esa-sceva/satcom-chunk-collection huggingface dataset:
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 4 Proposed Method
### Iacs
#### 4.2.2 Average Filtering  
Since the sample's weight is based on the first and second hop neighbors from the sample's points (including neighbors to the blind node neighbor), we consider the complement elements of the neighbor set between the neighbor node with the blind node to remove nodes that provide data less than the second hop neighbor. Total elements from both intersection and complement sets are calculated for their average. If the average contains decimals, the largest (closest to positive infinity) integer value that is less than or equal to the average is selected. If the total elements in the intersection set or complement set is lower than the average, they are considered as out of range, thus they are removed from the localization process. From the scenario given in Figure 3, neighboring node 6 is removed because the total elements in the intersection set are lower than the average of the intersection elements of 4. Then, node 1 is removed (Table 6) since the total elements in the complement set are lower than the average elements of three.  
This step can prevent the blind node from communicating with nodes that provide redundant and less significant information for the localization process. Therefore, the cost of computation and communication are expected to decrease in IACS because fewer nodes are used without ignoring the important data for the localization process.
# DPSIR Model Applied to the Remediation of Contaminated Sites. A Case Study: Mar Piccolo of Taranto
## 7 Impacts
### Human Health  
Great attention has been focused in Europe on the importance of food safety and the relation between diet and human health. Nowadays, it is fundamental to monitor contaminants in the food chain and their impact on human beings, because of the increased use of man-made chemicals due to our modern lifestyles. [PERSON] et al. (2019) investigated contamination levels and public health risks, associated with consuming seafood harvested from the Mar Piccolo areas, as shown in Figure 15. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metal** & **Experiment Typology** & **T15** & **T30** & **T45** & **Metal** & **Experiment Typology** & **T15** & **T30** & **T45** \\ \hline As & In situ & 21 & 27 & 47 & Pb & In situ & 160 & 204 & 268 \\ Ex situ & 2 & 19 & 38 & & Ex situ & 198 & 260 & 323 \\ Cd & In situ & \(-\)16 & 26 & 19 & & In situ & \(-\)4 & 5 & \(-\)1 \\ Ex situ & 10 & 26 & 7 & & Ex situ & \(-\)4 & 6 & 10 \\ In situ & 29 & 146 & 240 & & In situ & 23 & 113 & 204 \\ Cr & Ex situ & 1 & 42 & 201 & & Ex situ & 5 & 13 & 52 \\ Cu & In situ & \(-\)19 & 102 & 69 & Sn & In situ & 125 & 175 & 175 \\ Ex situ & \(-\)31 & 25 & 36 & & Ex situ & 425 & 400 & 525 \\  & In situ & 40 & 59 & 62 & & In situ & 126 & 149 & 187 \\ Fe & Ex situ & 18 & 49 & 51 & & Ex situ & 9 & 117 & 187 \\ In situ & 12 & 33 & 40 & & & In situ & 8 & 29 & 19 \\ Ni & Ex situ & 81 & 172 & 189 & & Se & Ex situ & 10 & \(-\)8 & \(-\)3 \\ In situ & 23 & 151 & 208 & & & In situ & 100 & 550 & 600 \\ Zn & Ex situ & 52 & 148 & 268 & & & Ex situ & 700 & 1250 & 1350 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage increase (%) of metals at T15, T30, and T45 in reference to T0 for the in situ and ex situ bioaccumulation tests ([PERSON], 2017). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollutants** & **Experiment Typology** & **T15** & **T30** & **T45** \\ \hline \(\Sigma\)PCB (ng/g wet weight) & In situ & 31 & 37 & 56 \\ Ex situ & 7 & 14 & 41 \\ In situ & 26 & 29 & 46 \\ Ex situ & 6 & 10 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage increase (%) of PCBs (sum of congeners) at T15, T30, and T45 in reference to T0 for the in situ and ex situ bioaccumulation tests ([PERSON], 2017). They analyzed concentrations of some metals (Cd, Cu, As, Hg, Pb) and PCBs in edible marine organisms from different trophic levels and feeding behavior for bivalve mollusks (_Mytiilus galloprovincialis and Chalurys glabra_), gastropod mollusks (_Hexaplex trunculus_), and some commercial species of fish (_Trachurus trachomatis_, _Boops boops, Sara salpa and Gobius niger_). In reference to the permissible limits set by EC Regulations, Cd and Pb levels were over the limit in the _H. trunculus_ (in all sampling stations) and in the fish _T. trachomatis_, respectively, as shown in Table 6.  
Additionally, PCBs were over the legal limit in all sampled species except for _M. galloprovincialis_ in one station, _C. glabra_, and the _herbivorous fish S. salpa_, as shown in Table 7.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 2 Discretisation
### Translating Continuous Space into Discrete  
Discretisation can be considered as a form of data reduction that is useful for the automatic generation of concept hierarchies from numerical data [12]. The main objective of a discretisation process is to reduce the number of values for a given continuous attribute by dividing the range of values into intervals [15]. Each bin is associated with a distinct discrete value. In this way, the attributes' values can be treated as nominal ones. This kind of preprocessing method is important, because there are algorithms that do not work with continuous features and require the discretised form of the attributes' values [16].  
Among other advantages, the following can be distinguished: lower memory requirements for storage, increased comprehensibility of the description of the presented knowledge [17]--which is more relevant for interpretation--the possible reduction of noise present in the data, and enhanced efficiency of induction algorithms that work on a reduced domain of attribute values, which also influences time needed for operations. However, it should be noted that the discretisation process can cause some loss of information [18]; therefore, it should be used with certain caution and always adjusted to the nature of available data, its characteristics, and inherent nature [19].
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 5 Conclusions  
In this paper, a hierarchical synchronization control strategy is proposed to solve the problem of steering angle control for the ARIS system. The proposed control strategy contains two levels. In the upper controller, VRSM is constructed to reduce the synchronization error caused by the inconsistent disturbance of the left and right wheels. The external disturbance of the left and right wheels is fed back to the dynamic model of VRSM, which makes the left and right wheels suffer the same disturbance in the virtual model. In the VRSM, a PD control element is also introduced to further reduce the synchronization error. The lower controller is designed to track the steering angle of each wheels by using an LADRC, where the uncertainties and external disturbance of each single wheel steering system are observed and compensated by the proposed LESO. The experimental results on the prototype vehicle show that the hierarchical synchronization control strategy for ARIS systems realizes a significant reduction in the steering angle tracking error and synchronization error compared with the non-synchronization control strategies under external uncertain disturbance. Figure 17: The tracking error and the synchronization errors without the proposed control strategy. (**a**) The tracking error of the rear wheels; (**b**) the synchronization error of the rear wheels. Figure 18: The tracking error and the synchronization errors with the proposed control strategy. (**a**) The tracking error of the rear wheels; (**b**) the synchronization error of the rear wheels. **Author Contributions:** Conceptualization, B.D. ; Data curation, B.D. and K.S. ; Formal analysis, W.L. ; Funding acquisition, H.Z. ; Investigation, B.D. ; Project administration, H.Z. and A.Y. ; Resources, A.Y. ; Supervision, H.Z. ; Validation, B.D. and K.S. ; Writing - original draft, B.D. and W.L. ; Writing - review & editing, K.S. and W.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Fundamental Research Funds for the Central University of China (Grant No. PA2019 GDZC0101). **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# Drought and Carbon Cycling of Grassland Ecosystems under Global Change: A Review
## 2 Drought and Grassland \(C\) Cycling under Global Change
### Drought and Grassland C Cycling
Aboveground organisms grow fast, are more susceptible to drought, and have a quicker recovery rate post-drought, while belowground organisms grow slowly, do not easily suffer from drought, and have slower recovery rates post-drought [51]. Also, plant belowground inputs can affect the recovery of belowground communities after drought [52]. Drought may change plants and soil microorganisms by altering the \(C\) transfer process at the plant-microbial interface [53; 54]. Drought can also change contemporary rates of biogeochemical processes by inducing a shift of abiotic drivers and microbial community structure [40; 55]. In hostile environments, droughs can cause the functional thresholds of an ecosystem to shift rapidly, changing a \(C\) sink to a source [56; 57; 58] or reducing resiliency to pests, fires, and disease [59]. In the future, increases in the frequency and intensity of droughs could turn temperate grasslands from \(C\) sinks into sources, with positive \(C\)-climate feedback [60]. In fact, ecosystems suffer from the effects of a single event by switching into alternative ecological regimes and cannot withstand the combination of multiple extreme events [61]. Grassland ecosystems are able to withstand moderate drought and maintain ecosystem functions [38; 62], but severe, extended droughs may induce catastrophic consequences, such as the 1930s Dust Bowl in North America [12; 63]. Droughs can significantly and divergently alter the resilience to new disturbances, such as insects, disease, or the next drought [59]. Droughs can trigger other disturbances such as wildfires [60] and pest invasion [64]. While fire and drought can increase short-term SOC accumulation, their long-term impacts on \(C\) cycling are still unclear [65]. Droughs interacting with high temperatures can cause plant mortality and accelerate seed germination and ecological invasion due to favorable weather conditions [66]. According to the 'fluctuating resources hypothesis', the rainfall after drought may even enhance the chance of invasion [67]. These effects of droughs on diversity, productivity, reproduction, phenology, nutrient cycling, and community resistance to invasion indirectly affect grassland \(C\) cycling [68]. Usually, the impacts of droughs are regulated by many environmental factors [69], as well as ecosystem traits, such as species and functional diversity [70; 71], and succession timing or growth stage [8]. Therefore, alongside global change and human activity, droughs with novel magnitudes, timing, and durations are out of synchrony with the resistance of ecosystems and will have stronger and more complicated effects on \(C\) cycling in the future [17; 72].
## 4 Conclusions  
In this work, a detailed analysis of the capabilities of the Gompertz, logistic, potential, and Bertalanffy models to describe MTS growth curves was carried out. Spheroids simulated with an ABM recently developed were used as \"pseudo-experimental\" data. Two samples of MTS in which the ABM parameters were assumed to have a different variability were considered.  
The best description of the whole growth data was provided by the Gompertz model that showed a value of \(\chi_{\text{all}}\) an order of magnitude smaller than the other models.  
In what refers to the predictive power, [PERSON] and Bertalanffy models provided the best retrospective predictions, and the Gompertz model was the one giving the best prospective predictions.  
Logistic and potential models had difficulties in fitting the data, even when restricted subsets were considered.  
In principle, it is possible to find a certain (reduced) number of spheroids such that one of the models can be fitted to correctly describe a specific part of their growth curves. However, this fitted model does not describe adequately either the remaining volume data of the spheroids included in the fit or the data corresponding to other spheroids in the sample. Usually, experiments of MTS or tumor growth involve quite a few individuals that are followed during a relatively short period and this may explain the \"success\" of some mathematical models in describing the corresponding growth curves.  
In summary, Gompertz model appeared to be the best option to describe the MTS growth data and showed to have enough flexibility to adapt its predictions to the volume growth data variability observed in the simulated spheroids. This indicates that this model should be considered in the clinical applications instead of the exponential model that is currently used.  
Conceptualization, S.R.-A.; D.G.; M.V., and A.M.L.; methodology, S.R.-A., D.G., M.V., and A.M.L.; software, S.R.-A. and A.M.L.; validation, S.R.-A., D.G., M.V., and A.M.L.; formal analysis, S.R.-A., D.G., M.V., and A.M.L.; investigation, S.R.-A., D.G., M.V., and A.M.L.; resources, S.R.-A., D.G., M.V., and A.M.L.; data curation, S.R.-A., D.G., M.V., and A.M.L.; writing--original draft preparation, S.R.-A., D.G., M.V., and A.M.L.; writing--review and editing, S.R.-A.; D.G.; M.V., and A.M.L.; visualization, S.R.-A., D.G., M.V., and A.M.L.; supervision, D.G., M.V., and A.M.L.; project administration, D.G. and M.V.; funding acquisition, S.R.-A., D.G., M.V., and A.M.L. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the Spanish Ministerio de Ciencia y Competitividad (FPA2015-67694-P, PID2019-104888 GB-I00), the European Regional Development Fund (ERDF), and the Junta de Andalucia (FQM0387, P18-RT-3237).  
Not applicable.  
Data Availability Statement: The data of the spheroids simulated with the on-lattice agent-based Monte Carlo model are available upon request from the authors.
## 3 Models' Development and Results
### Bagging and Boosting Models
In this chart, the fraction of records in every quantile that is hit with the overall percentage of hits in the training data is compared. Equation (3) shows its calculation formula. Hits refer to the values higher than the midpoint of the values' range. \[Lift=\frac{(Hq/Rq)}{(Th/Tr)} \tag{3}\]  
where \(Hq\) denotes hits in quantile; \(Rq\) refers to the records in quantile; \(Th\) signifies the total hits; and \(Tr\) is the total records. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum Error** & **Maximum Error** & **Mean Error** & **Standard** \\  & & & & **Deviation** \\ \hline Standard DT & \(-44.944\) & \(51.957\) & \(0.081\) & \(26.845\) \\ Bagged DT & \(-39.5\) & \(32.02\) & \(-1.205\) & \(13.486\) \\ Boosted DT & \(-4.638\) & \(21.466\) & \(3.231\) & \(4.872\) \\ Standard ANN & \(-60.37\) & \(72.076\) & \(-1.552\) & \(25.35\) \\ Bagged ANN & \(-46.864\) & \(42.601\) & \(-3.742\) & \(16.251\) \\ Boosted ANN & \(-15.577\) & \(17.198\) & \(0.314\) & \(7.332\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of models’ error. Figure 7: Actual and predicted values of the models developed in this study. Figure 8 shows the lift chart of the models developed in this investigation. In this chart, elevated lines show better models, especially on the left edge of the graph. As can be seen, boosted DT (XGBT) line was higher than the others. This line also was more stable compared to other lines. Great performance of the XGBT model compared to CART and RF models was expected because of the various capabilities of XGBT that help it to produce a reliable and accurate predictive model. XGBT enjoys the similarity score for a straightaway prune tree, beginning the real modeling goals. In addition, XGBT is a reliable choice for unbalanced datasets, but RF is not trustable for these circumstances. One of the most important differences between XGBT and RF is that the XGBT regularly provides added significance to functional space when decreasing the cost of a model, while RF seeks to provide more inclinations to hyperparameters to optimize the model. XGBT also outperformed all forms of ANN, which can add knowledge and experiences in the field of civil engineering, particularly mining. In comparison with the previous studies, the accuracy of XGBT model (R = 0.994) was more than that of [PERSON] et al. (2018) (R = 0.992), who applied an ANFIS model to the same database. They used DD, ultrasonic velocity, quartz content, and plagioclase to develop their predictive model. They selected these inputs using simple and multiple regression models. In addition, the accuracy achieved in this study was higher than in other studies reported in Table 1.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 5 Case Study
### Wind Speed Data Description of the Cases  
The wind speed data selected in Case A, Case B and Case C is the wind speed series from October to December of the anemometer tower 1, 2 and 3 respectively. The detailed information of the three anemometer towers is shown in Table 1.  
The direct multistep forecasting time of the wind speed series was taken as four hours according to the conclusion of Section 4. In each case, 864 wind speed data from 6 days in December were randomly selected as the test set of the forecasting model, and the wind speed data from the previous two months were used as the training set of the forecasting model. Taking the wind speed series \(\{v(t)\), \(t=1,2,\ldots,N\}\) of a certain case as an example, the training and test set of the forecasting model are illustrated as follows. The training set is \(\{v(t)\), \(t=1,2,\ldots,N-864-24\}\), in which \(\{v(t)\), \(t=1,2,\ldots,N-864-24\}\) is the training input sample and \(\{v(t)\), \(t=24+1,24+2,\ldots,N-864-24\}\) is the training output sample. Similarly, the test set is \(\{v(t)\), \(t=N-864-24+1,N-864-24+2,\ldots,N\}\), in which \(\{v(t)\), \(t=N-864-24+1,N-864-24+2,\ldots,N-24\}\) is the test input sample and \(\{v(t)\), \(t=N-864+1,N-864+2,\ldots,N\}\) is the test output sample.
# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation
#### 3.1.3 Feature Engineering Using Spammer Behavioral Features Analysis
Labeled datasets help the researcher to implement the supervised spam review detection methods, therefore, to overcome this problem, usually people are hired to write reviews about products and services. However, this study has observed that online workers (called Tuckers) usually provide a large collection of word distributions. As a result, linguistic n-gram approach could falsely detect spam reviews with high accuracy. There is a need for publicly available real-world labeled datasets so that researchers become able to train their proposed models using standard real-world datasets. It is also observed that most of the review datasets have a limited number of attributes and present attributes are not able to detect spam reviews accurately. For example, Amazon.com review dataset contains only the following attributes: product ID, product name, product title, product price, user ID, profile name, helpfulness, review score, review time, review summary, and review text. To achieve better accuracy in spam review detection, there is a need for multi-dimensional review datasets which contain more attributes, such as IP address and posting location of the reviewer [45]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper ID** & **Dataset Used** & **Feature Engineering Approaches** & **The Accuracy of Spam Detection** \\ \hline
[35] & Crawled from Amazon website & Review and reviewer features & 78\% \\ \hline
[28] & Crawled from Amazon website & Product characteristics, review and reviewer features & 78\% \\ \hline
[15] & Hotel review dataset through Amazon Mechanical Turk (AMT) & Bi-gram & 89.6\% \\ \hline
[27] & Hotel review dataset through Amazon Mechanical Turk (AMT) + doctor and hotel reviews from domain experts & Part-of-speech tagging + Unigram + Linguistic Inquiry and Word Count & 65\% \\ \hline
[4] & Yelp’s real dataset & Behavioral features + Bi-gram & 86.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of previous works and results for spam review detection using different feature engineering approaches.
## 2 Navigation System
### Integrated Navigation Systems  
Currently, there are a large number of navigation sensors (GPS, GNSS, and IMU in microelectromechanical systems) on the market that are used on board UAVs for autonomous flight. The navigation solution (position and trim usually stored in a GPX file, GPS exchange format) estimated by the UAV's internal sensor (metric accuracies on position and approximately \(\pm\) 2\({}^{\circ}\) on trim) is associated with the images [46]. INS/GNSS integration architectures can be classified as: uncoupled, loosely coupled (LC), tightly coupled (TC), and deeply coupled or ultra-tightly coupled [47; 48]. A widely used integration scheme for positioning UAV-Borne sensors is LC due to the simplicity of the integration architecture [49]. In this integration strategy, the position and velocity estimated by the GNSS Kalman filter (KF) are processed in the navigation KF to assist the INS (decentralised or cascaded filtering). However, this architecture contains errors related to the position and velocity information provided by the GNSS KF that are correlated over time; this problem can cause performance degradation or even instability of the navigation KF, so it is necessary to compensate for these correlations [50]. This means that, from an operational point of view, special care must be taken not to lose the lock with satellites, i.e., carefully planning the flight mission by verifying the good coverage of GNSS satellites and sharp turns or sudden movements due to strong winds. Indeed, it is necessary not to lose GNSS raw measurements, especially if the acquisition is based on phase measurements.  
A commercial example of a GNSS-assisted inertial solution is the APX-15 UAV from Applanix (Richmond Hill, ON, Canada), which is a platform capable of achieving centimetre accuracy on control points using next-generation GNSS-inertial MEMs sensors and implemented in a single board that is small in size, low power, and weighs only 60 g [51].
## 3 Routing in Satellite Networks
### Routing Protocols for Satellite Networks
#### 3.2.1 Boundary Routing  
The BGP-satellite (BGP-S) version is a recommended protocol for the smooth operation of IP connections in both networks during the consolidation of tellurian and satellite networks. It is a satellite variant of the traditional border gateway protocol. The BGP-S protocol was first introduced in [98], and the simulation results are shown in [99]. BGP-S is a unified routing protocol envisaged to work solely on a tellurian gateway in an autonomous system and transmits discovered routes using the BGP4 [100] protocol. Satellite networks are considered to be autonomous systems in terrestrial networks. However, autonomous systems in satellite networks have their own characteristics; for example, the delay is greater than that of autonomous systems in terrestrial networks [101].