method; Gaussian pyramid; super-resolution reconstruction; range profile +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
When building the 3D reconstruction of trees, we can obtain the 3D point clouds of trees through UAV tilt photogrammetry technology, airborne LIDAR technology, ground 3D laser scanning technology, laser SLAM, etc. The UAV tilt photogrammetry technology can obtain 3D data with real texture. However, due to the influence of the wind, the trees sway greatly, and the obtained aerial images of trees cannot be fixed in the same position. As a result, the final 3D model developed has problems of blurred and incomplete expression of 3D information, which further makes it impossible to obtain the 3D information under the canopy of trees (as shown in Figure 1a). Therefore, the carbon storage of trees cannot be estimated completely and accurately by this technology. The ground station 3D laser scanning technology is based on the principle of laser ranging, which can quickly obtain the 3D point clouds of the measured object at a single station. Although it has the advantages of high accuracy of tree point clouds obtained, the point cloud data obtained by single-station scanning cannot completely reflect the 3D information of trees due to the serious occlusion of trees. Multi-station scanning is required to compensate, resulting in a huge workload of data acquisition. This is not suitable for establishing 3D point clouds of large-scale forestry. Airborne LIDAR technology expands single-station scanning to overall scanning, which can quickly reconstruct geometric data such as points, lines, surfaces, and 3D models of the ground. This can enable a detailed understanding of the overall changes and characteristics of the survey object [18]. At present, most airborne LIDAR scanners use orthophoto acquisition, and the use of pulse frequency has a certain penetrability. However, there is still a large number of 3D point clouds missing at the bottom of trees when establishing tree point clouds (as shown in Figure 1b). Therefore, using laser SLAM technology to build 3D point clouds for trees is suitable for the accurate estimation of carbon storage in forestry carbon sink trees with its high efficiency and good accuracy.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 4 Experimental Results  
To establish streaming environment, images files in Visual Tracker Benchmark dataset [29] were converted into movie clips in MPEG-4 format. Each image has time stamp interval of 0.03 s so that the converted movie clips are played at 33.3 frame per second as shown in Figure 7.  
We acquired additional test videos of routine street scenes using a CCTV over the street, called CAU_Ped1, CAU_Ped2, CAU_Ped3 and CAU_Ped4. Those street scenes are suitable to evaluate the proposed method because some targets are completely occluded by other pedestrians, vehicles and trees very often.  
In image file-based datasets, ground truth is provided for each image as shown in Figure 7. Each value of ground truth denotes \(x\)-coordinate, \(y\)-coordinate, width and height of the target. On the other hand, ground truth is provided for a certain period in the streaming condition as shown in Figure 8. Spatial location of the target estimated by the tracker is saved with elapsed time, and evaluated by being compared to the time-based ground truth. In this work, we used a PC equipped with an Intel i7 8700K CPU and GTX 1080 ti.
## 2 Materials and Methods
### Experimental Evaluation of Stretching Behavior of the Planar Specimens  
As shown in Figure 3, the stretching of the planar stent mesh was, in fact, equivalent to the expanding of the tubular stent in the circular direction. Therefore, the planar mesh was expected to be conducive to the measurement and analysis of strain distribution, and to avoid the high costs related to the fabrication of real stents [10; 12]. In this research, the investigation of the deformability of AZ31 alloy planar specimens with the three stent design patterns was initially performed by performing stretching tests, and then the results were validated in the deformation tests of real tubular stents.  
Planar specimens with the unfolded stent patterns, as shown in Figure 3, were designed for the stretching tests. The whole stent was unfolded along the axial direction into the planar mesh. Both ends of the mesh were connected to rectangular sheets through smooth circular arc transition at each joining point. The stretching grippers were clamped onto the rectangular sheets at both ends and they stretched the specimen to impose deformation. Uniaxial stretching tests of planar specimens with the three stent design patterns were performed using a universal material testing machine (AG-X, Shimadzu, Japan). Stretching speed was kept at 0.5 mm/min, which is typically used in in vitro stent crimping tests. A DIC (Digital Image Correlation) optical measuring system as described in Reference [18], was employed to track the deformation. The stretching test was terminated, once fracture had occurred anywhere in the mesh.
## 5 Discussion
### Palaeodepositional Environments
#### 5.1.3 Palaeoclimate Characteristics  
The values of Sr/Cu are very sensitive to palaeoclimate changes and can therefore be used as an indicative parameter of palaeoclimate changes during the depositional period; the element Sr prefers dryness and the element Cu prefers wetness. Sr/Cu values less than 5 indicate a humid climate; values between 5 and 10 indicate a semi-humid climate, and values greater than 10 indicate an arid climate.  
Fe/Mn values can also be used as an indicator of palaeoclimate, with high values indicating humid climates and low values indicating arid climates. Of these, Fe tends to accumulate rapidly in humid environments, while Mn is very low in humid environments and high in arid environments. From Figures 5 and 6, we can see that the Northeast Sichuan Permian is basically in an anoxically reduced still-sea environment, corroborating the results of the intersection diagram.  
In the present study, the Sr/Cu values of the Dalong Formation ranged from 0.08 to 1.22, with an average value of 0.64, indicating that its depositional environment was a humid climate. In contrast, the value of Fe/Mn ranged from 3.69 to 206 with a mean value of 90.26, indicating its arid climate. In general, the values reflect the semi-humid climatic conditions of the Dallon Formation. The Sr/Cu values of the Wujiaping Formation range from 0.06 to 0.85, with an average value of 0.39, indicating that Sr/Cu deposition occurred in a humid climate. The value of Fe/Mn ranges from 4.0 to 164, with an average value of 49.14, reflecting that the Wujiaping Formation was formed under semi-humid-arid climatic conditions in general. In the palaeoclimate index convergence diagram (Figure 6c), the sample points of the Wujiaping Formation are relatively concentrated in the upper left, reflecting the arid climate in this depositional period. The Dallon Formation sample site is divided into two parts, reflecting changes in the climatic environment that occurred during the depositional period of the Dallon Formation. Combined with the longitudinal map, the analysis suggests that the palaeoclimate environment of the Dalong Formation changed from an arid climate to a semi-humid climate [67; 68; 69].
## 2 Materials and Methods
### Application of the Attended Encoder-Decoder to the Short-Term Load Forecasting
#### 2.4.3 Attention Score Function  
During the development of the algorithm, a simplified score function showed slightly better results. In Equation (15), the absolute difference between the encoder output \(h\) of the dimension \(N\) and the last decoder output \(s_{t-1}\) adjusted to the dimension of the encoder is calculated. Afterwards the softmax function is applied twice (16) and (17). The first time, to map the differences obtained from Equation (15) to the interval \([0,1]\). The second time, to assign small differences between the decoder and encoder output to the upper part of the interval \([0,1]\) and larger differences to the lower part (17). The final context vector is obtained in (18) as a sum over the time dimension. The decoder state for which the attention is applied is concatenated with the context vector. Finally, a dense layer is used to adjust the size of the concatenated vector to the output produced by the LSTM cell.  
\[e_{ij}=|h-s_{t-1\times N}| \tag{15}\]  
\[\alpha_{ij}=\frac{exp(e_{ij})}{\sum_{k=1}^{T}exp(e_{ik})} \tag{16}\]  
Figure 6: Artifitial neural network architecture for load forecasting.  
\[\beta_{ij}=\frac{exp(1-\alpha_{ij})}{\sum_{k=1}^{T_{x}}exp(1-\alpha_{ ik})} \tag{17}\] \[c_{i}=\sum_{j=1}^{T_{x}}\beta_{ij}h_{j} \tag{18}\]  
In this formulation, only one weight matrix is required instead of three matrices as specified in (10). The forecast error while using both formulations of attention is however similar.
# TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
## 5 Experiment and Case Study
### Case Study
#### 5.3.3 Trustworthiness and Fairness  
Trustworthiness: Firstly, the smart-contract-based authority-management function implements the access mechanism for distributed collaboration and completes the role-based authority assignment. The binding of identity information and authority encourages collaborative nodes to act honestly and avoid the influence of malicious nodes on collaborative tasks. Secondly, the blockchain records the collaborative tasks and the collaborative interactions between nodes, and its traceable and tamper-evident storage content strongly restrains the possible malicious behaviour of nodes. Thirdly, smart contracts control the process of collaborative tasks and complete the automatic incentive for collaborative nodes, freeing them from reliance on trusted third parties. Finally, the introduction of events in the mechanism further increases the trustworthiness of the collaboration mechanism. On the one hand, events serve as signals for smart contracts to respond to and trigger collaboration behaviours, describing the blockchain transaction behaviour at a specific or specified moment, and collaborating nodes can listen to the events to obtain the collaboration process and execute the corresponding collaboration behaviours. On the other hand, the Ethereum  
Figure 19: Pretrained model and global model classification accuracy.  
platform records events in the form of logs, and collaborative nodes can retrieve the history of collaborative actions through the logs.  
Fairness: The collaborative-learning mechanism introduces a model-verification consensus mechanism to evaluate the contribution of different task participants to the collaborative task. Multiple model verifiers independently verify the pretrained models submitted by task participants and reach a consensus on the verification through a smart contract and finally incentivise the task participants with the model accuracy. The model-verification consensus and task incentive are both accomplished by the smart contract, which protects the interests of both the task owner and task participants and encourages more data nodes to participate in the collaborative task.
## 4 Methodological Approach
### Classification of Challenges  
In this step, the challenges are represented in a four-quadrant diagram based on their betweenness centrality and eigenvector centrality values, as shown in Figure 3. This concept is derived from the MICMAC analysis by [PERSON] and [PERSON] [89] to determine the influence of challenges. The upper-right quadrant contains critical challenges characterized by high betweenness centrality and high eigenvector centrality values. These challenges are highly influential and central, and they act as bridges to several other important ones in the network. The upper-left quadrant contains localized influence challenges, defined by high eigenvector centrality and low betweenness centrality values. They are influential within their immediate surrounding challenges in the network, but they do not act as bridges to others. The lower-right quadrant contains connector challenges, identified by low eigenvector centrality and high betweenness centrality values. These act as a bridge linking other disconnected challenges in the network, but they are not closely connected to other influential challenges. The lower-left quadrant contains low-connectivity challenges, marked by low eigenvector centrality and low betweenness centrality values. These are less central and less connected to influential challenges, so they have limited influence in the network.  
Figure 1: Network of challenges: node size represents betweenness centrality.  
Figure 2: Network of challenges: node size represents eigenvector centrality.
## 3 Results and Discussion
### Imaging of PAN/Biochar Fibers though the Preparation Process
#### 3.2.5 FT-IR Spectroscopy of \"as-Spun\" and Cyclized Fibers  
Stabilized/cyclized samples were analyzed with an IR spectrometer. Figure 9 reports the as-spun spectra. The spectra for the 100% PAN fiber can be compared to the literature [40] to properly assess if the polymerization occurred correctly. The peak at approximately 2241 cm\({}^{-1}\) represents the triple bond (C\(\equiv\)N), as described in [41]. [PERSON] et al. [41] also referred to the peak at 1668 cm\({}^{-1}\) (1661 in the figure) to the precursor. As expected, the choice to use high-temperature gasification biochar impacted the results reported in Figure 9. Gasification biochar was produced in high-temperature environments (starting from 900-1100 \({}^{\circ}\)C in the combustion zone, then decreasing to 600 \({}^{\circ}\)C at the end of the reduction zone), and these conditions led to a structure where all of the functional groups left the surface. This can be observed by the general and diffuse decrease in the spectra transmittance (therefore, an increase in the material absorbance) as the amount of biochar in the preparation increased. Similar results were reported by [PERSON] et al. where active char was used as an additive in PAN fibers [20].  
Figure 8: EDS analyses of carbonized fibers, and mass percentages of the detected elements.  
The spectra of the cyclized PAN (Figure 10) showed a reduction in the above-mentioned peak corresponding to the C\(\equiv\)N bond, and an evolution of the peak at \(\sim\)1591 cm\({}^{-1}\) was observed. This peak corresponds to the C=N band. This indicates that the cyclization took place and a ladder-like structure was formed [42]. This structure enables the PAN fibers to withstand the high-temperature carbonization temperature [43]. This phenomenon was less and less pronounced as the biochar share in the solution increased due to the absorptive behavior previously described. Biochar had two major effects on the spectra: on the one hand, biochar \"diluted\" the amount of PAN, thus reducing the signal of the peaks that can be easily recognized in the 100% spectrum. Secondly, the char structure introduced a series of noises above 3000 cm\({}^{-1}\) and between 1300 and 1800 cm\({}^{-1}\). The literature review described the biochar-induced noises as associated with different groups [44,45]. As reported by [46], the presence of several peaks in the range of 3400-4000 cm\({}^{-1}\) may be assigned to the water absorbed. The spectra recorded in this work did not show sharp peaks in that area, but a general noise of the signal. Biochar had strong hygroscopic behavior, and the shares of biochar in the material may have adsorbed large amounts of water, leading to high noises in the test.  
Figure 9: IR spectrum of “as-spun” fibers.