esa-sceva/satcom-chunk-collection huggingface dataset:
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
To model renewable energy resource uncertainty and improve the accuracy of optimization results, the Monte Carlo method is extensively applied to study problems related to power system uncertainty. [PERSON] et al. [18] use the pseudo-sequential Monte Carlo simulation to model the uncertainty of PV outputs, and it also combines the intelligent state space reduction to evaluate the economic performance of a power system with PV installation. In addition, based on the state sampling non-sequential Monte Carlo simulation and the DC load flow-based load curtailment model, [PERSON] et al. simulated the random fault of the power system, and put forward a power system reliability evaluation method [19]. Also, by using the Monte Carlo method to simulate unbalanced voltage and then analyzing the effects of unbalanced voltage on PV hosting capacity, the hosting capacity of PV in different regions was shown in [20]. Moreover, the Monte Carlo method was used to simulate the uncertainty of wind power generation and users' electric vehicle charging behaviors in [21]. In this paper, a joint operation model is proposed to decrease the high uncertainty of a wind power system by centralizing electric vehicle charging stations. In [18; 19; 20; 21], the Monte Carlo method was successfully used to model the uncertainty of power systems, but this method has some limitations, such as for example, high complexity and low computation efficiency. In order to avoid the limitations of the Monte Carlo method, factors (excluding the uncertainty of renewable energy resource) that can affect energy storage system installation capacity are comprehensively considered when sizing hybrid energy storage systems. Then, by defining two indexes, wind speed volatility and solar irradiance volatility, the influence of renewable energy resource uncertainty on the proposed storage system is evaluated. This approach significantly reduces computation complexity. The main contributions of this article can be summarized as follows. (1) In the view of different time scales, this paper analyzes the renewable generation and load characteristics of a distributed power system, puts forward a high-precision storage capacity configuration method for a place with relatively stable meteorology, and optimizes the proposed model by using the genetic algorithm (GA). (2) By comprehensively considering the limitations of carbon emissions, costs, and the hybrid energy storage system installation capacity, this paper shows the relationship among the super-capacitor installation capacity, the battery installation capacity, and the average daily energy costs of the system in a 3D graph. Then, it generates a region of feasible hybrid energy storage system installation capacity in the 3D graph, and meanwhile reveals the influence of carbon emissions and daily energy cost on energy storage system installation capacity in a straightforward way. (3) The volatility of wind speed and solar irradiance are introduced at last to evaluate the anti-interference capability of the proposed hybrid energy storage system.
# Coherent Chaotic Communication Using Generalized Runge-Kutta Method
## 2 Materials and Methods
### Analysis of the Gokyildirim Discrete Model
#### 2.2.1 The \(\alpha h\) Bifurcation Diagram Analysis  
Let us plot the bifurcation diagram of the considered system using the alpha parameter (see the bottom plot in Figure 3). One can see that the system exhibits stable chaotic and periodic oscillations even beyond the ranges of \(\alpha\in(0,1]\). The 2D bifurcation diagram shows how the dynamics of the system (8) depend on the integration step \(h\) and the value of \(\alpha\). However, a simple plot of \(\alpha\times h\) will not clearly show all changes in the dynamics since the value \(\alpha\) is multiplied by the integration step and thus will violate the scale because the range of the \(\alpha\) values increases with the decrease in the integration step. Considering this, we used a technique similar to the method proposed in [18] for plotting two-dimensional \(\alpha\times h\) diagrams as follows: for each integration step value, the range of the midpoint value is calculated according to the formula:  
\[\alpha h^{2}=const. \tag{10}\]  
One can see that the stability of the obtained discrete map is preserved even outside the range of \(\alpha\in(0,1]\), allowing us to choose it arbitrarily. Figure 3 shows a 2D bifurcation  
Figure 2: Basin of attraction and phase portrait for system (9). [PERSON] and blue colors correspond to two attractors, while the white color corresponds to the unbound solution; (**a**) \(a=0.065\) and \(b=0.45\), \(z_{0}=0\); (**b**) \(a=0.0707\) and \(b=0.3955\), \(z_{0}=0\).  
diagram that illustrates the dynamics of the system at various integration steps h while varying the value of \(\alpha\). The 1D bifurcation diagrams for \(h=0.002\), \(h=0.01\), and \(h=0.05\) are given as well.  
As one can see from Figure 3, varying the alpha parameter affects the dynamics of the oscillator. In the context of chaotic communication systems, it is important to choose alpha values that preserve the chaotic behavior of the oscillator on the transceiver side. The following subsections describe the bifurcation and Lyapunov spectrum analysis for investigation of the ranges of the \(\alpha\) values that preserve the chaotic behavior of the system (8).
## 3 Method
### Heterogeneous Encoder
#### 3.1.1 MaxViT Block  
Figure 4 illustrates the internal structure of the MaxViT block. The MaxViT block comprises three fundamental components: MBConv, block attention, and grid attention. Initially, the feature map is processed by MBConv, which incorporates a squeeze-excitation (SE) module. Additionally, MBConv includes a depthwise convolution, which can be regarded as a form of conditional position encoding, thereby eliminating the necessity for explicit position encoding operations in \(MaxViT\). Following MBConv, the feature map undergoes block attention to extract local information. Finally, the feature map is subjected to grid attention to extract global information. The formula of the MaxViT block is provided in Equation (3).  
\[\begin{cases}x_{MB-out}=x\oplus SE(Conv_{3\times 3}(Conv_{1\times 1}(x)))\\ x_{B-out}=FFN^{*}(x_{MB-out}\oplus Block-SA(x_{MB-out}))\\ x_{G-out}=FFN^{*}(x_{B-out}\oplus Grid-SA(x_{B-out}))\end{cases} \tag{3}\]  
Let \(x\) represent the feature map, and \(Conv_{1\times 1}\) denote the convolution operation with a kernel size of \(1\times 1\). \(Block-SA\) and \(Grid-SA\) represent block attention and grid attention, respectively. \(FFN^{*}\) denotes the feedforward network with residual connection. The feature maps \(x_{MB-out}\), \(x_{B-out}\), and \(x_{G-out}\) represent the feature map \(x\) after processing via MBConv, block attention, and grid attention, respectively. The symbol \(\oplus\) represents matrix addition.  
Figure 3: Heterogeneous encoder structure diagram.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
###### Abstract  
The fight against deforestation and forest degradation is now a major challenge for the preservation of global forest ecosystems. The remote sensing forest monitoring methods that are currently deployed are not always adapted to the Ivorian context because of the high cloud cover, diversity of shaded crops, and land clearing techniques. This study proposes a drone-based approach to assess intra-annual tree losses in the Bossematic classified forest. The method used is based on a detection analysis of tree losses in forest areas from a time series of aerial images acquired by drones from November 2018 to April 2019 on five sites in the studied forest. Based on photogrammetric models and photointerpretation, tree heights and tree crown sizes were estimated. Then, tree losses were detected based on the variation of tree heights during the study period. An analysis of the distribution of tree heights in Bossematic classified forest reveals that the maximum tree height was 65.06 m in November 2018 and 64.07 m in April 2019 with an average tree height of 34.29-37.00 m in November 2018 and 34.63-36.88 m in April 2019. The average tree crown area, meanwhile, was estimated to be 152 m\({}^{2}\). With an estimation accuracy of about 97%, these tree structural data indicate a minimum loss of 107 trees corresponding to a clearing area of 2 ha across all the surveyed sites from November 2018 to April 2019. This forest monitoring approach shows a considerable local loss of biodiversity and should be involved in the implementation of preservation, rehabilitation, and deployment strategies in an operational deforestation monitoring system in Cote d'Ivoire.  
drones; REDD+; classified forest; deforestation; Cote d'Ivoire
## 4 Discussion
### Comparative Analysis of the Proposed Piezoresistive Chest Band with Similar Devices
Specifically, the optimization of the integrated piezoresistive sensor, namely, its geometry and position, and the shape of the electrodes, can improve the respiratory movement detection and overall system performance. In addition, firmware optimization, mainly related to the involved parameters, can reduce the presence of artefacts in the respiratory signal, and thus improve the detection of breathing activity. The developed chest band represents an optimal trade-off in terms of performance, complexity, and completeness. In actual test conditions, the proposed device obtained performance (mean error) comparable with that of similar systems that, however, use more performant and complex hardware solutions (i.e., a larger number of sensors, higher-resolution ADC, etc.) [29], or rely on an external data processing unit (i.e., PC) [23; 65]. That is, the obtained results indicate that our system should achieve higher performance than those of competitor systems using comparable hardware. Indeed, a preliminary study was carried out with the developed sensing band employing an acquisition section based on an MSP430 FG479 microcontroller equipped with a 16-bit \(\Sigma\)-\(\Delta\) ADC, which achieved a mean error of 1.25% when the user was seated. From these results, the potential of the presented solution can be inferred; that is, performance can be obtained that is comparable to, or an improvement of, that of the other solutions already presented in the literature [66]. In addition, unlike other systems, our chest band has the advantage of carrying out real-time data processing directly onboard, and provides the final parameters to the host device only for viewing.
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 5 Comparative Analysis
### Energy Consumption in ES-MAC  
In ES-MAC, the energy consumption in each ordinary sensor node is denoted by \(\text{EON\_ESMAC}\) and is computed as shown in Equation (4). The energy spent by the CH is denoted by \(\text{ECH\_ESMAC}\) and is calculated as shown in Equation (5).  
\[\text{EON\_ESMAC}=\text{ERE\_ADV}+\text{ERE\_JOIN}+\text{ERE\_SCH}+\text{ERE\_DATA} \tag{4}\]  
\[\text{ECH\_ESMAC}=\text{EBR\_ADV}+\text{ERE\_JOIN}+\text{EBR\_SCH}+\text{ERE\_DATA }+\text{ERE\_DATA} \tag{5}\]  
The total energy spent in a cluster in an ES-MAC frame is denoted by the symbol \(\text{EESMAC}\), where n is the number of member nodes in the cluster.  
\[\text{EESMAC}=\text{n}^{*}\,\text{EON\_ESMAC}+\text{ECH\_ESMAC} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **LEACH** & **ES-MAC** & **EE-MAC** & **EERP** \\ \hline Threshold Energy & \(\times\) & \(\times\) & \(\checkmark\) & \(\checkmark\) \\ \hline Proximity Threshold & \(\times\) & \(\times\) & \(\checkmark\) & \(\times\) \\ \hline Maximum Cluster Size & \(\times\) & \(\times\) & \(\checkmark\) & \(\times\) \\ \hline Cluster Selection Criteria & Strength of ADV & Strength of ADV & \
\begin{tabular}{c} Strength of ADV \\ \& MCS \\ \end{tabular} &
\begin{tabular}{c} Based on location \\ (join Upstream CH when \\ no node can become CH \\ in own grid) \\ \end{tabular} \\ \hline Distance to Event & \(\times\) & \(\times\) & \(\times\) & \(\checkmark\) \\ \hline Verification Period & \(\times\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \end{tabular} \(\checkmark\)—Considered, \(\times\)—Not Considered.  
\end{table}
Table 3: Comparisons of Protocols.  
In LEACH, the CHs lose a significant amount of energy in idle listening. In ES-MAC, the Verification Period is used to reduce idle listening in CHs. Hence, ES-MAC reduces energy consumption in sensor nodes compared with LEACH.
## 2 Optimization Methodology
### Contour Tracking Control
#### 2.1.3. Contour  
For the optimization steps to be more efficient, the controller is tuned to two kinds of contours, linear (straight-line) contour and nonlinear (circular) contour for the tracking of manipulator robot. The linear contour is faster than the circular contour with a coverage of bigger space. The characteristics of the two kinds of contour are presented in Table 1.
## Materials and Methods
### Model Development and Validation  
The five explanatory variables and SIF constitute a dataset with a very large number of data points (\(\sim\)3.7 million). We used the samples in 2015 (\(\sim\)1.3 million) and 2016 (\(\sim\)1.3 million) as the training data, and the samples in 2017 (\(\sim\)1.1 million) as the testing data. The size of the training sample may influence the prediction accuracy, especially for machine learning methods [59]. To test how the training sample size affected the SIF prediction, we used the stratified random sampling technique to generate a set of training samples with different sizes from 10,000 to 2.6 million. Each training sample dataset was individually used to develop a predictive SIF model, and the testing data was used to validate each model. Each model was evaluated by the root mean squared error (RMSE) and coefficient of determination (R\({}^{2}\)) between the predicted and observed SIF. We then divided the observed SIF in the testing data into 23 contiguous bins of 0.05 W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) each from 0 to 1.1 W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\), and calculated the averaged RMSE in each SIF bin. Finally, we calculated the percentages of SIF observations in each 0.05 SIF bin relative to all the observed SIF across all bins, and multiplied them by the averaged RMSE in each bin. The resulting indicator (i.e., accumulated error) accounts for the RMSE in each bin and the weight of each bin across all the bins, and can measure the accumulated performance of different models on each SIF bin and help us determine the best model.  
Another sensitivity analysis was conducted to test whether the incorporation of land cover type could improve the SIF estimation. The models mentioned above were developed and evaluated by dropping or adding land cover type into the models, respectively. Finally, we determined the optimal model and then validated this selected model.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 3 Effective Factors in the CO\({}_{2}\) Absorption by Nanofluid
### Effect of Nanoparticle Type  
[PERSON] et al., observed gas-liquid absorption enhancement when using nanoparticles in the form of nanofluids in bubbling absorption systems for CO\({}_{2}\) capture, which was different for each nanoparticle type. When equilibrium is reached in a gas-liquid absorption system, the gas concentration slightly increases in the liquid phase [85]. In a research study, [PERSON] et al., used a bubbling ammonia system for the investigation of nanoparticles' effects on CO\({}_{2}\) absorption. The relationship between the type of nanoparticle and the efficiency of CO\({}_{2}\) capture was determined. The sequence of CO\({}_{2}\) capture efficiency was TiO\({}_{2}\)\(>\) CuO \(>\) SiO\({}_{2}\)[86]. The CO\({}_{2}\) removal efficiency of a stirred reactor containing CNT and Al\({}_{2}\)O\({}_{3}\) nanoparticles was studied by [PERSON] et al. The results of their experiments indicated a considerably enhanced CO\({}_{2}\) capture when using carbon nanotubes in the absorption solvent [33]. In a study, [PERSON] et al. investigated the effect of TiO\({}_{2}\), SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\) nanoparticle addition to the absorption solvent in a new annular contactor (AC) that utilized a tray absorber. This study suggested up to 5%, 6% and 10% enhancement in the absorption rate when using TiO\({}_{2}\), SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\) nanoparticles, respectively [87]. [PERSON] et al., used a stirred reactor to examine the influence of TiO\({}_{2}\) nanoparticle addition into propylene carbonate, on the capture rate of the system; the effects of particle size and the optimum concentration of nanoparticles were also analyzed in their experiments [73]. [PERSON] et al., used a gas-liquid hollow fiber membrane contactor to remove CO\({}_{2}\) with a nanofluid containing silica nanoparticles and carbon nanotubes. Their findings showed that CNT nanofluid has better performance in CO\({}_{2}\) removal with up to 40% efficiency [31].
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 4 Conclusions  
(1) The Qaidam Basin is located in an arid and semi-arid region with a dry climate and fragile natural ecological environment. The precipitation process in the Qaidam Basin is significantly different spatially and temporally. Due to the sparse gauge stations and maldistribution, the rainwater resources  
Figure 9: The variation trend and significance of precipitation in the Qaidam Basin. (**a**) Corrected 3B42 RT change trend; (**b**) corrected 3B42 RT change significance; (**c**) corrected 3B43 change trend; (**d**) corrected 3B43 change significance.  
are hard to assess. A fusion of remote sensing precipitation of TRMM products and gauged precipitation is helpful.  
(2) The average mean annual precipitation is only 104.34 mm by ground gauges interpolation, and 148.45 mm and 146.26 mm by original 3B42 RT and 3B43. However, the precision evaluation of TRMM precipitation shows it was overestimated in the LGPA and underestimated in the HGPA. The original TRMM products need a correction.  
(3) The correction result shows the average mean annual precipitation is 186.01 mm by 3B42 RT and 174.74 mm by 3B43. The average bias of 3B42 RT and 3B43 at gauge stations are significantly reduced to \(-0.08\%\) and \(0.11\%\) after being corrected, and the average RMSD is significantly reduced to 37.51 mm and 34.22 mm, respectively. All of those indicated that the precipitation products fusion model based on ANN could effectively work on TRMM products.  
(4) The final result shows there are many differences in areal precipitation based on sparse gauge precipitation data, original TRMM data and fusion remote sensing data. The rainwater resources in the study basin have been underestimated in the past year, and both were derived from ground gauge stations and from original TRMM products.  
In the future, research needs to test the ANN effectivity on mining the normalized difference vegetation index and rainwater resources in this area.  
Conceptualization, Z.W. and G.X.; methodology, Z.W. and T.X.; formal analysis, G.X.; writing--original draft preparation, G.X.; writing--review and editing, Z.W. All authors read and approved the final manuscript. This research was funded by the National Key Research and Development Program (No. 2016 YFC0402900), Key R&D and Transformation Projects in Qinghai Province (No. 2017-SF-116), and the National Natural Science Foundation of China (NSFC, Grant No. 41671020). The authors would like to thank all the colleagues who generously provided their dataset. The authors greatly thank the editors and reviewers for providing thorough and constructive comments to improve the manuscript. The authors declare no conflicts of interest.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 4 Algorithm Design  
[PERSON] et al. [13] demonstrated that the VNF migration problem is NP-hard, since the significant scale of the network, the diversity of the network service flow, and the variability of the network environment make the VNF placement decision space high-dimensional and difficult.  
Therefore, this section proposes a Resource Predictive Load Balancing SFC Migration Algorithm (RP-LBM) to address the aforementioned issue. The overall architecture of the algorithm is shown in Figure 2. The resource monitoring module obtains the dynamic resource utilization of the physical network. The Long Short-Term Memory (LSTM) model uses historical resource demand data to predict the NF resource demand at a future time. The resource prediction result is input into the migration decision module. For the SFC deployed on the node that is expected to be overloaded, the Proximal Policy Optimization algorithm is used to obtain the migration strategy. This strategy includes determining the migration path and VNF embedding nodes, ensuring that the service is not interrupted, and minimizing resource variance.
###### Abstract  
This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs.  
dynamic viewpoint selection; next-best-view planning; economic analysis; sweet peppers; maturity classification; machine vision; harvesting robot
# Principle and Applications of the Coupling of Surface Plasmons and Excitons
## 3 Recent Progress in Coupling between Surface Plasmons and Excitons
### Strong Coupling between Plasmons and Excitons on Conductive Surfaces
[66] proposed a novel Ag nanoparticles (NPs) and Ag film (named the G (graphene)-NFG system) gap by introducing an ultrathin graphene monolayer as a well-defined sub-nanospacer between Ag NPs and a Ag film. This new G-NFG system offered tremendous near-field enhancement with one of the highest enhancement ratios (1700) reported to date and is illustrated in Figure 7.  
[PERSON] et al. [67] used a graphene-gold metal-surface composite structure to improve the sensitivity of a DNA detection structure effectively. (In systems based on coupling of excitons and SPPs, relevant studies were also performed on surface-enhanced Raman scattering (SERS), DNA detection and other aspects.) [PERSON] et al. [68] successfully achieved the integration of plasmonic Al NPs with wrinkle-like graphene sheets and obtained experimental Al nanoparticle-enhanced solar cells. As shown in Figure 8, the structure effectively enhanced the device absorption of the ultraviolet band while increasing the limiting effect on light, and thus ultimately improved the device efficiency. Strong coupling of the conductive surface plasmons and excitons has been achieved gradually by varying the distance between the thin layer of molecules and the metal surface.