esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 3 Proposed Methods
### Design of Backstepping Linear Active Disturbance Rejection Controller  
#### 3.2.1 Controller Design of Velocity Subsystem  
For Equation (17), take the velocity error: \(e_{V}=V-V_{d}\), where \(V_{d}\) is the reference velocity trajectory, and \(V\) is the actual velocity trajectory, and then derive the velocity tracking error as follows:  
\[\dot{e}_{V}=\dot{V}-\dot{V}_{d} \tag{21}\]  
Bringing Equation (17) into Equation (21),  
\[\dot{e}_{V}=f_{V}+g_{V}\beta_{c}-\dot{V}_{d} \tag{22}\]  
The controllable rate of the combination of Formulas (21) and (22) is the following:  
\[\beta_{c}=\frac{1}{g_{V}}\Big{(}\dot{V}_{d}-f_{V}-c_{1}e_{V}\Big{)} \tag{23}\]  
Figure 1: Hypersonic aircraft backstepping LADRC control system structure diagram.  
Figure 2: Backstepping LADRC control structure diagram.  
where \(c_{1}\) is an adjustable normal number.  
The velocity subsystem represented by the definition \(x_{1V}=V\) and \(x_{2V}=f_{V}\) in Equation (17) can be expressed in the form of the following state space expression:  
\[\left\{\begin{matrix}\dot{x}_{1V}=x_{2V}+g_{V}\beta_{c}\\ \dot{x}_{2V}=\dot{f}_{V}\\ y_{V}=x_{1V}\end{matrix}\right. \tag{24}\]  
The state variables of the system represented by Equation (24) are observed using the LESO. The observation equation of the LESO is shown by the following Equation (25):  
\[\left\{\begin{matrix}\varepsilon_{V}=z_{1}-y_{V}\\ \dot{z}_{1V}=z_{2V}-\beta_{01}^{V}\varepsilon_{V}+g_{V}\beta_{c}\\ \dot{z}_{2V}=-\beta_{02}^{V}\varepsilon_{V}\end{matrix}\right. \tag{25}\]  
The tracking of the state variables of the original system (24) can be realized when the system reaches stability through the observation of the LESO. According to Equations (23) and (25), the controllable rate is as follows:  
\[\beta_{c}=\frac{1}{g_{V}}\Big{(}\dot{V}_{d}-z_{2V}-c_{1}e_{V}\Big{)} \tag{26}\]
## 4 Case Study  
The presented framework is tested on two \(IEEE\) systems, i.e., \(IEEE\)-14 bus and \(IEEE\)-118 bus system. To apply multi-area state estimation, the \(IEEE\)-14 bus system is dived into 4 control regions [4] while IEEE-118 is partitioned into 3 regions [13]. In all cases, MATLAB package MATPOWER [22] is used to generate measurement sets with Gaussian noise for each scenario. The measurement sets consist of line power flows (real and reactive) for each line, bus power injections, and all voltage magnitudes. The implementation of the proposed framework and evaluation of results were conducted using MATLAB.
## 4 Conclusions  
A Pd/C catalyst was synthesized via an in situ synthesis method, using hydrogen gas as a reducing agent. The incorporation of 'Pd' nanoparticles on the surface of active carbon led to strong catalytic activity of the resultant catalyst. To examine the effect of the amount of 'Pd' on the catalytic activity of the catalyst, various catalysts were prepared by varying the palladium content. The as-prepared catalysts were identified by XRD, SEM, EDX and BET. The Pd/C catalyst containing 3% Pd demonstrated excellent catalytic activity toward the Suzuki-Miyaura and Mizoroki-Heck reactions. Moreover, the catalyst's reusability was also verified by repeated execution of the same reaction using the reused catalyst. The 3% Pd/C catalyst showed exceptional reusability even after numerous reactions.  
The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app11114822/s1. Figure S1: \({}^{1}\)H-NMR of Compound **2a**, Figure S2: \({}^{13}\)C-NMR of Compound **2a**, Figure S3: \({}^{1}\)H-NMR of Compound **2b**, Figure S4: \({}^{13}\)C-NMR of Compound **2b**, Figure S5: \({}^{1}\)H-NMR of Compound **2c**, Figure S6: \({}^{13}\)C-NMR of Compound **2c**, Figure S7: \({}^{1}\)H-NMR of Compound **2d**, Figure S8: \({}^{13}\)C-NMR of Compound **2d**, Figure S9: \({}^{1}\)H-NMR of Compound **2e**, Figure S10: \({}^{13}\)C-NMR of Compound **2e**.  
Conceptualization, H.M.A., O.F.A., M.H.A., S.F.A. and M.R.S.; methodology, H.M.A. and M.K.; software, M.R.S.; validation, O.F.A., S.F.A. and M.R.S.; formal analysis, M.H.A., R.L.A., M.S.A., M.H.M., M.S.I. and M.K.; investigation, S.F.A. and M.R.S.; resources, H.M.A., O.F.A., S.F.A. and M.R.S.; data curation, M.R.S.; writing--original draft preparation, O.F.A., S.F.A. and M.R.S.; writing--review and editing, S.F.A. and M.R.S.; visualization, O.F.A., M.H.A., S.F.A. and M.R.S.; supervision, A.A.; project administration, O.F.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** O.F.A. extends his appreciation to the Deanship of Scientific Research at Majmaah University for funding this research (Grant No. R-2021-135).  
Figure 10: Recyclability studies for the Pd/C 3% catalyst, using \(p\)-bromoanisol as the main reactant for the Mizoroki–Heck reaction.
## 4 Discussion and Future Research Directions
### Limitations of the Current SLR  
Hybrid methods in the field of flood indirect impacts on transportation refer to the combination of different data sources and methods, such as quantitative and qualitative data, remote sensing, and GIS. The limitations of the current SLR include:  
* Limited availability of studies: As with any systematic review, one of the main limitations is the potential for a limited number of studies that meet the inclusion criteria, particularly when focusing on a specific area, such as hybrid methods in flood indirect impacts on transportation.
* Heterogeneity of studies: Hybrid methods can encompass a wide range of data sources and methods, which may be used differently across studies. This can create challenges for comparing and synthesizing findings across studies, and can limit the generalizability of the findings.
* Quality of included studies: The quality of studies included in a systematic review can have a significant impact on the validity of the review findings. With hybrid methods, studies may use different combinations of data sources and methods, which can affect the quality of the studies included in the review. Nevertheless, since the database for the current SLR was selected from peer-reviewed publications, authors assumed that the quality and reliability of the studies included in the review have already been addressed.
* Publication bias: As with any systematic review, publication bias is a potential limitation. This can occur when studies with significant or positive results are more likely to be published than those with negative or inconclusive results.
* Language bias: As with any systematic review, language bias can be a limitation. Studies published in languages other than those included in the review may be missed, which could limit the scope of the review.
* Time constraints: Conducting a comprehensive systematic review of hybrid methods in the field of flood indirect impacts on transportation can be time-consuming. This may limit the depth of the review, potentially resulting in missed studies or incomplete analysis.
* Difficulty in synthesizing qualitative and quantitative data: Hybrid methods often involve the combination of qualitative and quantitative data, which can be challengingto synthesize in a systematic review. This can be due to differences in data types, measurement scales, and analysis methods.
## 2 Related Work
### Multicast Architectures
#### 2.1.4 ILNP-Based Multicast  
Identifier-Locator Network Protocol (ILNP) [46] is an experimental, evolutionary enhancement to IP, which introduces two distinct name spaces: (1) Identifier which represents a non-topological name for uniquely identifying a node; (2) Locator which represents a topologically bound name for an IP subnet. ILNP uses locator in network layer, and uses Identifier in transport layer, with the objective to split dual roles of IP in terms of identifier and location.  
The multicast forwarding and routing in ILNP are unchanged and inherit the IP multicast. Therefore, similar to LISP-based multicast, it inherits the shortcomings of IP multicast.
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Conditional Value-at-Risk  
Although the weighted summation of the \(K\) clustered scenarios can enhance the economic returns of the wind farm to a certain extent, it remains difficult to effectively mitigate significant profit drops caused by extreme scenarios, particularly under conditions of large forecasting errors or distributional deviations. To further enhance the robustness of the submitted schedule across different scenarios, a Conditional Value-at-Risk (CVaR) constraint is incorporated into the profit maximization objective function.  
CVaR is a risk management tool used to measure the expected loss beyond a specified threshold under the worst-case scenarios. Compared with traditional risk metrics, CVaR captures tail risks more effectively and better reflects potential losses under extreme scenarios, thus offering enhanced robustness when dealing with wind power forecasting errors and other uncertainties. A confidence level \(\alpha\) is specified, and two auxiliary variables, \(\zeta\) and \(\tau_{k}\), are introduced into the model. CVaR is typically used to assess losses under extreme scenarios, whereas the objective function in this study focuses on economic gains in wind power systems. Therefore, to apply CVaR in the context of revenue optimization, the profit is negated and transformed into a loss function. Through this transformation,maximizing CVaR becomes equivalent to minimizing losses in worst-case scenarios, thereby enhancing the system's performance under extreme conditions. The specific expression is given as follows:  
\[CVaR_{\alpha}(C)=\zeta-\frac{1}{1-\alpha}\sum_{k=1}^{K}w_{k}\tau_{k} \tag{16}\]  
where \(\zeta\) is the baseline value representing the quantile. \(\tau_{k}\) denotes the deviation between the revenue in scenario \(k\) and the quantile baseline. When the revenue in scenario \(k\) falls below \(\zeta\), \(\tau_{k}\) increases accordingly.
## 5 Results
### Result of Variable Importance Analysis  
The results of the assessment of the variable importance based on the mean decrease of Gini coefficient using RF model are shown in Figure 11. In addition, the coefficients of logistic regression which also indicates the importance of the variables are shown in Table 6. As Figure 11 shows, all the  
Figure 10: Area under the curves based on training datasets (success rate curve): (**a**) individual models, (**b**) ensemble of two models, and (**c**) ensemble of three or four ensemble models; based on validation datasets (prediction rate curve): (**d**) individual models, (**e**) ensemble of two models, and (**f**) ensemble of three or four ensemble models.  
causative factors generally contributed to the landslide susceptibility model. However, altitude is the most important factor, followed by drainage density, road density, lineament density and slope respectively. Like the mean decrease of Gini, the coefficients of logistic regression (Table 6) also indicate a strong relationship between the landslide occurrence and the altitude, rainfall, drainage density, road density and distance from lineament. These results showed that most of the landslides occurred in elevated areas with high drainage density and road density.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Landslide Causative Factors**} & **Coefficients of Logistic Regression (B)** & **Landslide Causative Factors** & **Coefficients of Logistic Regression (B)** \\ \hline Altitude & 1.461 & Geology & 0.122 \\  & & (Genesis-magnatites) & 0.122 \\ Slope & 0.028 & Geology (Tourmail & 2.478 \\  & & granite) & 2.478 \\ Aspect (Flat) & \(-\)1.185 & Geology (Chail-Ranghat) & \(-\)3.412 \\ Aspect (North) & \(-\)0.179 & Geology (Granite 500 Ma) & \(-\)1.744 \\ Aspect (North-east) & 0.348 & Geology (Salkhlas) & 1.062 \\ Aspect (East) & \(-\)0.447 & Geology (Shaiel Neoban) & \(-\)0.513 \\ Aspect (South-east) & 0.531 & Geology (Nagthal) & \(-\)1.185 \\ Aspect (South) & \(-\)0.632 & Geology (Chadpur) & \(-\)0.179 \\ Aspect (South-west) & \(-\)1.185 & Geology (Chamoli Qz) & 0.348 \\ Aspect (West) & \(-\)1.32 & Earthquake zone (High) & 0.348 \\  & & Earthquake zone & \(-\)0.447 \\ Aspect (North-West) & \(-\)2.199 & (Moderate) & \(-\)0.447 \\ Curvature & 0.976 & Major Road density & 1.241 \\ SPI & 0.026 & NDVI & \(-\)0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Logistic regression coefficients of landslide causative factors.  
Figure 11: Weights of landslide causative factors calculated by RF.
## 4 Discussion  
### Model Comparison  
Accuracy comparison between Geoman and the previous models in estimating RE in northern China's grasslands is displayed in Table 6. Geoman performed best, with the best explanation and minimum bias, when compared with seven previous models, including two semi-empirical models, one satellite-based model, three traditional machine-learning models, and one deep-learning model. Compared with semi-empirical models, deep-learning models can automatically extract the complex nonlinear dependence relationship between the target variable and the explaining variable, without excessive restriction from predetermined theoretical assumptions about complicated respiration processes. Based on similar methods, carbon flux estimations such as gross primary productivity (GPP) have been used as benchmarks and are widely employed in the evaluation and improvement of process models [62; 63; 64]. The combination of LSTM and the attention mechanism in the encoder and decoder stages further improves the predictive ability of the Geoman model (see Section 3.1.1). The LSTM in Geoman can adapt to time-varying behaviors and use dynamic data to update the parameters to improve the predictions. The local spatial  
Figure 9: The correlation between the mean annual RE and (**a**) average SOCD, (**b**) average clay content, (**c**) average silt content and (**d**) average sand content for all grids in each grassland subtype.  
attention module provides a means of distinguishing the importance of different input variables. The time attention module gains insight into past patterns of encoder outputs for different environment variables, and decoder outputs for the target variables. However, this insight can be difficult to model using traditional ML models.  
Despite the many challenges in adopting DL in ecology, DL can provide predictions with unparalleled precision without prior hypotheses. Geoman has been designed to make time-series predictions by understanding the impact of multiple explanatory variables on the target variables. However, the interpretation of the prediction results from the DL model structure is often insufficient, because the impact of the inputs on the outputs cannot be easily understood through neural networks [30]. In this study, an attempt was made to use the attention mechanism to explain the impact of environmental variables on RE, ecologically meaningful results were generated (see Section 3.1.2). The integration of DL models and ecological-process-based models represents a promising development in the domain of ecological study, and should substantially improve the interpretability of the DL models [65].
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 4 Effect and Optimization of Multiple Blasting Vibrations on Bridges Under Construction  
### Relationship between Blasting Vibration Velocity and Blasting Frequency of Bridge Structures  
To perform a macro-analysis of the overall condition of the bridge, peak vibration velocity data from measurement points on the bridge deck, bridge pile, and pier were selected for multiple cumulative blasting analyses. Specifically, the peak vibration velocities at Bridge Deck Measurement Point 11 and Pier Measurement Point 1 were measured multiple times, while the peak vibration velocity at bridge column measurement point 6 was simulated and predicted. These results are depicted in Figure 15. Examination of Figure 15 reveals irregular changes in vibration velocity for the first five peak vibration velocities at the three measurement points. However, from the fifth vibration velocity onwards, a consistent gradual increase followed by a gentle trend can be observed, all of which remain below 0.25 cm/s. Hence, the peak vibration velocity induced by cyclic blasting falls below the bridge construction safety standard requirement of 2.5 cm/s [28, 29, 30].  
Additionally, regression calculation and analysis of the change in blasting vibration at identical monitoring points can establish the relationship between the bridge structure and blasting frequency, thereby elucidating the overall trend of vibration velocity at the measuring point. The curve depicting the relationship between blasting vibration values at the measuring point and blasting frequency is illustrated in Figure 16.  
\[F(x)=0.00255x^{3}+0.02^{3}+0.18x+0.13 \tag{7}\]  
Regression calculation and analysis yield the polynomial regression equation for unit blasting vibration velocity and blasting frequency, as depicted in Equation (7), with a confidence level exceeding 0.95. The findings indicate that the relationship between blasting damage and blasting frequency of the bridge structure conforms to a cubic polynomial relationship. Moreover, although no damage occurs in this simulation after eight blasting events, there is an increase in the peak value of overall blasting vibration velocity. Hence, optimization of the current blasting scheme is imperative.  
Through regression analysis, the polynomial regression equation for unit blasting vibration velocity as a function of the number of blasts is obtained, as shown in Equation (7),  
Figure 15: Vibration data from multiple blasting events on the bridge deck, pile, and pier.  
with confidence levels exceeding 0.95. The results indicate that the relationship between blasting damage to the bridge structure and the number of blasts follows a cubic polynomial pattern. Additionally, according to the trend of this equation, as the number of blasts increases, the blasting vibration velocity initially increases and then begins to decrease. Although no damage is observed, the overall peak blasting vibration velocity increases, suggesting a need to ameliorate the current blasting plan.
## 2 Study Area and Data
### Soil Moisture Dataset  
To estimate the initial soil moisture condition prior to flood events, the European Space Agency Climate Change Initiative (ESA-CCI) data is considered. ESA-CCI V04.2 is a soil moisture product ([[http://www.esa-soilmoisture-cci.org/](http://www.esa-soilmoisture-cci.org/)]([http://www.esa-soilmoisture-cci.org/](http://www.esa-soilmoisture-cci.org/))) available over a 38-year period from 1978 to 2018. This database includes three products: active, passive and combined. It provides daily soil surface moisture with a spatial resolution of 0.25 degrees [31; 45; 46]. The product has been validated by [47], and recently validated in the High Atlas of Marrakech by [18; 48] for flood modeling. The ESA-CCI product shows in the mountainous area some missing values [49] that are related to a filter that is applied to the ESA-CCI product in order to ensure the data quality [45]. The ESA-CCI soil moisture pixel covering the Ghdat basin has been extracted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Type** & **Longitude** & **Latitude** & **Altitude (m)** \\ \hline Sidi Rahal & Rain and river discharge & \(-\)7.47 & 31.64 & 687 \\ Adrar Gardouz & Rain & \(-\)7.44 & 31.46 & 2285 \\ Tijdante & Rain & \(-\)7.39 & 31.46 & 1122 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Situation and period of measurement of hydro-rainfall stations.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 4 Simulation Results and Discussion
### Minimum Power Consumption Simulation of UAVs Base Station
Based on this, we can conclude that UAV base stations in rural environments can choose larger deployment heights and smaller transmitting power, such as a 200 m hover height deployment and a transmitting power of 20 dBm. UAV base stations can be deployed at a moderate altitude and transmit power, for example, at a hovering altitude of 150 m and transmit power of 30 dBm. UAV base stations in urban environments can be deployed at a lower altitude and with a larger launch power, such as a 100 m hover deployment with a launch power of 40 dBm. Thus, the QoS can be maintained while covering a large area of ground users to be rescued, and the power consumption efficiency of the UAV base station can be improved.
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 5 Nighttime Stargazing Experiment
Figure 15: The histogram regarding gray values and magnitude values. The asterisks represent the gray values corresponding to the magnitude values. The red dashed line represents the variation trend of the gray value when the magnitude value increases. With the increasing magnitude value, the disorder of the gray value indicates the presence of a magnitude error in the captured star image. Generally, higher magnitude corresponds to lower brightness; however, as shown in Figure 15, the gray values of stars do not consistently decrease as the magnitude increases. Specifically, star 9, with a magnitude of only 4.36, has a significantly higher gray value than star 2, which has a magnitude of 3.19. This indicates that a magnitude error is indeed present in the star image when the star sensor works in practice. Nevertheless, the proposed algorithm successfully identifies the star image, even with significant brightness interference, further demonstrating the robustness of the proposed algorithm against the magnitude error. The robustness is attributed to the fact that the brightness of star is not used as a feature during the process of star identification, making the algorithm less susceptible to the effect of magnitude errors. To thoroughly validate the identification performance of the proposed algorithm, the rotation of the telescope is controlled and the star sensor is guided to capture 5000 frames of star images corresponding to different celestial regions. Different star identification algorithms are applied to identify the stars in the star images, and the average identification rate and average identification time for each algorithm are recorded and listed in Table 3.  
The results in Table 3 demonstrate that, under the same experimental conditions, the proposed algorithm achieves an average identification rate of 99.760% and an average identification time of 8.861 milliseconds. Both the identification rate and time are significantly better than those of the other algorithms, further confirming the effectiveness of the proposed star identification algorithm in the application of a star sensor.