esa-sceva/satcom-chunk-collection huggingface dataset:
# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 3 Results
### Principal Component Analysis  
The 2-dimensional PCA of all the spectral image parameters observed at different timings after herbicide treatment showed a gradual clustering of herbicides, with time depending on the herbicide mode of action (Figure 6, Table S1). Paraquat (PSI) exhibited a noticeable separation from the other herbicides at as early as 3 HAT. At 24 HAT, tiafenacil (PPO) became independently clustered from the other herbicides. At 48 HAT, glufosinate (GS) and glyphosate (EPSPS) were independently clustered. Isoxaflutole (HPPD) was the last one separated from the other herbicides at as late as 120 HAT. However, pencosulam (ALS) failed to be independently clustered, even at 120 HAT. The PCA using the pooled data from 3 HAT to 120 HAT demonstrated a clear clustering of herbicides based on the herbicide mode of action except for pencosulam (ALS), which was not separated from the untreated control due to its very low efficacy against crabgrass (Figure 7, Table S2). The 3-dimensional PCA for the spectral image parameters observed for each time point and for the pooled data also showed a similar clustering depending on the herbicide mode of action (Figures S1 and S2). The quick-acting herbicides such as paraquat (PSI), tiafenacil (PPO), and glufosinate (GS) required only 2 days after herbicide treatment for a clustering by the herbicide mode of action, while the slow-acting herbicides such as glyphosate (EPSPS) and isoxaflutole (HPPD) required a bit of a longer period after herbicide treatment.  
Figure 6: PCA results of six spectral parameters of crabgrass observed at 3, 6, 24, 48, 72, and 120 h after herbicide treatment with different modes of action. Each symbol represents a replication.
## 4 Egocentric Betweenness Measure in VANETs
### Proposed Approach  
Due to the high mobility of the vehicles in VANETs, getting all network topology knowledge is not an easy task. The egocentric betweenness measure is computed using only the available local knowledge; in that case, the adjacency matrix of one-hop neighbours. Each vehicle gets the local knowledge of the network topology by means of periodic beacon packets broadcast by its neighbours. The beacon transmission frequency employed was 1 Hz. Since the vehicle's beacon packets are only useful to adjacent neighbours, the beacons received are not forwarded. Therefore, the information exchanged among vehicles is lists of neighbours, as illustrated in Figure 4. In this example with four vehicles, the grey vehicle (labelled as 1), receives the lists of neighbours of all vehicles that are currently within its transmission range (vehicles labelled as 2, 3 and 4). Once having received the lists, the vehicle constructs the adjacency matrix representation and calculates the egocentric betweenness score, according to Section 3.2. Each vehicle updates the egocentric betweenness score, whenever a new list is received.  
The main steps of our proposed approach are presented in Algorithm 1. The algorithm requires the list of neighbours of all vehicles that are currently within the transmission range (represented by \(L\)), as input information. The output information is the current list of neighbours and the egocentric betweenness score. Upon receiving a new list of neighbours, the adjacency matrix is updated to represent a new ego-network topology (Lines 2 and 3). After the adjacency matrix is updated, the algorithm computes the egocentric betweenness score (Lines 4, 5 and 6). Thereafter, the list of neighbours is also updated (Line 7). Lastly, a beacon packet containing a current list of neighbours is broadcast (Line 8).  
Figure 4: An illustrative example of the beacon packets’ exchange among the vehicles to calculate the egocentric betweenness score. In this case, the grey vehicle, labelled as 1, is doing the calculation.  
```
inputs :\(L=\{l_{1},l_{2},...,l_{n}\}\) list of neighbors of all vehicles that are currently within the transmission range output :Egocentric betweenness score and list of neighbors
1 foreach\(l_{i},l_{i}\in[1,n]\)do
2 ifisNew(\(l_{i}\))then
3\(A=updateAdjacencyMatrix(l_{i})\);
4 ifwasUpdate(\(A\))then
5\(E=A^{2}[1-A]\);
6\(egoValue=computeEgoBetweenness(E)\);
7\(urlListNeighbors=updateMyListNeighbors()\);
8\(sendBeacon(myListNeighbors)\);
```  
**Algorithm 1**Calculation of the egocentric betweenness scores
# Abnormal Ship Behavior Detection Based on AIS Data
## 4 Results and Discussion
### Experimental Results for Real Datasets  
In this section, we describe the use of the above-mentioned introduction framework to assess the degree of object suspicion from a ship's spatial and thematic attributes to detect  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Recall** & **Precision** & **F-Score** \\ \hline Our method & 0.988 & 0.780 & 0.975 & 0.867 \\ GMM & 0.733 & 0.820 & 0.142 & 0.243 \\ MT-MAD & 0.984 & 0.740 & 0.949 & 0.831 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effectiveness of our method in abnormal behavior detection for ship spatial attributes.  
Figure 8: Simulation data for abnormal behavior of ship thematic attributes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Recall** & **Precision** & **F-Score** \\ \hline Our method & 0.989 & 0.950 & 0.809 & 0.874 \\ DBSCAN & 0.995 & 0.520 & 0.347 & 0.416 \\ [PERSON]’s method & 0.987 & 0.950 & 0.792 & 0.864 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of this method in abnormal behavior detection for ship thematic attributes.  
anomalies with the real data. In the first step, we extracted the network structure of the maritime routes based on the principal graph structure learning and tree linking strategy, and then we detected the abnormal attributes of ship space with [PERSON]'s criterion. The second step was to determine the anomaly score for the distribution of thematic attributes for the maritime routes based on the isolation forest algorithm.  
China's vast sea areas and numerous ports constitute a complex route network in the coastal waters of China. The number of ports in each sea area was counted according to the boundary of the sea area. The number of ports in each sea area is shown in Table 4.  
In order to excavate more representative lanes, two sea areas with the most travel records were chosen as the target areas: the East China Sea and the Bohai Sea. In order to avoid skewness in the data, the ship trajectories before the first three ports in the East China Sea and the Bohai Sea were selected as the experimental data, which are the ports of Xihoumen, Majishan, and Liuheng in the East China Sea and the ports of Changxingdao, Bayuquan, and Caofeidian in the Bohai Sea. Figure 9 shows the trajectory data we selected to travel to and from these six ports.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Sea Area** & **Number of Tracks** \\ \hline East China Sea & 10,746 \\ Bohai Sea & 870 \\ Yellow Sea & 820 \\ South China Sea & 206 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics on the number of ports in the sea area.  
Figure 9: Ship trajectory in experimental area.
# Evaluating Soil Bacteria for the Development of New Biopreparations with Agricultural Applications
###### Abstract  
This study evaluates various strains of soil bacterial for use in the development of new biopreparations. Mesophilic spore-forming bacteria were isolated from cultivated soil and analysed for their enzymatic activity, ability to decompose crop residues, and antagonistic properties towards selected phytopathogens. Notably, this is the first cytotoxicity assessment of soil bacterial metabolites on _Spodoptera frugiperda_ Sf-9 (fall armyworm). _Bacillus subtilis_, _Bacillus licheniformis_, _Bacillus velezensis_, _Penaobacillus amylolyticus_, and _Prestia megaterium_ demonstrated the highest hydrolytic potential for the degradation of post-harvest residues from maize, winter barley, and triticale. They exhibited antimicrobial activity against at least three of the tested phytopathogens and demonstrated the ability to solubilize phosphorus. Metabolites of _B. licheniformis_ (IC50 = 8.3 mg/mL) and _B. subtilis_ (IC50 = 144.9 mg/mL) were the most cytotoxic against Sf-9. We recommend the use of the tested strains in industrial practice as biocontrol agents, plant growth biostimulants, crop residue decomposition stimulants, and bioinsecticides. Future studies should focus on assessing the efficacy of using these strains under conditions simulating the target use, such as plant microcosms and greenhouses and the impact of these strains on the abundance and biodiversity of native soil microbiota. This research can serve as a model procedure for screening other strains of bacteria for agricultural purposes.  
biopreparations; soil bacteria; spore-forming bacteria; crop residues; crop residues decomposition; antimicrobials +
Footnote †: journal: Applied sciences  
_Article_
## 4 Validation Results via Simulation
### Simulation Test 2 (European Standard 220 V @ 50 Hz)  
Figure 12 presents the waveforms at the converter for the simulation test  
Equal to the previous test, the DS-SMC and SS-SMC approaches were compared. Again, the voltage applied to semiconductors is always lower for the DS-SMC. The expression \(V_{dc}=V_{in}+0.338V_{m}+V_{+}\) being \(V_{+}\) settled to 5 V is used to define input voltage. In addition, similar to the previous case, the alleviation of the semiconductor voltage stress is at least of 30 V and becomes up to 100 V. A more accurate analysis is presented in the next subsection.
# A Single-Launch Deployment Strategy for Lunar Constellations
## 2 Low-Energy Captures in the Sun-Earth-Moon System
### Extension to the Sun-Earth-Moon System
In fact, a canonical transformation \(\mathcal{T}:(\mathbf{q},\mathbf{p})\rightarrow(\mathbf{Q},\mathbf{P})\), developed in Appendix A, can be introduced to rearrange Equation (14) to a form equivalent to \(H\) plus negligible higher order terms in the perturbations [49]  
\[\tilde{H}^{(e)}(\mathbf{Q},\mathbf{P})=\frac{1}{2}\Big{(}P_{1}^{2}+P_{2}^{2}+ P_{3}^{2}\Big{)}+P_{1}Q_{2}-P_{2}Q_{1}-\frac{K}{2}\Big{(}2Q_{1}^{2}-Q_{2}^{2}-Q_{3} ^{2}\Big{)}+o\bigg{(}e_{M},\frac{\mu_{0}}{r_{0}}\bigg{)} \tag{15}\]  
Because \(\tilde{H}^{(e)}(\mathbf{Q},\mathbf{P})\) is equivalent to \(H(\mathbf{q},\mathbf{p})\), all the results derived from the application of the canonical transformation \(\mathbf{T}_{N}\), and in particular the characterization of low-energy captures, are still valid [51]. Capture orbits with the desired orbital elements can be designed in the \((\mathbf{x},\mathbf{y})\) coordinates and then converted to position and velocity coordinates in \(\mathcal{F}_{r}^{(e)}\), applying in order the inverse of the two canonical transformations \([\mathbf{Q},\mathbf{P}]^{T}=\mathbf{T}_{N}^{-1}[\mathbf{x},\mathbf{y}]\) and \(\mathcal{T}^{-1}:(\mathbf{Q},\mathbf{P})\rightarrow(\mathbf{q},\mathbf{p})\).
## 3 Results
### Influence of Electrical Field on the Performance of MFRO Desalination
The increase in permeate flux with the voltage was relatively small at a salt concentration of 1000 mg/L, but the increase in permeate flux gradually increased with increasing salt  
Figure 5: Under pure brine conditions, an electric field was added to compare the membrane performance of the Multi-field reverse osmosis under different voltage conditions: (**a**) changing the solute rejection, (**b**) changing the permeate flux ratio, (**c**) changing the _SEC_, (**d**) linear fit of permeate flux to voltage. concentrations. This suggests that the electric field force has a more significant impact at higher salt concentrations. One possible reason for this observation is that the electric field force can affect the arrangement and orientation of water molecules. This leads to the creation of a driving force that accelerates the movement and diffusion of water molecules, facilitating their passage through the semipermeable membrane. Additionally, concentration polarization at the membrane surface is reduced by ionic electromigration induced by the electric field. Particularly in high-salinity water, the salt ions are highly concentrated and prone to polarization, which inhibits water permeation. The electric field effectively overcomes these effects, thereby increasing the permeate flux of the RO membrane. This effect becomes more pronounced with higher salt concentrations.
## 5 Experimental Results and Cases Analysis
### Evaluation and Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Algorithm** & **IMOAHLO** & **NSGA-II** & **MOHLO** & **SMPSO** \\ \hline HV & 0.5636 \(\pm\) 0.2892 & 0.5040 \(\pm\) 0.2676 & 0.4913 \(\pm\) 0.3023 & 0.3870 \(\pm\) 0.2193 \\ IGD & 0.1271 \(\pm\) 0.1948 & 1.8104 \(\pm\) 2.2911 & 0.3477 \(\pm\) 0.3263 & 1.4313 \(\pm\) 1.7409 \\ GD & 0.0320 \(\pm\) 0.0492 & 0.0952 \(\pm\) 0.1586 & 0.4678 \(\pm\) 0.4749 & 0.7442 \(\pm\) 0.4936 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results of four algorithms. and stable convergence toward the reference front across repeated runs. GD measures the average distance from the approximated Pareto front to the true front. Again, smaller values are preferable. In this metric, IMOAHLO obtains 0.0320 \(\pm\) 0.0492, outperforming NSGA-II (0.0952 \(\pm\) 0.1586), MOHLO (0.4678 \(\pm\) 0.4749), and SMPSO (0.7442 \(\pm\) 0.4936). The low GD mean and small deviation of IMOAHLO indicate that its solutions lie consistently close to the true Pareto set, confirming its superior convergence ability. Compared to the other three algorithms, IMOAHLO's average HV is 11.8% higher, while its average IGD and GD values are only 36.6% and 33.6% of those of the other methods, respectively. Taken together, the results in Table 3 clearly demonstrate that IMOAHLO outperforms the other algorithms across three evaluation metrics. Its higher HV, coupled with a significantly lower IGD and GD, confirms that IMOAHLO not only finds a wider, more diverse set of Pareto-optimal solutions but also converges more precisely to the true optimal front. These quantitative improvements substantiate IMOAHLO's enhanced search capability and robustness in RHF-blending scenarios.
# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 4 Result and Discussion
### Precision  
Tabulated results of a precision comparison of the DBN-MOA method to those of other existing methods are shown in Figure 6. The graph demonstrates how the ML approach led to improved precision and performance. By way of comparison, the SVM model, the RF model, the KNN model, the LR model, and the DT model all achieved precisions of 65.821%, 69.832%, 72.465%, 77.682%, and 83.312% with data set 100, respectively. However, DBN-MOA has demonstrated its best performance with varying sizes of data. Furthermore, under 600 data points, the precision values for the SVM, RF, KNN, LR, and DT models are 68.132%, 71.132%, 76.685%, 82.705%, and 88.932%, respectively, while the DBN-MOA model has a precision value of 94.621%.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 2 UWB Forklift Localization Method
### UWB Positioning  
Several UWB anchors are deployed in the target area to perform the forklift localization through a simple multilateration algorithm. Since the commercial system adopted in the experimental analysis can use up to four anchors per each timestep, the following method is described by considering only four anchors, even if more of them are detected.  
Let us consider a set of \(N_{UIWB}\) UWB anchors deployed in the warehouse at known locations \(\mathbf{p_{uJ}}=[x_{u,j},y_{u,j},z_{u,j}]^{T},j\in[1,\dots,N_{UIWB}]\), as outlined in Figure 2. At each timestep \(k\), we can write the vector \(\mathbf{r_{k}}\) of \(N_{UIWB}\) elements, in which the \(j\)-th element \(r_{k}^{(j)}\in\{0,1\}\) is 1 if the \(j\)-th UWB anchor data are available at the timestep \(k\), and it is 0 otherwise. Let us assume that, at each timestep, at least four anchors are detected by the onboard UWB tag, and a distance measurement \(d_{i}\) is available for each anchor, where \(i\) represents the index of an anchor in the set of available anchors at the timestep \(k\). For simplicity, let us assume that the set of indexes \(i=[1,2,3,4]\) is always chosen for each timestep.  
At each timestep, the following equations system can be written:  
\[\begin{cases}(x_{u,1}-x_{k}^{UIWB})^{2}+(y_{u,1}-y_{k}^{UIWB})^{2}+\Delta z^ {2}=d_{1}^{2}\\ (x_{u,2}-x_{k}^{TIWB})^{2}+(y_{u,2}-y_{k}^{TIWB})^{2}+\Delta z^{2}=d_{2}^{2} \\ (x_{u,3}-x_{k}^{TIWB})^{2}+(y_{u,3}-y_{k}^{TIWB})^{2}+\Delta z^{2}=d_{3}^{2} \\ (x_{u,4}-x_{k}^{TIWB})^{2}+(y_{u,4}-y_{k}^{TIWB})^{2}+\Delta z^{2}=d_{4}^{2} \end{cases} \tag{5}\]where we consider \(\Delta z=z_{u,j}-z_{k}^{UIWB}\) a constant for each timestep and for each anchor. The system can be linearized if we subtract the first row to all the others. With such an operation, all of the quadratic unknown terms are canceled. After some algebraic manipulations, we obtain:  
\[\begin{bmatrix}(x_{u,2}-x_{u,1})&(y_{u,2}-y_{u,1})\\ (x_{u,3}-x_{u,1})&(y_{u,3}-y_{u,1})\\ (x_{u,4}-x_{u,1})&(y_{u,4}-y_{u,1})\end{bmatrix}\begin{bmatrix}x_{t}^{UIWB}\\ y_{k}^{UIWB}\end{bmatrix}=\begin{bmatrix}(d_{1}^{2}-d_{2}^{2})+(x_{u,1}^{2}+y_{ 1,1}^{2})-(x_{u,2}^{2}+y_{u,2}^{2})\\ (d_{1}^{2}-d_{3}^{2})+(x_{u,1}^{2}+y_{u,1}^{2})-(x_{u,3}^{2}+y_{u,3}^{2})\\ (d_{1}^{2}-d_{4}^{2})+(x_{u,1}^{2}+y_{u,1}^{2})-(x_{u,4}^{2}+y_{u,4}^{2}) \end{bmatrix} \tag{6}\]  
which is an over-determined linear equation system of the form  
\[\mathbf{Ax}=\mathbf{b} \tag{7}\]  
which can be solved by computing the Moore-Penrose pseudo-inverse matrix \(\mathbf{A}^{\dagger}\) to get a measurement of \([x_{k}^{UIWB},y_{k}^{UIWB}]^{T}\):  
\[\begin{bmatrix}x_{k}^{UIWB}\\ y_{k}^{UIWB}\end{bmatrix}=\mathbf{x}=(\mathbf{A}^{T}\mathbf{A})^{-1}\mathbf{A}^ {T}\mathbf{b}=\mathbf{A}^{\dagger}\mathbf{b} \tag{8}\]
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method
### Data Description
#### 2.1.5 ERA5 Soil Temperature Data  
The dataset is the soil temperature at level 1 (in the middle of layer 1). The European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) has a four-layer indication for the soil, where layer 1 ranges from 0 cm to 7 cm. Soil temperature is measured in the middle of each layer. When a freeze-thaw transition occurs in soil, the water in the soil changes from solid ice to liquid water. The dielectric constants of water and ice differ significantly, so the reflectivity changes accordingly. By analyzing the changes in snow depth and SR during the period when the surface temperature does not change from positive to negative (or from negative to positive), we assessed whether the snow depth can be monitored by CYGNSS.
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 4 Results and Analysis  
### The Parameters of a 5 MW Floating VAWT  
The H-type floating VAWT studied as an example is shown Figure 5. The upper wind turbine refers to the VAWT proposed by [PERSON] et al. (2015), and the floating foundation is Truss Spar type, which consists of buoyancy tank, the upper machinery tank, truss structure, heave plates and ballast tank. The specific parameters of the floating VAWT are shown in Table 1. The floating VAWT was located by four groups (one line for each group), the angle between each group of mooring lines was \(90^{\circ}\). The lines were designed in the form of chain-cable-chain, and were connected with the floating foundation by the fairleads. The specific parameters of the mooring line are indicated in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Items** & **Values** \\ \hline Rated power & 5.00 (MW) \\ Number of blade & 2.00 \\ Radius of rotor & 37.00 (m) \\ Height of blade & 78.75 (m) \\ Chord of blade & 3.50 (m) \\ Hub height & 72.00 (m) \\ Airfoil profile & NACA0018 \\ Rated wind speed & 15.00 (m/s) \\ Rated rotor speed & 12.00 (rpm) \\ Mass of blades & 76.75 (ton) \\ Draft of the foundation & 65.00 (m) \\ Total water displacement & 7475.10 (t) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the floating VAWT. VAWT: Vertical Axis Wind Turbine.  
\begin{table}
\begin{tabular}{c c c} \hline
**Items** & **Chain** & **Cable** \\ \hline Length (m) & 55 & 135 \\ Diameter (mm) & 147 & 84 \\ Wet weight (Kg/m) & 375.7 & 25.6 \\ Dry weight (Kg/m) & 432.2 & 29.5 \\ Axial rigidity EA (N) & \(1.6\times 10^{9}\) & \(3.9\times 10^{9}\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the mooring line. EA: XXX.
# Classification of Retail Products: From Probabilistic Ranking to Neural Networks
## 9 Evaluation and Results
Additionally, we have assessed the results for the accuracy and the F1-score for the last four algorithms with respect to _Top_1_, _Top_2_ and _Top_3_ on a reduced dimension dataset with 400, 500, 600, 700, and 800 principal components, as shown in Figure 7. The FKNN obtains the best accuracy on _Top_1_, _Top_2_, and _Top_3_. The highest accuracy, in _Top_1_, with the appropriate PCA, is PCA-800 is (84.59%), and, in _Top_2_ and _Top_3_, with the suitable PCA, it is PCA-600 is (91.53%, 94.13%), respectively. The best algorithm to obtain the highest result in the F1-score is FKNN in _Top_1_ with the appropriate principal component which is PCA-800 at the rate of 84.59%, while, in _Top_2_ and _Top_3_, the best algorithm is MLP with PCA-800 at the rate of 90.46%, 91.09%, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PCA** & **400** & **500** & **600** & **700** & **800** \\ \hline \hline
**Accuracy** & 0.8327 & 0.8218 & 0.8366 & 0.8346 & 0.8388 \\
**Precision** & 0.8332 & 0.8220 & 0.8362 & 0.8343 & 0.8391 \\
**Recall** & 0.8324 & 0.8218 & 0.8360 & 0.8340 & 0.8389 \\
**F1-score** & 0.8328 & 0.8219 & 0.8361 & 0.8342 & 0.8390 \\ \hline \hline
**Accuracy** & 0.8407 & 0.8324 & 0.8417 & 0.8404 & 0.8436 \\
**Precision** & 0.9688 & 0.9717 & 0.9814 & 0.9803 & 0.9815 \\
**Recall** & 0.8324 & 0.8218 & 0.8360 & 0.8340 & 0.8389 \\
**F1-score** & 0.8954 & 0.8905 & 0.9029 & 0.9013 & 0.9046 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: MLP: Quality measures by PCA components. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PCA** & **400** & **500** & **600** & **700** & **800** \\ \hline \hline \multicolumn{6}{c}{_Top_1_} \\ \hline
**Accuracy** & 0.7071 & 0.6909 & 0.6928 & 0.6885 & 0.6923 \\
**Precision** & 0.5908 & 0.5651 & 0.5664 & 0.5395 & 0.5648 \\
**Recall** & 0.5722 & 0.5467 & 0.5471 & 0.5176 & 0.5411 \\
**F1-score** & 0.5814 & 0.5557 & 0.5577 & 0.5283 & 0.5526 \\ \hline \hline \multicolumn{6}{c}{_Top_2_} \\ \hline
**Accuracy** & 0.8126 & 0.8095 & 0.7949 & 0.7994 & 0.7946 \\
**Precision** & 0.6850 & 0.6756 & 0.6445 & 0.6179 & 0.6544 \\
**Recall** & 0.6619 & 0.6556 & 0.6348 & 0.6058 & 0.6424 \\
**F1-score** & 0.6733 & 0.6655 & 0.6396 & 0.6117 & 0.6483 \\ \hline \hline \multicolumn{6}{c}{_Top_3_} \\ \hline
**Accuracy** & 0.8568 & 0.8534 & 0.8404 & 0.8489 & 0.8389 \\
**Precision** & 0.7243 & 0.7059 & 0.6878 & 0.6391 & 0.6647 \\
**Recall** & 0.7114 & 0.6987 & 0.6723 & 0.6272 & 0.6597 \\
**F1-score** & 0.7178 & 0.7023 & 0.6799 & 0.6331 & 0.6622 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: XGBoost: Quality measures by PCA components. To sum up, Figure 8 shows the comparison in terms of accuracy among all the models (for their optimal values) regarding _Top_1_, _Top_2_ and _Top_3. The best in _Top_1_ is FKNN followed by KNN. In addition, the appropriate one in _Top_2_, _Top_3_ is BM25 followed by FKNN.