esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Method
### Error Compensation Model  
In order to quickly and accurately control the pose of the table, the control strategy of \"first attitude, then position\" is adopted. Firstly, the error compensation model is used to compensate for the attitude error of the table. Subsequently, any positional discrepancies resulting from this adjustment are addressed, targeting the desired position. The inverse kinematics model is then employed to determine the control volume for each hydraulic cylinder. This paper predominantly examines the error coupling relationship between pitch and roll angles and only investigates the error compensation methods for these two, with others being excluded from the discussion. The error compensation model for both pitch and roll layers can be represented as  
\[\Delta^{*}=A-E \tag{11}\]  
Figure 6: Coupling errors from pitching motion.  
where \(\mathbf{A}^{\star}=\begin{bmatrix}\hat{x}^{\star}&\hat{y}^{\star}\end{bmatrix}^{\sf T}\) denotes the actual angle of motion, \(\mathbf{A}=\begin{bmatrix}\hat{x}&\hat{y}\end{bmatrix}^{\sf T}\) denotes the target angle, and \(\mathbf{E}=\begin{bmatrix}\varepsilon_{x}&\varepsilon_{y}\end{bmatrix}^{\sf T}\) denotes the coupling error. Combining Equations (5)-(11), the inverse kinematic model after error compensation can be obtained as  
\[s_{4}=\sqrt{m_{4}^{2}+r_{4}^{2}+r_{4}\Delta r_{4}-(2r_{4}+\Delta r_{4})m_{4} \cos(\hat{x}_{4}-\arctan\frac{\Delta r_{5}\cos\hat{y}_{5}}{L_{5}}+\theta_{4 \text{m}})} \tag{12}\]  
\[s_{5}=\sqrt{m_{5}^{2}+r_{5}^{2}+r_{5}\Delta r_{5}-(2r_{5}+\Delta r_{5})m_{5} \cos(\hat{y}_{5}-\arctan\frac{\Delta r_{4}\cos\hat{x}_{4}}{L_{4}}+\theta_{5 \text{m}})} \tag{13}\]
# Design of the Input and Output Filter for a Matrix Converter Using Evolutionary Techniques
## 6 Results
### Optimal Solutions  
- Input filter:  
The tuning parameters of DE for the input filter were:  
* Design variables: 3
* Individuals: 50
* Generations: 500
* Mutation factor: random from 0.4 to 0.9
* Cross factor: random from 0.3 to 0.9  
The mutation factor was randomly obtained by generation, while the cross factor was randomly generated for each run, in order to diversify the exploitation and exploration while solving the optimization problem. The population number was determined in the tuning to have a large initial exploration and thus achieved the convergence of the algorithm in a smaller number of generations. The algorithm was executed 30 times to observe its convergence, and it produced the same results in each run. Table 1 lists the best individual of each execution, and for all of them, the value of the objective function was \(-33.4705013527862\) dB. The statistical analysis of the results is presented in Table 2. As can be seen, the variance and the standard deviation were almost zero, which indicated the stability of the algorithm. Additionally, the convergence graph is presented in Figure 6 including the best, the average, and the worst convergence behavior of the thirty runs. It can be seen that differential evolution converged to the same minimum value of the objective function. The values obtained in the design vector in the 30 runs were the same; therefore, it could be concluded that these were the best optimal values found for the optimization problem. - Output filter:  
The results obtained for the output filter are presented. The tuning parameters of DE were as follows:  
* Design variables: 4
* Individuals: 20
* Generations: 30,000
* Mutation factor: random from 0.6 to 0.9
* Cross factor: random from 0.5 to 0.9  
As for the input filter case, the mutation factor was randomly obtained by generation, while the cross factor was randomly generated by run. The algorithm was evaluated thirty times. Table 3 shows the best result of each run. For each individual, the value of the objective function was \(126.767966149519\) dB.
## 3 Analysis of the Collapse Mechanism of the Surrounding Rock of Large-Span Caverns
### Solution to the Collapse Curve of Rock Surrounding a Large-Span Cavern Vault
 \tag{29}\]  
where \(P\) is the generalized pressure, which can be expressed as  
\[P=\frac{1}{L}\gamma\int_{0}^{L}c(x)dx\]  
Substitute Equations (26) and (27) into Equation (20); and combine the principle of virtual work to obtain  
\[\gamma kL^{\frac{1}{B}}B(1+B)^{-1}-\sigma_{t}\ +P=0 \tag{30}\]  
Substitute with Equations (26) and (27), and the expression of the maximum half width of the collapse body can be obtained as  
\[L=AB^{-B}(1+B)^{B}\gamma^{-1}\sigma_{c}^{1-B}(\sigma_{t}\ -P)^{B} \tag{31}\]  
The expressions of the collapse body height h is  
\[h=(\gamma B)^{-1}(1+B)(\sigma_{t}\ -P) \tag{32}\]  
Substitute Equation (32) into Equation (25), and the collapse body curve equations can be obtained as  
\[\left\{ \begin{array}{c}f(x)=kx^{\frac{1}{B}}-h\\ k=A^{-\frac{1}{B}}\left(\frac{\gamma}{\sigma_{c}}\right)^{\frac{1-B}{B}}\end{array} \right. \tag{33}\]  
According to Figure 1, the coordinates of the dome center of the large cavern are \(\left(0,-\sqrt{R^{2}-\ L^{2}}\right)\), and the expression of the curve \(c(x)\) can be obtained as follows:  
\[c(x)=\sqrt{R^{2}\ -x^{2}}\ -\sqrt{R^{2}\ -L^{2}} \tag{34}\]  
where \(R\) is the radius of the long-span cavern vault curve \(c(x)\), and the generalized pressure is  
\[P=\gamma\frac{1}{2L}R^{2}\left[\alpha rc\sin\frac{L}{R}-\frac{L}{R}\sqrt{1- \left(\frac{L}{R}\right)^{2}}\right] \tag{35}\]  
Substituting Equation (35) into Equation (31), it can be found that  
\[L-AB^{-B}(\ 1+B)^{B}\gamma^{-1}\sigma_{c}^{1-B}\left\{\sigma_{t}\ -\gamma\frac{1}{2L}R^{2}\left[arcsin\frac{L}{R}-\frac{L}{R}\sqrt{1- \left(\frac{L}{R}\right)^{2}}\right]\right\}^{B}=0 \tag{36}\]  
In the above equations, all other parameters except \(L\) are known. After \(L\) has been obtained using MATLAB numerical calculation software, \(P\) can be calculated, and the expressions of the collapse body height \(h\) and the collapse line \(f(x)\) can be obtained in sequence.
## 4 Numerical Verification for Design
### Results Analysis
#### 4.4.3 Stress Response  
It was observed that the amplification of acceleration is intensified depending on the form and material of the support system of the target structure. Accordingly, the location most affected by this was selected, and the stress generated in the structure was reviewed, as shown in Figure 11. The review locations were determined to be three areas where stress concentration occurs: the connection point between the vessel body and support column (S1), the bottom plate of the support column (S2), and the anchor (S3). When examining the stress in isotropic materials such as steel, Von Mises stress was investigated. At location S1, which is the welded connection between the vessel body and the support column, the maximum stress was observed to be 64.30 MPa, the highest among the three measured locations. The maximum stress for the bottom plate of the support column and the anchor were relatively similar, measuring 41.33 MPa and 42.09 MPa, respectively. As a result, the maximum stresses observed at all measured locations are below the yield strength of the common structural steel SS 400, which is 225 MPa. Therefore, it is concluded that the structural integrity of the target structure is ensured.  
Figure 11: _Cont._  
Figure 10: Acceleration response by location of target structure according to seismic load: (**a**) footing concrete (A1), (**b**) support column (A2), (**c**) middle of the hydrogen storage vessel (A3), and (**d**) top of the hydrogen storage vessel (A4).
# Synthesis of Si-Fe Chondrule-like Dust Analogues in RF Discharge Plasmas
## 5 Conclusions and Future Prospects  
This research analyses the nanoscale properties of Si-Fe nanomaterials using SEM coupled with energy-dispersive spectroscopy, TEM, Mossbauer spectroscopy, and XRD (before/after) exposure to a high-energy pulsed plasma beam to simulate rapid heating. In non-thermal RF discharge, vapour from feedstock moves into a plasma region, where it fragments and nucleates into particles, forming larger powder particles. Elemental Fe inclusion was confirmed through EDS analysis. The results suggest that a non-thermal plasma environment can serve as a lab-scale platform to study the formation of chondrule-like particles using rapid heating sources to mimic interstellar conditions. We believe that the data obtained open new perspectives and provide valuable insights into the initial formation process of chondrule particles. Future work could focus on creating more complex chondrule-like structures by incorporating precursors of other common chondrite elements such as magnesium, copper, and aluminum.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 3 MSE Growth of GaN Thin Films Using Liquid Ga Target
### Doping and Fabrication of III-Nitride Devices Grown Using MSE
At lower temperature, more indium can be incorporated, allowing for the growth of full color LED structure [11]. An example of full color LEDs fabricated from III-nitride deposited on a flexible metal foil is shown in Figure 6. Another advantage of the low growth temperature in PSD is the reduced strain and increased critical thickness for GaN films. [PERSON] et al. [60] has demonstrated an AlGaN/GaN HEMT structure on a Si(110) substrate with a much higher critical thickness compared to MOCVD-grown structure. The heterostructure exhibits a two dimensional electron gas at the interface, with a mobility of 1360 cm\({}^{2}\)/Vs and a sheet carrier density of \(1.3\times 10^{13}\) cm\({}^{-2}\). The fabricated device and its output characteristics are shown in Figure 7.  
Figure 6: (**a**) Schematic illustration of the pulsed sputter deposition (PSD)-grown light-emitting diode (LED) on Hf foil. (**b**) Optical photograph of GaN-based LED arrays grown on flexible Hf foil. (**c**) EL spectra of a blue LED at various injection device. Inset shows the LED at 8 mA injection current. (**d**) Optical photographs of green and red LEDs. (**e**) Photograph of the blue LED operating at a bending radius of 5.0 mm [11]. Reproduced with permission from Nature Publishing Group. Figure 7: (**a**) Schematic of an AlGaN/GaN HEMT structure grown using PSD on a Si(110) substrate. (**b**) \(\mathrm{V_{ds}}\)-\(\mathrm{I_{ds}}\) curve of the HEMT, with \(\mathrm{Vg}\) varied from \(-4\) V to 1 V [60]. Reproduced with permission from AIP publishing.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 6 Conclusions  
The spatial-temporal distribution of the FSR is vital for fire management; for example, the establishment of firebreaks and the evacuation of personnel. The near real-time FSR extracted from the Himawari-8 data can provide more information for fire agencies in predicting the location of fire propagation. This in turn could lead to improvements in the efficiency of operational firefighting. This paper first defined the fire center as the burned area centroid and then proposed a novel method for near real-time FSR extraction based on the movement rate of the fire center which is extracted from the high-temporal resolution Himawari-8 data. The methodology was applied to a wildfire case that occurred in Esperance, Western Australia on 17 November, 2015. The results demonstrated that the H8-FSR could extract the FSR from Himawari-8 data well, compared with the CSIRO GFS model. The accuracy of burned area extraction and the scale of wind may also affect the efficacy of H8-FSR, which was discussed in Section 5 and remains to be investigated further. Further improvement in terms of the H8-FSR and its application to other vegetation types such as forest and shrubland, and testing the H8-FSR with other geostationary satellites needs to be implemented. Such satellites could include GaoFen-4 (GF-4), Feng Yun-4 (FY-4), GEO-R and Next Geostationary Korea multipurpose Satellite-2A (GK-2A). This would serve to improve the development of a global geostationary fire observation and prevention system.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; and [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper. All of the authors contributed to the editing of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Contract No. 41671361 and 41601373 and 41801272), the Fundamental Research Fund for the Central Universities (Contract No. ZYCX2017 KYQD195).  
The authors would like to thank the JAXA for providing the Himawari-8 data, as well as the Australian Bureau of Meteorology for contributing weather information.  
The authors declare no conflicts of interest.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator  
### Structure of the NOMA Link-Level Simulator  
The main structure of the MC-NOMA link-level simulator is explained in this section; however, for comparison purposes, a broad description of SC-NOMA has been briefly addressed alongside MC-NOMA. MC-NOMA uses orthogonal frequency-division multiplexing (OFDM) as a multi-carrier modulation technology that permits the simultaneous distribution of a single subcarrier to many users; however, SC-NOMA does not use OFDM processing. The structure of the downlink MC-NOMA simulator comprises three parts, i.e., transmitter, channel and receiver as shown in Figure 1, and its details have been provided in the following subsections. Error-free propagation for signaling is assumed for simplicity. The reason for this assumption is supported by the fact that signaling channels only face errors if the data channels are already facing severe conditions as mentioned in [10]. Signaling information usually comprises a precoding matrix indicator (PMI), channel quality indicator (CQI), coding, and rank indicator. The uplink transmission, which works mainly on the CQI, is not considered in the initial stage of the simulator. However, it will be added to the next version of the simulator.
## 4 Results and Discussion
### Mineralogy of PG Amended Lime-Stabilized Soil
The peak intensities of ettrinigite at a 2-theta value of 35.2\({}^{\circ}\) were 971, 996 and 1108 for combinations LP1, LP2 and LP3, respectively. Looking at peak intensities of CSH at 23.5\({}^{\circ}\), they also increased with an increase in the lime content; 1016, 1071 and 1152 counts for LP1, LP2 and LP3, respectively. At a 2-theta value of 29.3\({}^{\circ}\) as well, a similar trend was seen with intensities of 1062, 1153 and 1271 counts, respectively, for LP1, LP2 and LP3. Thus, there is an increase in the intensity of ettrinigite and CSH with an increase in the lime and PG content in the combinations which is the reason for the augmented strength of the stabilized soil composite. Moreover, comparing the three combinations, the XRD studies reveal that the destruction of soil minerals and the formation of reaction products is most prominent in the combination of ICL with 0.5% PG when compared to the other two. Thus, in the present study, the micro-level investigation clearly substantiates the behavior noticed in the stress-strain characteristics and reinforces the inference that the PG modification of lime-stabilization can be at its most advantageous at ICL rather than OLC. Figure 7 shows a higher resolution SEM image of 7% lime-stabilized soil amended with 1% PG reported in earlier work [23]. From the figure, it is clear that there are clusters of leaf-like formations indicating the development of CSH gel due to pozzolanic reactions. The formation of CSH is clearly evident from the XRD diffractograms. The SEM micrograph also shows the presence of folded solidified gel. The formation of a similar microstructure was also reported by [PERSON] et al. [34]. In the earlier work by [PERSON] and [PERSON] [23] it was reported that the strength development may also be due to the possible onset of the formation of ettrinigite (Aft). Needle/rod-like formations, indicative of the onset of ettrinigite formation, are also seen in the micrograph. This is also supported by the presence of ettrinigite in the XRD diffractograms in the present study. The overall microstructure appears to be a dense mass-like structure with few micro and macro pores in the field of view. [PERSON] et al. [34] also report the formation of a dense compact microstructure due to stabilization. Higher-resolution SEM images of LP1 and LP2 (Figure 8) from an earlier investigation [23] have also been provided for comparison. Figure 6: Mineralogy of (**a**) LP1 (**b**) LP2 and (**c**) LP3.
## 2 Materials and Methods
### MODIS Reflectances and Related Vegetation Parameters
#### 2.1.2 Use of Linear Combinations of Bands as an FAPAR\({}_{\rm chl}\) Proxy  
In place of VIs or modified VIs in Equations (1) or (2), we alternatively use the form:  
\[\mathrm{FAPAR}_{\rm chl}\propto(A_{0}+\sum_{i=1}^{N}A_{i}\rho_{i}), \tag{7}\]  
where \(i\) is a particular reflectance band, \(N\) is the number of bands considered and \(\rho_{i}\) are combined using weights \(A_{i}\) and an offset \(A_{0}\). [PERSON] and [PERSON] [62] similarly used a neural network to estimate a SIF-based FAPAR\({}_{\rm chl}\) proxy using MODIS reflectances. We examine the results below applied to GPP estimation for two different combinations of MODIS bands: (1) Bands 1 and 2 (red and NIR\({}_{1}\)) that are used to compute NDVI and NIR\({}_{V}\) and (2) Bands 1-7 that are generally used for terrestrial remote sensing. Note that from MODIS, the first two bands are measured at \(\sim\)250 m, while the second group is measured at \(\sim\)500 m. Bands 1 and 2 or similar bands are also available on a number of other satellite sensors, such as the Advanced Very-High Resolution Radiometer (AVHRR) dating back to the early 1980s (albeit at lower spatial resolution than MODIS) and very high spatial resolution imagers such as on the Landsat and Sentinel 2 satellites. The additional MODIS bands may be useful for further removal of signals from non-chlorophyll-containing substances or atmospheric effects, as well as for noise reduction in the case of redundant information.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 5 Conclusions  
This paper proposed a monitoring system of a transmission line in a mountainous area based on LPWAN. Considering thee different failure rates, three monitoring states of transmission lines in mountainous areas were proposed. According to the requirements of energy and delay constraints, an online monitoring framework of transmission lines in mountainous areas was designed based on the LoRa and cellular mobile network, and a dynamic group network model of LoRa was established. The multi-objective particle swarm optimization algorithm was used to optimize the energy consumption and time delay of the system, and then the suitable working mode for the three monitoring periods can be obtained.  
The simulation results showed that the minimum packet loss rate of the system could be less than 1%, the energy consumption of the system was 80% lower than the existing monitoring system, and the service life of the system could reach 15.13 years under the normal failure rate. Through the conversion of the working mode, the system could achieve the communication quality required of the online monitoring of transmission lines and greatly reduce the energy consumption of the system at the same time. Compared with the existing schemes, the proposed work shows the advantages of high reliability transmission, low cost, and long-term monitoring, which is particularly important for transmission line monitoring in mountainous areas.  
Conceptualization, H.Z. and F.D.; Methodology, P.Z. ([PERSON]) and P.Z. ([PERSON]); Validation, P.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China, grant number 51767006\", Key Research and Development Plan of Jiangxi Province grant number 20181 BBE50019\", 20181 BBE50009\", 20202 BBGL73098\", Key projects of Natural Science Foundation of Jiangxi Province, grant number 20202 ACBL214021\", and Science and Technology Project of Education Department of Jiangxi Province, grant number GJJ170378\".  
The authors declare no conflict of interest.  
The data used to support the results of this study have not been provided because the author's college does not allow the data to be published.
## 3 Reinforcement Learning Method
### SGD SARSA with Linear Function Approximation
In these methods, an area of varying shape and size is defined in the state-action space, representing each feature in the feature vector. If a given state-action pair is within this area, the corresponding element in the feature vector has the value of one and is a zero otherwise. The Gaussian RBF method is quite similar to the tile/coarse coding methods, but the features are defined as bell curves with a center point in the state space and some width \(\sigma\), as shown in (17), where \([SA]\) is a vector of scalars holding the current state and action. \[\mathbf{X}(S,A)=e^{-\frac{\|[SA]-\mathbf{C}\|^{2}}{2\sigma^{2}}} \tag{17}\]  
Using the Gaussian RBF method, the feature vector defines how close a given state-action pair is to the center point of each feature rather than the feature vector describing whether or not a given state-action pair is located within the area of each feature. Therefore, graphically speaking, the approximated Q-function representing the state-action space is continuous and smooth. For a sparsely defined feature space (i.e., few features or center points), these continuous functions may improve the accuracy of the estimation of the Q-value as defined by (16). Therefore, the Gaussian RBF method has been selected in this work. With the use of a function approximator rather than updating the Q-values directly, the SARSA update equation shown in (15) is refocused on updating \(\mathbf{W}\). If combined with an SGD method, the weight update occurs in the direction that reduces the error for the current sample [37], and the weight update can be written as follows  
\[\mathbf{W}\leftarrow\mathbf{W}+\alpha\left[R+\gamma Q(S^{\prime},A^{\prime}, \mathbf{W})-Q(S,A,\mathbf{W})\right]\cdot\
abla Q(S,A,\mathbf{W}) \tag{18}\]  
Considering the linearity of the RBF function approximation method, the gradient on the right-hand side of (18) can be simplified to the value of \(\mathbf{X}(S,A)\). Hence, the final weight update equation takes the following form:  
\[\mathbf{W}\leftarrow\mathbf{W}+\alpha\left[R+\gamma Q(S^{\prime},A^{\prime}, \mathbf{W})-Q(S,A,\mathbf{W})\right]\cdot\mathbf{X}(S,A) \tag{19}\]
## 3 Algorithm Description and Process
### Algorithm Description
#### 3.2.4 Finding a Next Cluster Needing to Adjust  
After the cluster head node \(f_{m}\) calculates the diving depth of all nodes in its cluster, it transmits the \"adjust depth\" message (diving depth, basic node ID) to the corresponding in-cluster node to notify them to dive. After all nodes dive to the specified location, \(f_{m}\) broadcasts the \"update\" message (ID, diving depth and adjusted communication radius of every node), and the other cluster head nodes that receive the message update their _all_deployed_. \(f_{m}\) then queries its last-hop cluster head nodes whether some cluster head nodes have not adjusted. Thus, \(f_{m}\) transmits \(M_{CD}\) to one of them, and the cluster head node receiving the message operates by Section 3.2.3. Otherwise, \(f_{m}\) transmits the \"been adjusted\" message \(M_{AD}\) to its next-hop \(f_{j}\), and \(f_{j}\) selects to transmit \(M_{CD}\) or \(M_{AD}\) according to the situation of its last-hop cluster head node. This process is repeated until all clusters are adjusted.  
Figure 5: Flow chart of Section 3.2.3.