esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 3 ML for Wind Energy Prediction
### Machine Learning and Wind Energy Forecast
#### 3.2.2 Random Forest Regression
Often used against the single assessment on the test set, the \"Mean\" values show the average across several folds (splits) of the dataset. * Mean Cross-Validation R\({}^{2}\) (0.64517): With an average R\({}^{2}\) value of 0.64517--lower than the test set R\({}^{2}\)--0.76087--over the cross-valuation folds. This implies that, on average, during cross-valuation, the model explains roughly 64.5% of the variation, whereas on the test set it explains about 76% of the variance. Although this difference suggests some variation in model performance over several data subsets, overall, the finding is still really strong. * Mean Cross-Validation Adjusted R\({}^{2}\) (0.64509): Considered as lower than the Adjusted R\({}^{2}\) on the test set (0.76060), the average Adjusted R\({}^{2}\) across cross-valuation is 0.64509. Like the R\({}^{2}\) score, this indicates that although the model may be slightly overfitting the test data relative to its performance on several validation sets, it generalizes somewhat reasonably. * Mean Cross-Validation MSE (0.02943): Higher than the test set MSE (0.01976), the average MSE among several cross-valuation folds is 0.02943. This implies that, on the test data, the model did rather better than on the average validation folds. Still, the variation is not significant, suggesting a rather steady performance. * Mean Cross-Validation RMSE (0.17155): Higher than the test RMSE (0.14057), the average RMSE for the validation sets is 0.17155. This suggests that, although still within a reasonable range, the model's mistakes during cross-valuation are rather greater than on the test set on average. * Mean Cross-Validation MAE (0.13200): Higher than the test MAE, 0.10439, the average MAE during cross-valuation is 0.13200. Consequently, the model performs really well over several data splits but makes somewhat more mistakes on the cross-valuation folds. Cross-validation results reveal that, when tested on several subsets of the data, the model's performance is consistent but rather less. Although the lower cross-valuation R\({}^{2}\) (0.64517) points to some variation in the generalizing capacity of the model, the test and cross-valuation metrics differ only in minor degree. Currently, the evaluation of the model utilizing the most associated features, namely 'windspeed_10m', 'windspeed_100m', and 'windgusts_10m', yields the following results:  
* R\({}^{2}\) Score: 0.67234
* Adjusted R\({}^{2}\) Score: 0.67223
* MSE: 0.02707
* RMSE: 0.16453
* MAE: 0.12603  
By picking solely the most correlated features, the model may forfeit significant interactions or information offered by less correlated variables. The optimal parameters chosen yielded a less intricate model (fewer trees, reduced depth), which may not adequately depict the previous degree of intricacy. A strong correlation may not necessarily reflect a feature's complete impact on a model's performance, particularly in non-linear models such as Random Forests.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description
### Clustering
#### 4.1.3 Common Nodes in Clusters  
The node that becomes the final cluster head broadcasts its successful campaign to the entire network. The normal node selects the best cluster head by calculating the generation value to the cluster head within the communication range and notifies the cluster head by sending a message to join. The generation value from the node to the cluster head is designed based on their distance, the distance from the sink to the cluster head, and the residual energy of the cluster head. The weight relationship between node \(s_{p}\) and cluster head \(z_{q}\) can be expressed as  
\[w_{pq}^{h}=a_{3}\frac{D_{pq}}{R_{max}}+b_{3}\frac{D_{q0}}{D_{max}}-c_{3}\frac{E_ {q}^{\prime}}{E_{q}^{\prime}}, \tag{14}\]  
where \(D_{pq}\) represents the distance between the node and cluster head; \(D_{q0}\) stands for the distance between the cluster head and sink; \(E_{q}^{o}\) denotes the initial energy of the node; \(a_{3}\), \(b_{3}\), \(c_{3}\) are adjustment factors, and \(a_{3}+b_{3}+c_{3}=1\).  
Through the above calculation, nodes select the cluster head with the minimum corresponding weight into the cluster, as shown in Equation (10), to form a network cluster. The whole process of the clustering algorithm is shown in Figure 2.
## 2 Materials and Methods  
### Plant Material and Study Site  
During the 2022 growing season, a field study was carried out at the experimental vineyard of the Food Science and Engineering College in Gansu Agricultural University, located in Lanzhou, China, at 103 deg41' E, 36 deg5' N and 1530 m above sea level. The study site is situated in the Hexi Corridor, which offers optimal conditions for cultivating wine grapes due to its exceptional combination of water, soil, sunshine, and diurnal temperature differences. In particular, the site is characterized by a temperate continental climate and experiences an average annual high temperature of 19.0 degC and an average low temperature of 5 degC. The total annual rainfall measures 429.9 mm, while the site benefits from more than 2358 h of sunshine annually. The average wind speed is recorded at 6.9 km/h. The frost-free period extends for 182 days, and there are 48 days of precipitation throughout the year. The main grape varieties grown in this region include Cabernet Sauvignon, Chardonnay, Riesling, Syrah, and Cabernet Gernischt. The experiment was conducted on 13-year-old Cabernet Gernischt vines grown on a vertical trellis system with a row spacing of 2.5 m and a plant spacing of 0.80 m in an east-west orientation. The vineyard was managed in accordance with established standard viticultural practices, such as pruning and shade control, and was irrigated using a drip irrigation system. Two types of fertilizers, urea (46% nitrogen) and ammonium phosphate dibasic (18% nitrogen and 46% phosphorus), were applied to the vineyard in mid-April each year at a rate of 0.75 t/ha. Also, a sheep-based organic fertilizer (organic matter > 45% and N + P2O5 + K2O > 5%) was applied to the vineyard in mid-October each year at a rate of 9 t/ha.
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 3 Forecasting Approach
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity** & **ARIMA(\(p\), \(d\), \(q\))** & **Ljung-Box Test** \\ \hline \multicolumn{2}{c}{Vehicle Miles Traveled **(VMT)**} & ARIMA(0, 2, 1) & \(p\)-value = 0.5683 \\ \hline \multicolumn{2}{c}{Population (\(P\))} & ARIMA(0, 2, 1) & \(p\)-value = 0.9822 \\ \hline \multicolumn{2}{c}{Value of Statistical Life (\(VSL\))} & ARIMA(0, 2, 0) & \(p\)-value = 0.8907 \\ \hline \multicolumn{2}{c}{Median Household Income (\(MHI\))} & ARIMA (0, 1, 0) & \(p\)-value = 0.6957 \\ \hline \multirow{2}{*}{Harvested area} & Soybean (\(A_{S}\)) & ARIMA(0, 0, 0) & \(p\)-value = 0.9131 \\ \cline{2-3}  & Corn (\(A_{C}\)) & ARIMA(0, 0, 0) & \(p\)-value = 0.7899 \\ \cline{2-3}  & Wheat (\(A_{W}\)) & ARIMA(0, 0, 0) & \(p\)-value = 0.6069 \\ \hline \multirow{2}{*}{Crop Yield} & Soybean (\(Y_{S}\)) & ARIMA(1, 1, 3) & \(p\)-value = 0.3430 \\ \cline{2-3}  & Corn (\(Y_{C}\)) & ARIMA(1, 1, 2) & \(p\)-value = 0.2767 \\ \cline{2-3}  & Wheat (\(Y_{W}\)) & ARIMA(3, 1, 1) & \(p\)-value = 0.7521 \\ \hline \multirow{2}{*}{Price} & Soybean (\(p_{S}\)) & ARIMA(0, 0, 0) & \(p\)-value = 0.8212 \\ \cline{2-3}  & Corn (\(p_{C}\)) & ARIMA(1, 0, 1) & \(p\)-value = 0.3311 \\ \cline{2-3}  & Wheat (\(p_{W}\)) & ARIMA(2, 1, 1) & \(p\)-value = 0.0925 \\ \hline \multicolumn{2}{c}{Livestock count (\(L\))} & ARIMA(0, 1, 1) & \(p\)-value = 0.3429 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of forecasted variables using the ARIMA model. Figure 2: Logical flowchart for the ARIMA model. **Alternative Hypothesis (\(Ha\)):** The residuals are not independently distributed (they exhibit serial correlation). Generally, a model is considered as the best model if the residuals are independently distributed. Thus, if the \(p\)-value is greater than 0.05, it means this model is good for forecasting our time series. If not, different lag or another model has to be tried. Finally, we forecasted the time series with the fitted model. While forecasting each variable, we used a 95 percent confidence interval to consider uncertainty in those variables.
## 3 Mathematical and Problem Formulations
### Power System Modeling with UPFCs and PSSs
#### 3.1.2 UPFC Modeling
Measurement systems that capture the \(d\) and \(q\) components of the AC positive-sequence voltage, the currents to be controlled, and the DC voltage (\(Vdc\)). 3. An outer regulation loop, which encompasses an AC voltage regulator and a DC voltage regulator. The AC voltage regulator's output is the reference current (\(Iq_{ref}\)) for the current regulator. \(Iq\) represents the current in quadrature with voltage and controls reactive power flow. The DC voltage regulator's output is the reference current (\(Id_{ref}\)) for the current regulator, where \(Id\) is the current in phase with voltage and regulates active power flow. Figure 5: Controller structure diagram for the shunt component of the UPFC. An inner current regulation loop, comprising a current regulator. This current regulator manages the magnitude and phase of the voltage generated by the PWM converter (V2d and V2q) based on the reference currents, i.e., \(Id_{ref}\) and \(Iq_{ref}\), which are produced by the DC voltage regulator and the AC voltage regulator, respectively (in voltage control mode). The current regulator is further supported by a feed-forward-type regulator that predicts the V2 voltage output (\(V_{2d}\) and \(V_{2q}\)) based on the \(V1\) measurements (\(V_{1d}\) and \(V_{1q}\)) and the transformer leakage reactance. The management of the series branch differs from that of the SSSC. In an SSSC, the series converter's two degrees of freedom are employed to oversee the DC voltage and the reactive power. Conversely, in the case of a UPFC, these two degrees of freedom are utilized to govern the active power and the reactive power. Figure 6 illustrates a simplified block diagram of the series converter. The series converter offers the flexibility to operate in either power flow control (automatic mode) or manual voltage injection mode. In power control mode, it compares the measured active power and reactive power with reference values to generate \(P\) and \(Q\) errors. These \(P\) and \(Q\) errors are then employed by two PI controllers to determine the \(V_{q}\) and \(Vd\) components of the voltage to be generated by the VSC. (Vq, which is aligned with \(V1\), regulates active power, while \(V_{d}\), which is in phase with \(V1\), manages reactive power). Conversely, in manual voltage injection mode, there is no need for regulators. Instead, the reference values for injected voltage (\(Vd_{ref}\) and \(Vq_{ref}\)) are used to create the converter voltage.
## 6 Conclusions  
In this paper, we first propose a new inter-community bridge node detection framework, BND, which transforms the bridge node detection task into a classification problem and uses deep learning techniques to detect bridge nodes in the network, thus overcoming the traditional method's dependence on the community division algorithm. At the same time, BND makes judgments on bridge nodes after synthesizing network topology information and node characteristics, so it has high accuracy. On this basis, we propose a graph neural network model BND-GCN and two variants, BND-GAT and BND-GraphSAGE, for bridge node detection. Our extensive experiments on five real social networks show that they outperform other deep learning models and some traditional machine learning models in bridge node detection. As far as we know, this is also the first application of graph neural network technology in the field of bridge node detection, and there is no similar application work in this field.  
In future work, we will continue to study how to mine community structure information in the graph embedding or node feature stage, improve the accuracy of inter-community bridge node detection, and further improve our framework.  
Conceptualization, P.J.; methodology, H.L.; software, H.L.; validation, Z.H.; formal analysis, H.L.; investigation, Y.L.; resources, P.J.; data curation, Z.H.; writing--original draft preparation, H.L.; writing--review and editing, H.L.; visualization, Z.H.; supervision, A.Z.; project administration, A.Z.; funding acquisition, P.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Sichuan Province under Grant 2021 YFG0156.  
Not appliable.  
Not applicable.  
The authors would like to thank the Key Research and Development Program of Sichuan Province for its funding and support in this research.  
The authors declare no conflict of interest.
## 3 Multi-Motor Power Distribution Control Strategy
### Fuzzy Regulator Design  
To ensure the distributed permanent magnet direct drive belt conveyor possesses adequate power for material transportation, it becomes imperative to assess the material carrying capacity. Consequently, a fuzzy controller is designed to ascertain the minimum number of motors required for operation. With the development of intelligent coal mines, the carrying capacity of transport materials can be obtained through the existing detection device. The input of the fuzzy controller is the fuzzy value of the material carrying capacity at time t. The fuzzy rules are defined as follows:  
\[f(x)=\left\{\begin{array}{l}b_{0},\;\max(X_{t_{0}-t_{\mathrm{s}}}^{t_{0}})=r _{0}=0\\ b_{1},0<\max(X_{t_{0}-t_{\mathrm{s}}}^{t_{0}})\leq r_{1}\\ \cdots\cdots\\ b_{\mathrm{i}},\;r_{\mathrm{i-1}}<\max(X_{t_{0}-t_{\mathrm{s}}}^{t_{0}})\leq r _{\mathrm{i}}\\ \cdots,\cdots\\ b_{\mathrm{n}},\;r_{\mathrm{n-1}}<\max(X_{t_{0}-t_{\mathrm{s}}}^{t_{0}})\leq r _{\mathrm{n}}\end{array}\right. \tag{6}\]  
where \(X_{t_{0}-t_{\mathrm{s}}}^{t_{0}}\) is the carrying capacity of a series of materials in the period from \(t_{0}-t_{\mathrm{s}}\) to \(t_{0}\); \(r_{\mathrm{i}}\;(i=1,2,3,\ldots,n)\) is the fuzzy boundary; and \(b_{\mathrm{i}}\;(i=1,2,3,\ldots,n)\) is the fuzzy input.  
In accordance with Equation (1), it is evident that there exists a linear relationship between the material carrying capacity and the operational resistance. Based on no-load operating conditions, the additional number of motors can be anti-ambiguous:  
\[n_{ad}=\lfloor b_{i}(n_{\max}-n_{\min})\rfloor \tag{7}\]  
where \(n_{\max}\) is the total number of distributed permanent magnet direct drive belt conveyor motors.  
The number of running motors of the distributed permanent magnet direct drive belt conveyor is the following:  
\[n=n_{\min}+n_{ad} \tag{8}\]  
To ensure that the driving system of the distributed permanent magnet direct drive belt conveyor can supply adequate power to the belt, the process of increasing the number of multi-motors and adjusting the power distribution should be accomplished prior to the rise in material carrying capacity. Conversely, the task of reducing the number of multi-motors and adjusting the power distribution should be undertaken following a decrease in material carrying capacity.
## 4 Discussion
### Potential Drivers of Coastline Change at Selected Hotspots
#### 4.3.1 Erosion Hotspot in the Mekong Delta  
Vietnam's longest erosional coastal segment lies in the lower Mekong Delta of Ca Mau province and is shown in Figure 13a. The average erosion rate at the hotspot is \(-25\) m/year, resulting in a total coastline shift of 1200 m from 1986 to 2021. The time series graph of the sample transect shows a clear continuous erosion trend with a few data gaps in the 1990s and in 2012. Constant retreat of the coastline in Ca Mau has been observed multiple times before [13; 17; 19; 20; 21; 80; 81]. Identified drivers for coastal erosion in Ca Mau include natural sediment redistribution by along-shore currents [13], land subsidence through ground water extraction [80], mangrove removal for aquaculture production [81], reduced river discharge through river damning [17], and sand mining [19; 20]. A comprehensive analysis of the various influencing factors was provided by [PERSON] et al. (2019) [13], who compared all of the above-mentioned drivers by modelling their relative impacts. The authors simulated the natural redistribution of the Mekong Delta and found that the east coast from Soc Trang to Ca Mau (which includes this hotspot) is naturally eroding due to predominant south-westwards currents close to the shore. The eroded sediment was observed to partly accumulate at the Ca Mau head (which can also be seen in Figures 7 and 9). However, several naturally accreting coastal segments within the Mekong Delta were found to be eroding. This could be caused by a combination of land subsidence and a reduction in river sediment supply, according to [PERSON] et al. [13]. In this respect, the observed continuous coastline retreat at this hotspot suggests a constant natural cause, which was potentially accelerated by human activity. However, since the potential drivers for coastline change are very diverse and overlap each other, it remains a challenge to prove causal relationships. This would require further modelling and the use of additional datasets [41].
###### Abstract  
(1) Background: Enhancing repeated sprint ability (RSA) is critical to soccer performance. Post-activation performance enhancement (PAPE) protocols, particularly those utilizing heavy resistance preloading, have shown promise in improving RSA. Yet, the optimal preloading intensity for amateur players is still unknown. This study investigated the effects of two PAPE protocols using different preloading intensities (85% and 90% of 1 RM back squat) on RSA. (2) Methods: A double-blind, randomized, two-period crossover design was used, and 18 amateur male players were included in this study. RSA was assessed using a 6 \(\times\) 35 m sprint protocol with 10 s rest intervals (RAST). (3) Results: While the main effect of time significantly (\(p=0.01\), \(\eta^{2}=0.71\)) influenced performance decline across all groups, there was no significant (\(p=0.65\), \(\eta^{2}=0.01\)) interaction between time and condition. Individual responses to PAPE varied, highlighting the importance of individual differences. Interestingly, the BS85% group exhibited a less pronounced decline in performance compared to the control group, while the BS90% group only showed significant differences in three sprint levels. (4) Conclusions: Our findings suggest that a lower preload of 85% of 1 RM may be as adequate as a higher preload of 90% of 1 RM in enhancing RSA in amateur soccer players. These results may indicate a potential fatigue-delaying effect of PAPE protocols and emphasize the importance of individualized approaches for optimizing PAPE strategies.  
warm-up; repeated sprint performance; human physical conditioning; soccer; post-activation potentiation; resistance training +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Conditions for LC-ESI/Ms  
A Vanquish ultra-HPLC system (Thermo Scientific Inc. (Waltham, MA, USA) with a Cortex T3 column (150 \(\times\) 2.1 mm, 16 um particle size, Waters Corp., Milford, MA, USA) kept at 45 \({}^{\circ}\)C was used for separation. The mobile phase consisted of water with 0.1% formic acid (eluent A) and acetonitrile with 0.1% formic acid (eluent B). The total run time was 55 min: the gradient started at 3% eluent B, ramping up to 15% over 15 min, then climbing to 100% over the next 35 min, and holding for an additional 5 min. After that, the column was re-equilibrated to 3% eluent B for 5 min. A constant flow rate of 0.25 mL was maintained. A heated electrospray ion source equipped with a high-resolution mass spectrometer (HRMS) was used for the analysis. The HRMS collected full-scan MS spectra (\(m/z\) 100-1500) with a resolution of 70,000 using a quadrupole system, while operating in positive and negative modes. For the positive ion mode, the spray voltage was adjusted to 3.5 kV, while for the negative ion mode, it was set to 3.0 kV. For the MS2 fragmentation, the 10 most intense precursor ions were chosen, and the spectra were acquired at a resolution of 17,500. Additional MS parameters included a capillary temperature of 320 \({}^{\circ}\)C, sheath gas at 50 astronomical units (AU), sweep gas at 1 AU, and auxiliary gas at 10 AU.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Discussion  
In addition to the remarks mentioned in the previous subsections, other essential aspects deserve our attention. They refer to (1) the real-time applications vs. non-real-time ones and to (2) the mobility states between initial and final positions. Concerning the first aspect, the monitoring tasks assigned by the Cloud Center are not critical ones, as usually, the sampling time in environment monitoring takes minutes or even hours to reduce the power consumption of the devices [81]. An execution time of 8.982 s is sufficient for these purposes, even though the implementation can be further enhanced to reach less than 4 s execution time. Concerning the mobility states of the UV between CC trigger-UV initial position sending-CC algorithm execution-UV final positions sending, this can be mitigated by forcing the UVs to keep their positions between the trigger and the CC position message sending time window. Without such mitigation measure, for a theoretical horizontal speed of 16 m/s, the new position of a drone may change with 320 m for a time window of 20 s. For a theoretical vertical speed of 6.28 m/s, the summed vertical offset (for consecutive up-down movements) can vary over 120 m.