esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 2 Background
SVM classification is based on the formation of a decision boundary that owns the largest margin of separation between data samples from several classes. This decision boundary could be linear or non-linear with the help of kernels [53]. The ability of SVM to discern complex, nonlinear relationships and form decision boundaries with the maximum margin of separation between classes makes it invaluable for classification and segmentation tasks. [PERSON] et al. [54] utilized gaussian kernel SVM for spectral spatial classification of Pavia University data along with mathematical morphology. The SVM multi-class results were converted into multiple binary classification results to reduce the noise. [PERSON] et al. [55] applied HSI analysis to detect decay in potatoes where too SVM played a pivotal role. To achieve the best detection accuracy, the parameters of SVM were tuned through the grid search method. In the approach by [PERSON] et al. [56], SVM played a major role in the non-invasive identification of defective potatoes. On being combined with k-means clustering, it gave the desired results. Apart from being in use in agriculture and food quality assessment, SVM has been utilized to identify multi-class landcover scenes as well. [PERSON] et al. [57] employed SVM on spectral spatial features obtained through Gabor and wavelet filters on Pavia University (PU), obtaining high classification of 96.7% accuracy. SVM has also been applied in multiple kernel learning environment, like [PERSON] et al. [58] utilized spectral, semantic and spatial features infused kernel. This composite kernel SVM was used to classify the landcover Indian Pines and Pavia University dataset. [PERSON] et al. [59] also fed 3D spectral-spatial features of these datasets into SVM for per-pixel prediction. [PERSON] et al. [18] reduced spectral dimensions through wavelet transform and classified using SVM. As evident from above discussed literature, the ability of SVM to discern complex, nonlinear relationships and form decision boundaries makes it invaluable for classification and segmentation tasks.
## 1 Introduction
Based on this, a patch selection and refining scheme is proposed to continuously generate redundant waypoints through the cumulative gradients of key regions based on multi-scale feature maps since we learn that the key-areas are extremely difficult to be distinguished at the beginning and would change during the procession. The insignificant waypoints will be adaptively deleted in each region which is divided according to the point distribution until the key point calibration becomes stable. Experiments show that our approach converges the results in less than 40% of the number of iterations of other baseline algorithms and reach the same or even a higher obstacle avoidance rate. In addition, from the perspective of application scenarios, recent works can only plan for two-degree-of-freedom or three-degree-of-freedom robots, while our proposed approach can simultaneously perform multiple-degree-of-freedom robots. Specifically, we balance the gradient of multi-category waypoints (for example, road boundary, road center, close to obstacle) planning to avoid over-optimizing one of them during the training phase. Figure 1 illustrates an example that our HDPP greatly reduces the scale of waypoints based of efficient path planning. The algorithm can realize migration applications in complex environments, especially high-dimensional space. In summary, the contribution of this work is fourfold:  
* A novel multi-scale positioning model of high-dimensional path planning is proposed to adaptively extract and learn multi-scale feature maps with multiple predictions, while dynamically refine them through fast positioning. The multi-scale strategy is used to improve the characterization ability of unknown environmental factors. The results demonstrate the superiority of our approach through comparison with state-of-the-art models;* A patch selection and optimization scheme are proposed to dynamically find and divide category areas, and gradually eliminate insignificant waypoints for multi-degree-of-freedom planning;
* We balance the gradients of multi-category feature points to process obstacles and road segment edges at the same time to avoid over-optimizing one of them during the training phase, eliminating the redundancy and collapse of previous research planning methods;
* Extensive experiments conducted on both manually annotated and machine weakly labeled datasets demonstrate the effectiveness of the proposed approach. We have verified the effect of HDPP not only in high-dimensional space, but also in a planar simulation environment. The remainder of the article is structured as follows. We present the related work in Section 2. The proposed approach--HDPP--is then introduced in detail in Section 3. Next, the experimental results are elaborated in Section 4. Finally, we make conclusions and describe future work in Section 5.
## 6 Conclusions  
In this article, the resonance instability phenomenon that was found during tests of a photovoltaic charging station is analyzed. The interaction of a resonance situation in the grid impedance and non-passivity of the inverter in that frequency range causes the system formed by the inverter and grid to be non-stable.  
The inverter output admittance determines whether the inverter is passive or not. In order to analyze the inverter output admittance, the single-phase inverter system is modelled, taking into account the two common control methods. These are, respectively, the PR-based current control and the synchronous rotating frame based current control methods (dq-based). In order to consider the effect of the PLL on the output admittance, a linear small signal model is used for the PLL modeling.  
This article verified the modeling by simulation. The analytical model shows that the real part of the inverter output admittance is negative for frequencies above 1700 Hz. In case the resonant frequency of the grid impedance exceeds 1700 Hz, the inverter-grid system will be unstable. The results are confirmed by the time domain simulation.  
This article further analyzes the influence of different control parameters on the inverter output admittance. The analysis shows that the PLL bandwidth and the control parameters of the current loop will have an influence on the output admittance of the inverter. By adjusting these parameters, the inverter-grid system may be prevented from being unstable at certain resonant frequencies.  
On the basis of the above analysis, this article gives two methods to improve stability: connecting a low pass filter or reducing the control bandwidth. The simulation results show that both of these methods can significantly improve the stability of the inverter-power grid system.  
Writing-original draft preparation, formal analysis and software, Z.Z.; investigation and validation, Z.Z. and C.G.; conceptualization, resources, L.F., H.R and A.R; review and editing C.G., H.R., L.F. and A.R.; supervision, project administration and funding acquisition, A.R. and L.F.  
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the ERA-Net Smart Grids plus, grant number 646039, from the Netherlands Organisation for Scientific Research (NWO) and from BMVIT/BMWFW under the Energy der Zukunft programme.  
Our project has received funding in the framework of the joint programming initiative ERA-Net Smart Grids Plus, with support from the European Union's Horizon 2020 research and innovation programme. We would like to thank University of Twente for supporting this research study, as part of the Solar bike and Smart Living Campus projects.  
The authors declare no conflict of interest.  
The content and views expressed in this material are those of the authors and do not necessarily reflect the views or opinion of the ERA-Net SG+ initiative. Any reference given does not necessarily imply the endorsement by ERA-Net SG+.
## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture  
The models proposed by researchers based on the discriminant deep architecture include convolutional neural networks (CNNs) and cyclic neural networks (RNNs) in the field of video applications over CVIS.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 1 Introduction
### Optimization Process for Nonlinear Image Reconstruction
The Levenberg-Marquardt method, which is often employed in DOT image reconstruction, uses the following iteration to solve Equation (2), with \(W\) equal to an identity matrix and \(\gamma=0\). \[\mu_{t+1}=\mu_{t}-(J^{T}J+\gamma^{\prime}\cdot I)\
abla f(\mu_{t}), \tag{5}\]  
where \(\gamma^{\prime}\) is a small positive value and \(I\) an identity matrix. The nonlinear optimization methods, such as the steepest descent and conjugate gradient methods, have been compared for DOT image reconstruction with DA [29].
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### The Effect of System Errors
#### 2.3.3 System Error of Target's Elevation Bias  
When the target's elevation bias is present, the constrained conditions in (1) become \(\mathbf{u}_{o}^{T}\mathbf{u}_{o}\
eq\left(R+h\right)^{2}\). Define \(\delta_{h}\) as the elevation bias; hence, the mean of \(\boldsymbol{n}\) is \(E(\boldsymbol{n})=0\) and \(\boldsymbol{G}(\theta_{1})=\left[\delta_{h}^{2}+2(N+h)\delta_{h},0\right]^{T}\). The localization accuracy is obtained from (48) as  
\[\text{MSE}(\theta_{1})=4A_{11}J_{1}^{T}C^{-1}J_{1}A_{11}^{T}+A_{12}\bigg{[} \begin{array}{cc}\left(\delta_{h}^{2}+2(N+h)\delta_{h}\right)^{2}&0\\ 0&0\end{array}\bigg{]}A_{12}^{T} \tag{54}\]  
\(A_{12}\bigg{[}\begin{array}{cc}\left(\delta_{h}^{2}+2(N+h)\delta_{h}\right)^ {2}&0\\ 0&0\end{array}\bigg{]}A_{12}^{T}\) is the amount of degradation for localization accuracy introduced by the target's elevation bias.
## 2 Numerical Model Development and Experimental Validation
### Numerical Model
#### 2.2.2 Weak Adhesion Simulation  
To simulate weak adhesion, it was first necessary to divide the simulated adhesive into three parts, as represented in Figure 8. The outside edges were considered a layer of weak adhesion with an overall thickness of 0.001 mm, while the center of the adhesive, with the remaining height, was considered not to have been subjected to any physical alterations and thus had the total mechanical properties of the material.  
In regards to the materials' properties, the overall value of stiffness of the weak adhesion layer was lowered by reducing its Young's modulus, as suggested in [20,21] and reinforced in [22], where it is possible to see the initial validation of the weak adhesion simulation.  
Utilizing the previously described techniques, a total of 72 unique cases were generated, each one with a different level of weak adhesion varying from 100 to 700 MPa. These simulations presented time-series that had an overall close visual aspect to the experimental ones, but no conclusions or validation could be made based only on this due to the discrepancies of data previously commented on and seen in Figure 6.
# Nonlinear Nonsingular Fast Terminal Sliding Mode Control Using Deep Deterministic Policy Gradient
## 4 Experimental Results and Discussion
### Results and Discussion  
From Tables 4 and 5 and Figures 4-7, it could be seen that the position error and velocity error of traditional TSMC, NTSMC, and RBF-SMC changed with the application of uncertain interference and friction. Compared with the three strategies, DDPG-NNFTSMC was much smaller in position error, velocity error, average position error and average velocity error, the corresponding tracking position and speed almost fitted the theoretical value. In addition, the proposed sliding surface function \(\mathbf{s}\) was designed based on the control function in Equation (12), which played an important role in providing fast convergence and robustness to uncertainties and disturbances. Compared with other control strategies, the control strategy proposed in this paper provided the best path tracking performance and the fastest convergence speed.  
In Figure 8a,b compared with the traditional TSMC and NTSMC which converged on finite time, but for nonlinear and nonsingular systems, there were still system chattering and errors, so we must choose between chattering elimination and path tracking accuracy. Therefore, the robustness of the system was reduced, and the tracking error was increased. As shown in Figure 8c, although the control input 1 of RBF-SMC had the characteristics of fast convergence speed and elimination of chattering phenomenon, the tracking error was greatly increased; control input 2 provided a continuous control signal with partial chattering behavior, but the tracking error also decreases, as shown in Figures 4-7. The NNFTSMC designed in this paper provided continuous control signals for the manipulator, and led DDPG network into the controller, the tracking accuracy was guaranteed, and the chattering phenomenon of Figure 8d was eliminated, and the convergence time of the control system signal was effectively reduced without losing its effectiveness, which is shown in Figure 8e. These adaptive feedbacks were estimated according to the change of system disturbance and uncertain disturbance terms. Once the error variables converge to the sliding surface, they would be close to a constant value. From the simulation results, the controller was superior to the traditional TSMC, NTSMC, and RBF-SMC in tracking accuracy, convergence speed, and chattering elimination.
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 2 Scheduling Flexibility and Demand Response Model  
### Scheduling Flexibility for Grid-Connected Wind Power  
After a large-scale wind power grid-connected, the net load changes more dramatically, which makes it difficult to effectively respond to the rapid change of net load through reserve capacity allocation alone and brings a severe test to the safe operation of the system. Dispatch flexibility has become a key indicator to measure the system operation performance by optimizing the allocation of various flexible resources in a certain time scale and responding to the change of net load at a certain cost.  
Whether the dispatch flexibility is adequate depends on two aspects. One is the adjustable capacity that the system can provide. The other is the demand of the adjustable capacity for uncertainties in the system. Therefore, quantitative description is made from these two sides.
# Low-Cost Rapid Fabrication of Conformal Liquid-Metal Patterns
## 5 Conclusions  
Various fabrication techniques for patterning sprayed LM on flexible and rigid substrates were demonstrated. The effects of the spraying time, distance, and pressure on the obtained LM thickness and conductivity were characterized. Optimal spraying conditions were used to achieve a minimum feature size of 48 um in sprayed LM, and a minimum gap width of 201 um between LM lines. Physical scraping of the sprayed LM can reduce the minimum gap width to 34 um. Polyimide and UV-curable masks were used to pattern the sprayed LM. Both masking techniques are compatible with flexible and curved substrates. Thus, this sprayed LM fabrication process can be used for low-cost rapid prototyping of flexible and conformal communication devices and sensors. As a proof of concept, a patch antenna and transmission line were fabricated using sprayed LM. The performance of the sprayed LM devices were very similar to the reference copper versions.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(Z_{o}\) (target impedance) & 50 \(\Omega\) \\ \(t\) (trace thickness) & 0.06 mm \\ \(h\) (dielectric thickness) & 0.787 mm \\ \(e_{r}\) (relative dielectric constant) & 2.2 \\ \(w\) (trace width) & 1.9 mm \\ \hline \end{tabular}
\end{table}
Table 1: TL dimensions and parameters.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/8/1565/s1](http://www.mdpi.com/2076-3417/9/8/1565/s1)]([http://www.mdpi.com/2076-3417/9/8/1565/s1](http://www.mdpi.com/2076-3417/9/8/1565/s1)), Figure S1: Simulation results for the insertion loss of as transmission line with a capacitive coupling; Figure S2: Images of the droplets sprayed at various pressures measured by a laser scanning microscope.  
**Author Contributions:** Conceptualization, S.S. and A.T.O.; data curation, K.S.E., S.S., and T.K.A.; formal analysis, K.S.E. and S.S.; investigation, K.S.E. and S.S.; methodology, K.S.E. and S.S.; project administration, S.S. and A.T.O.; resources, S.S., W.A.S., and A.T.O.; supervision, W.A.S. and A.T.O.; validation, S.S. and A.T.O.; visualization, K.S.E., S.S., and T.K.A.; writing--original draft, K.S.E. and S.S.; writing--review and editing, K.S.E., S.S., W.A.S., and A.T.O.  
**Funding:** Research supported in part by the National Science Foundation, award number ECCS-1546980.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparative Technical-Economical Analysis of Transient Stability Improvements in a Power System
## 4 Improved System Description
### Improved IEEE 9 Bus System Transient Stability Analysis  
The results of the analysis carried out to study the effects of the solutions presented are shown in Figure 3, which describes the rotor angle plots of generator 2 obtained for each case. From Figure 4, it can be seen that the addition of a new parallel line is the most effective solution to contain the rotor angle variations; moreover, thanks to its effect on the system admittance matrix, it allows reducing the initial rotor angle, increasing the stability margin.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IEEET1 Model** & \multicolumn{3}{c}{**EXST4B Model**} \\ \hline
**Systems** & & & \\
1-Event V, & & & \\
3-V, & & & \\
4-V, & & & \\
**Parameter** & **Value** & **Parameter** & **Value** \\ \(K_{A}\) & 3.15 & \(K_{pr},K_{ir}\) & 50 \\ \(T_{A}(\text{s})\) & 0.035 & \(T_{A}(\text{s})\) & 0.4 \\ \(K_{E}\) & 1 & \(K_{pm}\) & \(-0.06\) \\ \(T_{E}(\text{s})\) & 6.5 & \(K_{p}(\text{s})\) & 0.6 \\ \(K_{F}\) & 0.08 & \(K_{c}\) & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SCV and STATCOM model and parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
Looking at the results in Tables 5 and 6, the best improvement solution is to double lines 5-7. This solution gives the lowest maximum rotor angle and allows to obtain the higher CCT. The values of the maximum rotor angle confirm that the contribution of SVC and STATCOM to the rotor angle stability is limited for the analyzed system. As for the CCT, STATCOM leads to a slightly higher value, while SVC worsens it.  
The values shown in Tables 5 and 6 for the fast-acting exciter confirm that this solution allows improving the transient stability thanks to its effect on the rotor angle stability. Comparing the Values of FCT and CCT, in All Five Cases, the FCT Values Are Smaller than the Corresponding CCT Values, Which Allows a Transient Stable Operation of the System.
## 2 Material and Methods
### Methods of Pad Surface Characterisation
#### 2.2.1 Confocal Microscope  
Characterization of the pad surface using a confocal microscope was preliminarily investigated with a reference pad and several sample pads that were heated from 30 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. The reproducibility of the change was confirmed by applying the same location and measurement conditions. In general, the change in asperities according to temperature or the abrasion of asperities occurring during the CMP process is strongly related to the surface roughness height factor [19]. Therefore, a comparative analysis was performed on the surface roughness factors, focusing on the values of Ra (arithmetical mean height), Rp (maximum profile peak height), and Rv (maximum profile valley depth). Figure 3a is a set of SEM images showing the changes in the pad surface according to temperature, and Figure 3b is a graph showing the surface roughness results for each heated pad. The pad used in the experiment was performed after applying a short conditioning process to evenly distribute the entire pad surface. In Figure 3a, by comparing the SEM images of the pad heated to 30 \({}^{\circ}\)C (room temperature) with those treated with higher temperatures (40-80 \({}^{\circ}\)C), it was confirmed that the height distribution changed as the temperature increased, and it was observed that a circular pore shape became apparent at the higher temperatures. In addition, this trend could be confirmed in the measured surface roughness values as shown in Figure 3b. While the change of the pad surface was insignificant in the low-temperature area close to room temperature, the surface roughness values all showed a tendency to increase above a certain temperature (50-60 degrees). Therefore, through this measurement, it could be predicted that the change of the surface according to the pad temperature is the main change in the height distribution of the surface as shown in Figure 3c.  
However, given the measurement principles of the confocal microscope, the height distributions established in this way are relative values, and therefore the pore shapes at high temperatures could not be accurately determined. Thus, evaluation using an additional measurement method was deemed essential.  
Figure 2: Components and testing stages in examination of pad surface before and after change in temperature.
## 2 Materials  
### Study Area and Field Campaigns  
China is one of the largest cotton producers and importers in the world, and Xinjiang is the primary cotton-growing region in China [33; 34]. The chosen study area was on a large agriculture region (6118.08 km\({}^{2}\) or 1, 511, 810 acres) in Shihezi (44\({}^{\circ}\)37\({}^{\prime}\) N, 85\({}^{\circ}\)42\({}^{\prime}\) E), Xinjiang Province, China. The region is located in a temperate continental climate zone. The average field size in the study area is 73139.7 m\({}^{2}\) (18.07 acres), and the majority of fields have a flat topography, which is preferable for decametric remote sensing applications (e.g., Sentinel-2). The annual mean temperature of the study area is 7.39 \({}^{\circ}\)C, the annual total precipitation is 206 mm, and the average altitude is 450.8 m.  
The field campaigns were conducted in early June 2018, mid-July 2018, and mid-August 2018 (Table 1), with 117 total quadrats obtained. Each quadrat was assigned one leaf area index (LAI) value, obtained as the average leaf area index (LAI) of the three sample points that matches the corresponding Sentinel-2 pixel (Table 2). Each sample point datum was collected using an LAI-2200C Plant Canopy Analyzer (Li-Cor, Inc., Lincoln, NE, USA). The main planted crops in the study area are cotton, grape, spring maize and winter wheat, and cotton holds the largest planting proportion, which is 71.7%. The cotton season is from mid-April to early October. The spring maize season is from mid-April to late September. The winter wheat season is from late September of the previous year to late June. Notably, the study area has completely achieved mechanization for agricultural production and management. Figure 1 shows the location of the study area and field observation sites with Sentinel-2A imagery (20 August 2018). A descriptive statistic of the measured cotton LAI at three observation dates is shown in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Satellite** & **Date (dd/mm/yy)** & **Field Campaign Date** & **Quadrats** \\ \hline Sentinel-2B & 6 June 2018 & 1–3 June 2018 & 23 \\ Sentinel-2A & 11 July 2018 & 8–11 July 2018 & 58 \\ Sentinel-2A & 20 August 2018 & 18–21 August 2018 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-2 imagery and the corresponding field campaign data.