esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 4 Results
### CHINARE2012 Ice Navigation  
Ice concentration was low in the bay during the austral summer of 2012. [PERSON] _[PERSON]_ took 3 days (28-30 November) to navigate from the ice edge to her anchorage point 25 km north of Zhongshan Station. The SIC was less than 70% in most parts of Prydz Bay (Figure 4), with lower concentration (less than 50%) in the area between 76\({}^{\circ}\) and 78\({}^{\circ}\)E, and between 64\({}^{\circ}\) and 66\({}^{\circ}\)S. This area featured the merge of two polynyas; Barrier Bay and Prydz Bay as confirmed in a coincident MODIS image (Figure 8). The low SIC favors utilization of MODIS images without further consideration of the expensive SAR images. The low SIC, however, may subject to rapid retreat of sea ice distribution in response to the weather conditions.  
On 25 November the outer boundary of an anticyclone with a wind speed of less than 5 ms\({}^{-1}\) was situated over the Prydz Bay, and a low pressure trough had moved over the bay by 27 November (Figure 5). On 29 November a cyclone with its center near 60\({}^{\circ}\)S, 60\({}^{\circ}\)E moved east. With expected increase of wind speed during the cyclone overpass, the SIC was expected to further decrease though the ice surface deformation would intensify. The latter factor was not crucial to incorporate in the route planning as the ice cover appeared to be relatively thin. Operation of SatSINS advised RV _[PERSON]_ to travel in the region between 76\({}^{\circ}\)E and 78\({}^{\circ}\)E, heading south of 66.5\({}^{\circ}\)S to reach the Prydz Bay Polynyna by 28 November. This route ensured avoidance of unfavorable changes in the sea ice cover due to the  
Figure 8: Sea ice in the region of interest as seen in composite Terra and Aqua MODIS imagery acquired on 23 November (**left**) and 25 November (**right**), 2012. The blue squares are the navigation GPS points of RV _Xuelong_ from 28 November to 3 December 2012. The zone delineated by the two red lines presents the boundaries of the recommended sector for the route (no waypoints were proposed).  
approach of the low-pressure system (Figure 8). [PERSON] _[PERSON]_ took about 23 h at an average speed of 10.2 knots to pass through sea ice to the polynya. On the afternoon of 29 November she experienced blizzard conditions, with wind speed peaking at 27 ms\({}^{-1}\). On 30 November the vessel had to wait to be escorted by the Russian ice breaker through the 25 km of the land-fast ice to the final destination.  
It is worth reiterating that only the MSLP reanalysis and surface wind forecasts are needed to develop a route when the ice types and concentration do not seem to be hazardous (<70%). That is to ensure that the strong wind, associated with a possible cyclonic pattern, will not disturb the ice cover which has been estimated from maps generated a few days ago. This approach was not used in the case of the CHINARE2011 because ice concentrations of less than 70% were encountered. The ice conditions in November 2012 demonstrated that daily MODIS imagery can be used together with passive microwave.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 4 LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES  
Differential Evolution (DE) is a stochastic population-based optimization algorithm, starting with randomly generated individuals that evolve through probabilistic operators such as recombination and mutation [30; 31; 32]. The efficiency of DE depends heavily on population size (\(Np\)), the chosen mutation and/or crossover strategy, the mutation-associated scale factor (\(F\)), and the recombination-associated crossover rate (\(Cr\)). Adaptive methods for online modification of control parameters during DE were explored to eliminate the need to tune the parameters. In the literature, novel mutation techniques were introduced.  
In [PERSON] et al. [26], a semi- parameter adaptation scheme was implemented to enhance LSHADE technique's performance. Besides, a hybridization strategy between LSHADE-SPA and CMA-ES was presented and considered successful on a wide range of optimization issues. In this section, the specifics of LSHADE-SPACMA are summarized, which is a recent improvement of LSHADE and is introduced in [PERSON] et al. [26].
# Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks
## 4 Results  
In order to prove the rationality of the novel proposed method, four algorithms are compared, namely FaceNet [27], SBM + MLE [48], CLBM [49], and PisCES [51]. Firstly,FaceNet was chosen because it was the first proposed dynamic web community discovery algorithm that could be compared as a baseline; secondly, SBM + MLE and CLBM were used because they are the latest proposed probabilistic model-based algorithms; finally, PisCES is also a recently proposed non-probabilistic model algorithm. In this paper, the indicators of the following two evaluation models are adopted.  
1. Adjust Rand Index (_ARI_), \(ARI\in[-1.1]\), if the value of \(ARI\) is closer to 1, it means better results. \[ARI=\frac{RI-\mathrm{E}(RI)}{\mathrm{max}(RI)-\mathrm{E}(RI)}\] (28) where \(\mathrm{E}(RI)\) represents the expected value of \(RI\) and \(\mathrm{max}(RI)\) denotes the maximum value of \(RI\).
2. Mean-squared errors (MSE), the smaller the value, the smaller the error, that is, the better the result. \[MSE=\frac{1}{n}\sum_{i=1}^{m}\left(y_{i}-\hat{y_{i}}\right)^{2}\] (29) where \(y_{i}\) is the real data, \(\hat{y_{i}}\) expresses the fitting data, and \(n\) implies the number of samples.  
Figure 4 can simulate the evolution process of an artificial dynamic network over time. The network consists of 156 nodes and 614 edges, and a total of eight time steps are set.  
In Figure 4, there are eight rhombic blocks, and the whole dynamic social network can be represented by the evolution of these eight rhombic blocks over time. The upper part represents the dynamic network of a time step, the lower part denotes the community where the current time step may exist, and the lower part is composed of the nodes with the highest degree of nodes of each color in the network absorbing nearby nodes to form larger nodes. (Absorption rule: connected with the node with the greatest degree and with the same color). For example, in the lower half \(t=1\), there are three submodules, each of which represents a possible community. More specifically, each submodule can be composed of nodes of different colors and sizes, and each color can represent nodes with the same characteristics in the dynamic social network. it can be seen that the community structure of dynamic social networks is phylogenetic over time.
## 3 Simulation Results
### Effects of Different Load Increase Scenarios
#### 3.1.2 IEEE 118-Bus System  
The IEEE 118-bus system is also used as a test model to illustrate the applicability of the proposed approach to large power grids. This test system composes of 54 generators, 64 loads, and 186 lines [31].  
An illustrative example of the single load bus variation (load increase at bus 118) is shown in Figure 9. The CPU times are 1.95 s for the proposed method and 11.27 s for the conventional CPFLOW method. Figure 10 illustrates another test result for the multiple load bus variation (load increase at all buses). The CPU time from this case for the proposed method and CPFLOW method are 2.15 and  
Figure 8: Comparison with \(\lambda^{\max}\) and CPU time between the proposed approach and conventional CPFLOW approach under multiple load change scenario on an IEEE 30-bus system: proposed approach, \(\lambda^{\max}=2.9493\), CPU = 0.29 s; conventional CPFLOW approach, \(\lambda^{\max}=2.9493\), CPU = 1.15 s.  
Figure 7: Comparison with \(\lambda^{\max}\) and CPU (central processing unit) time between the proposed approach and conventional continuation power flow (CPFLOW) approach under single load change scenario on IEEE 30-bus system: proposed approach, \(\lambda^{\max}=1.8777\), CPU = 0.23 s: conventional CPFLOW approach, \(\lambda^{\max}=1.8777\), CPU = 0.86 s.  
6.58 s, respectively. From the two cases presented, it is clearly observed that the proposed approach is faster and runs more efficiently, compared to the conventional CPFLOW approach.
## 7 Conclusions  
In this study, a pioneering work applies the RBF method with polyharmonic polynomials to model the movement of contaminants based on the stationary convection-diffusion equation. The concept of the proposed approach is addressed in detail. Significant findings are concluded as follows.  
1. In this study, we demonstrated that the radial basis function method with polyharmonic polynomials could achieve accurate results for inverse problems of the stationary convection-diffusion equation. Due to the meshless nature, the proposed RBF method is superior to solving the inverse problems in groundwater pollution problems with highly complicated domains such as the multiply-connected domains containing a finite number of cavities;
2. The polyharmonic RBFs with a certain order are often used for function approximation. Because the order of the conventional polyharmonic RBF is fixed and needs to be given prior to the analysis, it is often challenging to determine the certain order of the polyharmonic RBF. In this study, we proposed polyharmonic polynomials (PPs). The PPs are a series of polyharmonic RBFs, including any order of the polyharmonic RBFs. Accordingly, the order of the polyharmonic RBF is not required to be given prior to the analysis;
3. Numerical examples in simply and multiply connected domains such as cavities with complicated shapes were carried out. We may recover the missing boundary observations such as concentration on the remaining boundary or those of the cavities with highly accurate results using more terms of the PPs;
4. Comparative analysis was conducted for three different scenarios for collocating sources, such as sources inside the domain randomly, random sources within a circle containing the domain, and sources outside the domain. It was found that the sources collocated outside the domain exhibit the best accuracy;
5. The results depict that the proposed method could recover highly accurate solutions for inverse problems of the stationary convection-diffusion equation with cavities even with 5% noisy data. Moreover, the proposed method is a meshfree method and collocation only such that we can solve the inverse problems with highly complicated domain shapes easily and efficiently;
6. In this study, a pioneering work attempted to apply the radial basis function method with PPs for inverse problems with very complicated domains. We achieved a promising result for multiply connected domains containing a finite number of cavities. Further studies to investigate the characteristics of the proposed method to solve inverse problems in three dimensions are suggested.  
Conceptualization: J.-E.X. and C.-Y.K.; methodology: J.-E.X., C.-Y.K., and C.-Y.L.; validation: J.-E.X.; data curation: C.-Y.L.; preparing the manuscript J.-E.X.; reviewing and editing: C.-Y.K., and C.-Y.L. All authors have read and agreed to the published version of the manuscript.  
No external funding is received.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
XAI has the higher performance, with a mean squared error of 0.9412, a mean absolute error of 0.9874, consistently below 1, and an R-squared value of 0.94152. [PERSON], [PERSON] et al. [64] utilized the long short-term memory recurrent neural network (LSTM-RNN) model, implementing weekly weather data from Uniform Soybean Tests to predict the genotype responses in diverse environments. It was presented as being superior compared to the others in crop output accuracy. In the results, the R-squared value is 0.796 between the observed and predicted yields, which shows an adaptation of environmental variability. [PERSON], [PERSON] et al. [65] proposed a framework for intelligent crop management using a language model and reinforcement learning. Here, the study on maize crops results in presenting an enhanced approach that provides over 49% improvement in economic profit by reducing the environmental factors. [PERSON], [PERSON] et al. [66] developed a machine-learning-based Met algorithm for forecasting the productivity of crops using data-driven techniques in the farms of Africa by collecting remotely sensed data from 2200 farms. They suggested a model that works as an early warning system on climates to provide an impact on agriculture productivity. It has been observed from the extensive literature review that many researchers applied different approaches to their datasets for soil and crops. Many technological tools are used,such as sensors, drones, and automated devices, to collect and analyze real-time data. Different methods, including machine learning, deep learning, fuzzy systems, explainable AI, etc., were used to analyze the real-time data and the existing standard soil mineral dataset. Here, different measuring parameters were important to identify the best methods for predictions.
## 3 Results and Discussion
### Spatial Distribution  
In order to explore the spatial distribution characteristics of each water quality indicator, the nine indicators were subjected to kriging interpolation, and the water quality status corresponding to each indicator was graded according to the Environmental Quality Standard for Surface Water (GB3838-2002) (Figure 3).  
Among them, NH\({}_{4}\)+-N and TN show a similar spatial distribution pattern, with concentrations in the Tayang River, a tributary of the Wanquan River, and at the confluence of the two rivers at V water quality level or even worse, both of them reaching Class III water quality in most areas, with NH\({}_{4}\)+-N concentrations meeting Class II water quality requirements in most areas; TP concentrations meeting Class III water quality requirements and Class II water quality in the vicinity of the downstream mouth of the Wanquan River and the southern part of Qionghai City; COD, Cr and As reaching Class II water quality requirements in most areas; and COD and BOD meeting Class II water quality requirements in most areas. With Hg\({}_{\text{Mn}}\)\({}^{6+}\)5, the spatial distribution pattern of Hg is the most special, corresponding to water quality class IV or worse in most areas and only in the northernmost part of the river west of Qionghai city.
## 7 Conclusions  
In this work, a RoFSO link for CDMA signal transmission with receivers' diversity is studied. An array of \(M\) laser sources transmits towards an array of \(M\) receivers. Analytical closed-form mathematical expressions are obtained for the estimation of the ABER and the OP, which are used for the numerical performance estimation of the specific system for both, the forward and the reverse link. As it is presented, the CDMA RoFSO link operates adequately well with the spatial diversity scheme even in the worst case scenarios of saturated atmospheric turbulence conditions with enhanced PE influence. Also, in the derived numerical simulations a large number of users are considered that simultaneously access the network for the cases of the forward and the reverse link. The latter situation implies that the effects of IMD, MAI and clipping distortion are also immense.  
M.P.N. carried out the mathematical analysis and simulation results of the manuscript. H.E.N. confirmed their validity.  
This research received no external funding.  
The authors declare no conflict of interest.
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 3 Proposed Approach
### LDIF Approach
#### 3.2.3 Algorithm Complexity Analysis  
In Algorithm 1 of LDIF, a deep forest is used as a super-integrated ensemble model; we first analyze the time complexity of the random forest and complete random forest. Since the trees in both models are CART trees, the complexity in building the trees is the same. When there are \(n\) samples and \(m\) attributes, the time complexity of building the tree is \(O(mn*\log n)\). In this algorithm, two models are used, so the time complexity is \(O(2 mn*\log n)\).  
```
0: User Service QoS dataset \(X\), User location information \(U_{loc}\), Service location information \(S_{loc}\), Word Table \(E_{table}\)
0: The prediction value \(\hat{O}_{us}\)
1: X \(\leftarrow\) (\(U_{i}^{1}\), \(U_{i}^{2}\)... \(U_{i}^{k}\), \(S_{1}^{1}\), \(S_{2}^{1}\)... \(S_{j}^{n}\), \(Q_{ij}\))
2: if\(E_{table}[U_{loc}]\) and \(E_{table}[S_{loc}]\)then
3:\(U_{loc}\gets E_{table}[U_{loc}]\)
4:\(S_{loc}\gets E_{table}[S_{loc}]\)
5: end if
6:\(Sim_{us}\leftarrow\) Equation (1)
7:\(U_{loc}S_{loc}\gets Sim_{us}.Sim_{us}\)
8:\(Previous_{Loss}\leftarrow\infty\)
9:\(Now_{Loss}\leftarrow\) SIS Structure
10:while\(Now_{Loss}<Previous_{Loss}\)do
11:\(Temp_{Loss}\leftarrow\) SIS Structure
12:\(Previous_{Loss}\gets Now_{Loss}\)
13:\(Now_{Loss}\leftarrow Temp_{Loss}\)
14:endwhile
15:\(\hat{Q}_{u,s}=prediction\)
16: return \(\hat{Q}_{u,s}\)
```  
**Algorithm 1** The algorithm of LDIF  
In step 1, it is necessary to use Glove to replace the text category features. If there are \(v\) text words in total, the time complexity of building a corpus is \(O(v^{2})\). Assuming that the feature length of users and services is \(d_{1}\), the local feature size is \(z_{1}\), the sliding step size is \(1\), and the highest order of feature interaction calculation is \(P\), then it takes \(d_{1}-z_{1}+1\) times to complete one scan, denoted as \(Q\). So, the computational complexity of the feature interaction module is \(O(PQz_{1}^{3})\), Then the time complexity of completing a single-dimensional scan layer is \(O(6 PQz_{1}^{3}mn\star logn)\). In step 14, assuming that the max depth of layers is \(k\), the time complexity is \(O(6 kPQz_{1}^{3}mn\star logn)\). The overall time complexity is \(O(6 kPQz_{1}^{3}mn\star logn+v^{2})\).
## 2 Autonomous Vehicle Take over Simulation
### Driver Monitoring System (DMS)  
Research has been conducted to determine the drowsiness, fatigue, and distraction of drivers using a DMS [45; 46]. In manual driving, methods to use steering and pedal input patterns without additional sensors have been proposed [47; 48]. The state of the driver must be determined by measuring vital signs such as gaze, electroencephalograms (EEGs), electrocardiograms (ECGs), skin temperature, and respiration because driver control input is not present in the autonomous driving mode [49]. Moreover, determining abnormal conditions such as the driver's excitement or drowsiness by calculating the heart rate (HR) and heart rate variability (HRV) is also possible with ECG data [50; 51].  
Methods to measure the driver's workload or drowsiness using a breathing pattern have been proposed [52; 53]. Some studies have reported that sleep can stabilize the rhythm without causing significant changes in the respiratory rate [54]. Therefore, monitoring the driver's condition using biometric signals requires further analysis of experimental data. In addition, a sensing technology that can acquire data in a noncontact manner must be developed [55].  
In this study, a respiration and ECG sensor was attached to the driver to analyze data patterns in the manual driving, autonomous driving, and takeover scenarios. As shown in Figure 5, biosignal data were acquired while driving, and peaks were detected in real-time to calculate heart rate and respiration rate. According to the UNECE ALKS regulation [5], four conditions (driver control input, eye blinking, eye closing, and conscious head or body movement) have been cited as examples for determining driver availability. Furthermore, it has been suggested that gaze direction toward the front road or rear-view mirror or head movement toward the driving task could be considered as a criterion for determining the driver's attentiveness. Studies predicting driving behavior from gaze patterns [56] or measuring cognitive workload during driving [57; 58; 59] have been conducted.  
In this study, the driver's gaze data were acquired using a Tobii eye tracker having an accuracy of less than 0.1\({}^{\circ}\) and a sampling rate of 90 Hz. Thus, the location information that the driver is watching on the simulation screen at a level could be used for research using this device via calibration as in Figure 6a [60]. The eye-tracking system was developed by referring to the hardware mounting condition and calibration method proposed in [PERSON]'s study [41]. Figure 6b shows the results of eye tracking when manually driving the experimental course using the MATLAB heatmap function. In this figure, area A represents the simulator screen displayed as the front screen, area B is the cluster monitor between the steering and screen, and area C is the location of the touch display used for NDRT work.  
Figure 4: Steering system and control system block diagram.
## 3 Model Formulation
### Covariate Selection Process
#### 3.4.1 K-Fold  
A common tool used in machine learning for evaluating model accuracy is cross-validation, which partitions the dataset into training and validation sets. This method is typically employed when dealing with supervised data or features intended for prediction. However, in this study, the objective was not to predict a specific variable but to optimize the weights (or estimators) in Equation (3) to minimize the log-likelihood score. To achieve this, the research used a strategy to assess the consistency of these covariate weights using cross-validation. This approach is conceptually similar but not identical to the implementation described in Ref. [41].  
For this purpose, K-fold cross-validation was implemented. In this approach, the data were divided into \(K\) equal-sized folds. Each fold served as the validation set once, while the remaining folds were used for training. This process was repeated \(K\) times, ensuring the stability of the model's performance. Specifically, for this investigation, K-fold cross-validation helped verify the consistency of the covariate weight results provided by the optimization process.