esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Invasive Watermilfoil on Primary Production in Littoral Zones of North-Temperate Lakes
## 3 Results
### Standing Crop  
Standing crops of primary producers were also not different between +IWM and -IWM plots. Total macrophyte standing crops in study plots ranged from 16.6 to 307.7 g m\({}^{-2}\) (Figure 3). Although we hypothesized that total macrophyte biomass would be higher in +IWM plots, it was not significantly different across all plot pairs (Figure 3, \(t\) = 0.43, df = 5, \(p\) = 0.34). Additionally, epiphyte chlorophyll a (\(t\) = -0.40, df = 5, \(p\) = 0.35) and AFDM (\(t\) = -0.53, df = 5, \(p\) = 0.31) were not significantly higher in +IWM vs. -IWM plots (Figure 5). Phytoplankton Chla and AFDM were not significantly different between paired +IWM and -IWM plots (Chla, \(t\) = -0.01, df = 5, \(p\) = 0.99; AFDM, \(t\) = -1.10, df = 5, \(p\) = 0.32). Benthic periphyton Chla and AFDM were not significantly different between paired +IWM and -IWM plots (Chla, \(t\) = 0.36, df = 5, \(p\) = 0.73; AFDM, \(t\) = 0.33, df = 5, \(p\) = 0.76) (Figure 5). Benthic periphyto standing crops were generally one order of magnitude higher than phytoplankton and two orders of magnitude higher than epiphytes, as quantified by both Chla and AFDM (Figure 5). However, it should be noted that the AFDM of benthic periphyto included sediment organic matter collected in the core, and not strictly benthic periphyto.  
Stepwise multiple linear regression identified significant models for Chla and AFDM of benthic periphyton and phytoplankton. Benthic periphyto Chla was positively related to water temperature and negatively related to macrophyte standing crop with R\({}^{2}\)adj = 0.49, but benthic periphyto AFDM was positively related to macrophyte standing crop and negatively related to light extinction coefficients with R\({}^{2}\)adj = 0.47 (Table 4). Phytoplankton Chla was positively related to TD  
Figure 5: Standing crops of chlorophyll a and ash free dry mass (AFDM) for (**a,d**) epiphyte, (**b,e**) phytoplankton, and (**c,f**) benthic periphyto. Means \(\pm\) 1 SE are represented by symbols with error bars. Shaded symbols represent +IWM plots and unshaded symbols represent -IWM plots for each waterbody. Lines connecting symbols illustrate differences in paired plots. Symbols are offset horizontally to aid in interpretation of error bars.  
crops with R\({}^{2}_{\text{adj}}\) = 0.65, while phytoplankton AFDM was positively related to water temperature with R\({}^{2}_{\text{adj}}\) = 0.30 (Table 4). No significant models were produced for epiphyte Chla and AFDM (Table 4).
## 4 Results
### Review Papers and Application Fields
#### 4.6.6 MCDA and General Environmental Sciences  
[PERSON] et al. [125] conducted a review of the use of MCDA principles and tools in environmental decision making. They found that integrating MCDA with existing approaches, such as risk and cost-benefit analysis, can lead to more effective, efficient, and reliable decision making. However, choosing between MCDA methods can be a difficult task, and a case-by-case process should be followed. [PERSON] et al. [121] found that the use of MCDA has increased due to the rising complexity of environmental decision making and the need to consider multiple factors in relevant consultations. This is because MCDA methods are suitable for structuring different types of data and preferences related to decision-making processes. Moreover, [PERSON] et al. [122] have observed that many studies have claimed that the application of MCDA methods results in substantial improvements in the decision-making process and public backing for proposed restoration or mitigation policies. [PERSON] et al. [124] conducted a literature review on the application of PROMETHEE across various domains. However, our attention is focused solely on their review papers related to agriculture, energy management, environmental management, hydrology, and water management, where PROMETHEE demonstrates particularly extensive and efficacious implementation. [PERSON] et al. [123] conducted a systematic review of DEA models in the field of energy and environmental economics. They found that various DEA models were used across different disciplines, with a predominant focus on efficiency analysis.
## Data Classification
### Brief Introductions to Selected Classifiers  
#### 2.2.1 \(K\)-Nearest Neighbors  
KNN is one of the most common and straightforward methods in the machine learning toolbox [49]. The simplicity of the KNN concept has made it a favorite tool for classification in different applications [50]. As an instance based learning method in pattern recognition, the KNN classifier can sort each element of a study case on account of its nearest training examples in the feature space.  
For instance, to classify a sample \(S_{i}\), the algorithm first searches for its \(K\) nearest neighbors in the feature space depending on the feature vectors and defined distance. The algorithm then executes votes of these neighbors according to their labels. The object sample will be classified in a group with the largest number of same label neighbors. The accuracy of classification usually increases with heightened participation in votes. In general, the amount of training set data and votes mainly affect the accuracy of the KNN [51; 52].  
Figure 2 shows a simple demonstration of the KNN methodology. When the parameter \(K\) is equal to 3, the vote result is that the class of triangles with dots in it is in the majority. However, when the parameter \(K\) is equal to 11, the solid triangles are the majority, although the object in question (the circle in the center) is closer to the squares.  
Figure 1: Flow chart illustration of classification modeling.  
This approach relies on statistics and is profoundly affected by the value of \(K\), and thus selecting the best value for this parameter is a major problem for KNN [53]. Depending on the classification problem posed, it requires several experiments to investigate different values for the parameter \(K\) and find a value that gives proper results. Trial and error has been the dominated, but tedious, method for determining an acceptable value of \(K\).
## 1 Introduction
The streamlines, temperature, microrotation, and vorticity contours are presented for characteristic ranges of solid volume fractions (\(0\leq\varphi\leq 0.03\)), Hartmann numbers (\(0\leq Ha\leq 1000\)), Rayleigh numbers (\(10^{6}\leq Ra\leq 5\times 10^{8}\)), and porosity of the medium (\(0.5\leq\varepsilon\leq 0.7\)). In the proposed model, the Darcy-Brinkman momentum equations with buoyancy and advective inertia terms are used. The microrotation vector is affected by the presence of the particles and the pore walls. For this reason, a single-fluid equation in a porous setting is implemented, incorporating the effective microrotational properties of the nanofluid, using volume-averaging in representative elementary volumes (REVs), avoiding the necessity of considering the effect of the detailed pore structure on the form of the equation itself. Similar manipulation has been applied for the presence of the magnetic field. In this way, a magnetic field term is not required in the transport equation for the microrotation vector. The rest of the paper is as follows. In Section 2, the governing equations of the proposed micropolar nanofluid model are presented, while the validation of the numerical method used for the solution of the flow equations and of the proposed micropolar nanofluid model, as well, are depicted in Section 3. In Section 4, the heat-transfer performance of a micropolar nanofluid enclosed in a rectangular cavity is studied. For all simulations, pure water is considered as the base fluid, with \(Pr=6.2\). Finally, in Section 5, the conclusions complete the paper.
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### Refuse-Operation Probability Model for Consistent Delays  
It has been proved that there is a consistent delay threshold \(t_{\max}\)[19; 26; 27]. If the consistent delay \(t_{d}\) is larger than \(t_{\max}\), the relay protection would be blocked. In this case, a refuse-operation would occur if there is a fault happing within the protection area. This section will analyze the refuse-operation probability considering the uncertainty of communication delays.  
According to [24], the practically observed consistent delay shows a normal distribution. Considering the uncertainty of environment impacts, the measurement errors and communication condition variations, the measured consistent delay \(t\) follows the normal distribution \(t\sim N(t_{d},\sigma_{1}{}^{2})\). \(t_{d}\) is the expected value of the consistent delay \(t\), and \(2\sigma^{2}\) is the variance. The probability density function of the consistent delay \(t\) is expressed as:  
Figure 5: False trip probability follows the first form of braking ratio criterion and start criterion.  
\[f(t)=\frac{1}{\sqrt{2\pi\sigma_{{}_{1}}}}e^{\frac{(t-t_{d})^{2}}{2\sigma_{{}_{1}} ^{2}}} \tag{15}\]  
Figure 6 shows the probability density of the consistent delay. The orange area represents the probability of consistent delay exceeding the threshold \(t_{\max}\) when the expected value of the consistent delay is \(t_{\text{d1}}\).  
Refuse-operation would occur if the measured consistent delay \(t\) is larger than \(t_{\max}\) and a fault happens within the protection area. Therefore, the refuse-operation probability can be expressed as:  
\[P_{rd}(t_{d})=1-\varphi(\frac{t_{\max}-t_{d}}{\sigma_{{}_{1}}}) \tag{16}\]  
where \(\varphi(x)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{x}e^{\frac{t^{2}}{2}}dt\) is the standard normal distribution function.  
Figure 7 gives the refuse-operation probability of relay protection caused by consistent delays.  
Figure 6: The probability density function of the consistent delay.  
Figure 7: Refuse-operation probability for consistent delay.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Hybrid
#### 5.4.2 Enhancing Routing Performance Using the Node Density of UAVs  
[PERSON] et al. [103] propose a hybrid routing scheme that enables nodes in a swarm of UAVs (or multi-UAV swarm) to form clusters and to establish a route in which nodes are geographically nearer to each other in order to increase link expiration time. The main objective is to enhance route stability (O.1) in FANETs. The routing scheme addresses the challenge of high dynamicity (X.1) of network topology. The routing scheme is designed for source routing (C.1.2).  
During the neighbor and route discoveries stage, each node periodically broadcasts hello message to form a neighbor set comprised of two-hop neighboring nodes. The hello message contains the three-dimensional geographical location of nodes. During the route selection stage, in order to increase network stability and hence the route lifetime, the underlying network is clustered based on (a) residual energy (M.4) and mobility metrics (M.1), whereby nodes with higher residual energy and moving close to the average speed of the nodes in the neighbor set are selected as CHs, and (b) link expiration time (M.2) and geographical location (M.3), whereby nodes geographically close to the CH joins its cluster to reduce the latency of data dissemination and to improve network connectivity in order to increase route stability in both inter- or intra-cluster communication. During the route maintenance stage, link disconnection occurs due to the dynamicity of network topology, and so, the route discovery mechanism is initiated to select a new CH or to join another CH as well as to reestablish routes. The CH collects data from CMs and then aggregates and forwards the data. CHs are responsible for sending the data between the clusters as well. The route between CHs are established by considering the mobility metrics (M.1) (i.e., average distance), whereby nearest located CH is selected as an intermediate node to route the data. During the route maintenance stage, which is required when there are lower number of CMs in a cluster than a predefined threshold, the node initiates the route discovery mechanism (or the first stage) to re-cluster the network and to reestablish routes.  
The routing scheme is compared with a state-of-the-art scheme, which is a cluster-based routing scheme. Realistic mobility model (A.6) is used in simulation. The routing scheme has been shown to increase QoS (P.1) as well as to reduce energy consumption (P.2) and the number of clusters in the network (P.3).
# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 3 Results  
### Visual Analysis  
Compared with the original MS image, the pansharpened images exhibit an obvious improvement in spatial quality shown in Figure 2, and Figure 10 to Figure 11 from Appendix A. The original MS images were shown in Figure 2a, Figure 11a, Figure 11a, Figure 11a, and Figure 11a, and the original PAN images were Figure 2b, Figure 11b, Figure 11b, Figure 11b, and Figure 11b. Slight variations are observed between the pansharpened images (Figure 2c to Figure 2f, Figure 11c to Figure 11f, Figure 11c to Figure 11f, Figure 11c to Figure 11f, Figure 11c to Figure 11f), but in general, all the approaches satisfy the criteria for improving the spatial resolution of the original MS image. A visual evaluation of the spectral quality was performed using the false-color image. When the spectral information is preserved perfectly, the land covers in the pansharpened images have a similar tone to those in the original MS images. Comparing the images resulting from the different pansharpening  
Figure 3: Flowchart of this study.  
methods revealed that the pansharpened images had color distortions, as evidenced by the tone changes of the land covers in these images. In the cases of the forest region (Figure A1), and shrub and grassland region (Figure A2), it was difficult to visually detect color differences among the pansharpened images (Figure A1c to Figure A1f, and Figure A2c to Figure A2f). Figures 2, A3 and A4 showed that, visually, the best color-preserving results were obtained with the mIHS pansharpening method, particularly for trees in the urban region (Figure 2f), winter wheat (Figure A3f), bare salt-affected soil and salt water (Figure A4f). The PCA pansharpening method exhibited the worst color-preserving results (Figure 2e, Figure A3e, and Figure A4e).
## 3 Materials
### Integrated Multi-Satellite Retrievals for GPM (IMERG)  
The Global Precipitation Measurement (GPM) mission is the most recent joint space venture between NASA (National Aeronautics and Space Administration) and JAXA (Japan Aerospace Exploration Agency) with contributions from several other countries and organizations (e.g., France's CNES, the Indian Space Research Organization (ISRO), the USA's NOAA, EUMETSAT, and others). The GPM expands upon the TRMM mission with higher spatial and temporal coverage and higher accuracy. It provides the next generation of global rain and snow observations. The scientific community responded immediately to the availability of the new source of spatiotemporal distributions of rainfall data [58].  
The GPM core observatory satellite was launched on 27 February 2014, carrying an advanced set of instruments onboard. It houses a Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multi-channel GPM Microwave Imager (GMI) capable of sensing light rain and snow fall [7; 54; 59; 60]. The GPM mission is characterized by its distinct orbit, inclined 65\({}^{\circ}\), allowing continuous sampling over all hours of the day [59]. The core observatory is complemented by a constellation of other spacecrafts. The core observatory provides a new calibration standard for the rest of the satellite constellation. The data from all satellites comprising GPM form the basis for the products of this mission.  
The Integrated Multi-Satellite Retrievals for GPM (IMERG) is the GPM's Level 3 multi-satellite precipitation algorithm. It combines intermittent precipitation estimates from all constellation microwave sensors, IR-based observations from geosynchronous satellites, and monthly gauge precipitation data [47; 50]. Three different daily IMERG products exist: IMERG Day 1 Early Run (near real-time with a latency of 6 h), IMERG Day 1 Late Run (reprocessed near real-time with a latency of 18 h), and IMERG Day 1 Final Run (gauged-adjusted with a latency of four months) [57]. The IMERG Final Run product provides more accurate precipitation information than the near-real-time products across GPCC-gauged (Global Precipitation Climatology Centre) regions [50] (see Table 1).  
Fifty daily IMERG scenes collected at half-hour intervals were retrieved from the official NASA Mirador webpage to cover precipitation events from 2015 to 2018 (in a netcdf format). The data were opened and clipped in ArcGIS10.5 software. The mean of the half-hourly scenes was calculated every three hours. This step facilitated the statistical comparison between the half-hourly GPM(IMERG) data and the three-hourly TRMM(3B42V7) data with eight scenes for each type. The value of each pixel in the previously mentioned scenes was calculated and stored in a spreadsheet. Next, the pixel values that coincided with rain gauges were collected in a separate spreadsheet to further calculate statistical metrics.
# Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization
## 1 Introduction  
A swarm intelligence optimization algorithm originates from the simulation of various types of biological behavior in nature and has the characteristics of simple operation, good optimization performance and strong robustness. Inspired by this idea, many bio-inspired swarm intelligent optimization algorithms are proposed, such as, ant colony optimization (ACO) [1], differential evolution (DE) [2], particle swarm optimization (PSO) [3], firefly algorithm (FA) [4], glowworm swarm optimization (GSO) [5], monkey search (MS) [6], harmony search (HS) [7], cuckoo search (CS) [8, 9], bat algorithm (BA) [10], krill herd algorithm (KH) [11, 12, 13], Swarm intelligence optimization algorithm can solve problems which traditional methods cannot handle effectively and have shown excellent performance in many respects, and its application scope has been greatly expanded.  
The Social Spider Optimization algorithm (SSO), proposed by [PERSON] in 2013 [14], is a novel metaheuristic optimization algorithm that simulates social-spider behavior. Although SSO has obtained good performance on many optimization problems, it still falls easily into a local optimal solution. In order to enhance the performance of SSO on optimization problems, this paper presents a novel SSO algorithm called EOSSO by using OBL and elite selection mechanism. Opposition-based Learning (OBL) is a new concept in computational intelligence, many algorithms have used the OBL mechanism [15, 16], and it has been proven to be an effective strategy to improve performance of various optimization algorithms. The main idea behind OBL is to transform solutions in the current search space to a new search space. By simultaneously considering the solutions in the current search space and the transformed search space, OBL can provide a higher chance of finding solutions which are closer to the global optimum. However, OBL could not be suitable for all kinds of optimization problems. For instance, the transformed candidate may jump away from the global optimum when solving multimodal problems. To avoid this case, a new elite selection mechanismbased on the population is used after the transformation. The proposed elite opposition-based Social Spider Optimization algorithm is validated by 23 benchmark functions. The results show that the proposed algorithm is able to obtained accurate solution, and it also has a fast convergence speed and a high degree of stability.  
The rest of the paper is organized as follows. Section 2 introduces the original Social Spider Optimization algorithm (SSO). Section 3 proposes a new elite opposition-based Social Spider Optimization algorithm (EOSSO). A series of comparison experiments on various benchmarks in Section 4. The results analysis is described in Section 5. Finally, conclusion and future works can be found and discussed in Section 6.
## 3 Results
### Mesh Dependency  
Mesh convergence was used to evaluate the invariance of numerical simulation results with respect to the mesh. The calculations were performed in parallel on a desktop computer equipped with a 3.40 GHz quad-core processor and 16.0 GB RAM. Figure 4 shows the variation in time-averaged magnitude velocity at different mesh refinement levels and that obtained from Richardson extrapolation (=0.066). The number of fine meshes for 670,000 cells was selected for evaluation. The uncertainty was \(\pm\)1.92% for the whole domain, as shown in Figure 4. The mesh convergence results are listed in Table 1. The grid convergence index (GCI) was subject to a mesh refinement ratio r\({}_{i}\) equal to \(dv_{i}/dv_{i+1}\) because the volume across simulations was similar. If the index ratio GCI\({}_{3,2}\)/(r\({}^{n}\)GCI\({}_{2,1}\)) converges close to one, the CFD results are within the asymptotic convergence range. We obtained an approximate ratio of 1.032, indicating mesh convergence; thus, a finer mesh was not required.
# Wide-Area Protection System for Radial Smart Distribution Networks
###### Abstract  
The integration of Distributed Energy Resources (DERs) in power distribution networks poses challenges for protection systems due to dynamic bidirectional fault currents. This paper presents a novel wide-area protection scheme for modern Doubly Feed Induction Generator (DFIG)-integrated distribution networks that simplifies fault location and relay coordination. The system employs Artificial Neural Networks (ANNs) for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location. The simulation results demonstrate the effectiveness of the proposed approach in fault detection and isolation.  
wide-area protection; radial smart distribution networks; ANN; fault location; DFIG +
Footnote †: journal: Applied sciences  
1 Center of Excellence for Power and System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran; [EMAIL_ADDRESS]  
2
## 5 Conclusions  
Based on the PSO and mini-batch gradient descent algorithms, an improved MLP model, namely, the PSO-MLP, was developed to carry out LSP for Shicheng County, China. The FR values of 14 predisposing factors were used as the input variables, while 2709 recorded landslide grid cells and 2709 randomly selected non-landslide grid cells were combined as the output variables of the prediction models. The MLP-only, BPNN, and IV models were also applied to perform LSP for comparison. The results showed that the landslide susceptibility maps produced by all four models were reasonable and reliable. However, the PSO-MLP model had higher prediction capability than the other three models, as assessed by the FR and ROC accuracies.  
In summary, the PSO algorithm can effectively optimize the structure parameters of the MLP model with the mini-batch gradient descent algorithm compared with conventional MLP. The proposed PSO-MLP model has the advantage of being able to more globally and accurately predict landslide susceptibility compared with the commonly used BPNN and IV models. This novel PSO-MLP model can be used in other study areas for LSP. Moreover, the produced landslide susceptibility map of Shicheng County is necessary and valuable for the local government to carry out landslide hazard prevention and land use planning.  
Formal analysis: D.L.; investigation: D.L., L.Y. and F.H.; project administration: D.L.; writing--review and editing: D.L., F.H., L.Y., Z.C., J.C., and Z.Y.  
This research is funded by the Natural Science Foundation of China (no. 41807285 and no. 41772370), the Youth National Science Foundation of Jiangxi Province, China (no. 20192 BAB216034), and the China Postdoctoral Science Foundation (no. 2019M652287).  
The authors declare no conflict of interest.