esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Olfactory Sensory Evaluation of Different Oolong Tea Samples  
The expert evaluation results and the consumer acceptance evaluation results are shown in Figure 1 and Table S1. In Figure 1A-D, the scores of both experts and consumers are shown to be significantly higher (\(p<0.01\)) for the suitable tea cultivars (MZ, HD and TGY) than the unsuitable cultivars (FD, WNZ, and YS). Based on expert evaluation, the products of the suitable cultivars had a stronger and richer floral and fruity aroma than  
Figure 4: Network diagram of the relationship between electronic nose data, HS-SPME-GC-MS data, and sensory data in six tea cultivars. Node information: V1: Benzaldehyde; V2: 6-Methyl-5-Hepten-2-one; V3: 2-Pentylfuran; V4: Octanal; V5: Phenylacetaldehyde; V6: \(\beta\)-Ocimene; V7: (Z)-Furanlinalo oxide; V8: (E)-Furanlinalool oxide; V9: Linalool; V10: Phenylethyl alcohol; V11: (3E)-4,8-Dimethyl-1-3,7-nonatriene; V12: Cosmene; V13: (R,S)-5-Ethyl-6-methyl-3E-hepten-2-one; V14: (E)-Pyranoid linalool oxide; V15: Linalool oxide pyranoside; V16: Methyl salicylate; V17: Butanoic acid hexyl ester; V18: \(\alpha\)-Terpineol; V19: Decanal; V20: (E,E)-2.4-Nondienal; V21: Linaly formate; V22: Geraniol; V23: (Z)-2-Decanal; V24: Indole; V25: Methyl gernate; V26: \(\alpha\)-Cubebene; V27: (E)-2-Undecanal; V28: (Z)-3-Hexenyl hexanoate; V29: (Z)-3-Hexenyl (Z)-3-hexenoate; V30: (Z)-Jasmone; V31: \(\beta\)-Caryophyllen; V32: \(\alpha\)-Ionone; V33: \(\gamma\)-Elemene; V34: (Z)-Geranylacetone; V35: Humulene; V36: (E)-\(\beta\)-Famesene; V37: (E)-2-Dodecanal; V38: \(\beta\)-Ionone; V39: (Z,E)-\(\alpha\)-Farnesene; V40: \(\alpha\)-Muuronlee; V41: \(\alpha\)-Farnesene; V42: \(\delta\)-Cadinene; V43: (E)-Nerolidol; V44: (Z)-3-Hexenyl benzoate; V45: Geranyl isovalerate; V46: T-Muuronol; V47: \(\alpha\)-Cadinol. Connection information: the red line segment represents negative correlation; the green line segment, positive correlation; line segment thickness, correlation magnitude.  
those of the unsuitable cultivars (Table S1), and the floral and fruity aroma of Oolong tea was not influenced by different producing regions [18]. Based on the evaluation of consumers, the acceptance of tea products may be influenced by multiple factors apart from aroma (Table S2). Multiple regression analysis (Tables S2 and S3) and logistic regression analysis (Table S4) indicated that among several common factors influencing consumer acceptance, aroma is always an important factor, even with the interaction of different factors (Table S3). In the acceptance evaluation, the consumers' reviews of the aromas of different oolong tea products were comprehensive and objective, with high scores from both the expert evaluation and consumer evaluation for the products of the suitable cultivars, reflecting the economic value and significance of the application of suitable oolong tea cultivars.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer
#### 4.3.1 Selective Forwarding Attacks
The proposed scheme consists of a collection of danger signals and the selection of an optimal danger threshold. During the screening stage, a support vector machine (SVM) screens suspected selective forwarding attacks through the nodes' remaining energy, forwarding rate, connection duration, transmission frequency, and transmission time. During the confirmation stage, the optimal danger threshold is set. Using this threshold, a novel suspected node is confirmed. The authors proved that the combination of the danger model and SVM renders detection against selective forwarding attacks more effective than those of previous studies. [PERSON] et al. [57] proposed a long short-term memory (LSTM) ML algorithm to detect SFA using training and detecting stages. Through LSTM, a node's forwarding behavior is trained, and the error is predicted. Based on this model, dynamic thresholds are set to identify misbehavior during the detecting stage. An extended neighbor voting method in [55] was proposed to differentiate between malicious and normal nodes. In addition to the voting system, one more similarity exists with [55,56]. The authors assumed SFA under time-variant harsh environments in WSNs. During the training stage, normal nodes' forwarding rate and variational mode decomposition method were used for training. Following this model, a rational threshold selection rule and neighbor voting method were used to identify the abnormality. [PERSON] et al. [58] proposed a novel detection scheme for SFA with a deep belief network (DBN) and density-based spatial clustering of applications with noise (DBSCAN) under harsh environments. The DBN is used to extract the features of the node, whereas the DBSCAN induces clustering to distinguish malicious nodes. A DBN is a probability-generating deep neural network that extracts data features through the underlying network quickly. DBSCAN is used to separate noise points from clusters more efficiently than other clustering methods. After identifying the behavior of the node, the normal node can be screened through DBSCAN. Unlike the previous work from the same research group, this research targets mobile and immobile harsh environments. The feasibility of combining DBSCAN with DBN was proven through simulations.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 3 Measurement Results
### Received Signals
#### 3.2.2 Non-Line-of-Sight Communication  
Two expeditions leaving the Princess Elisabeth station traveled in a southern direction. In this direction, mountains quickly interrupted LoS propagation. In the measurement performed at 868.1 MHz, packets were received up to 21.1 km distance. On the height profile displayed in Figure 13, a sharp mountain is clearly blocking the LoS path. Calculations based on [18] indicate that diffraction is possible. The diffraction loss caused by the mountain top equals 6.88 dB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Start Distance** & **End Distance** & **Packets Received** & **Packet Loss** \\
**(km)** & **(km)** & **Packets Received** & **\%** \\ \hline
0.5 & 5 & 219 & 0.00 \\
5 & 10 & 150 & 0.00 \\
10 & 15 & 62 & 0.00 \\
15 & 20 & 95 & 0.00 \\
20 & 25 & 101 & 0.00 \\
25 & 30 & 798 & 16.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Packet loss under LoS conditions.  
Figure 12: Packet loss observed at two positions separated by 500 m.  
On the return trip, exactly the same route was followed. Packets were again received, which demonstrates that the shape of the terrain is the key factor in this transmission. Tropospheric propagation can be excluded, as this mechanism would result in the reception of a more time-varying signal observed over a much larger area.  
During a different expedition, at a distance between 19.25 and 19.5 km, after a long silent period, suddenly about 200 packets were received, as shown in Figure 14. Figure 15 reveals that there is no LoS path between transmitter and receiver at that location.  
Figure 14: Packets received in NLoS conditions, probably via reflection.  
Figure 13: Scenario where NLoS propagation was detected. The location where the transmission took place is indicated on the map.  
The most likely scenario here is reflection. Finding the exact signal path is difficult, since there are multiple possible routes, as shown in Figure 15. These signal paths are composed of two specular paths, of which one (shown in Figure 16) matches the direction of the base station Yagi-Uda antenna. These observations indicate the possibility of setting up LoRa links without LoS via knife-edge diffraction over mountain ridges, or via reflections on higher mountains that dominate the landscape.
## 3 Processing Method
### Feature Space Construction  
The clutter-simulated radargram is co-registered with the original radargram to remove vertical and horizontal offsets. Due to the nature of radargram signals, translation, rotation, and scale-invariant image registration are essential. Therefore, we employed a fast Fourier transform (FFT)-based registration method, which utilizes frequency domain analysis to find matching signals [57]. The performance of the FFT-based registration method is demonstrated in Figure 5.  
The labelled discontinuity is prepared manually for approximately twenty tracks from different regions for the training components of the machine learning algorithm (see Figure 6) The characteristics of \"subsurface discontinuity\" do not vary significantly for different regions, as per our experiences. Therefore, our deep learning model does not employ region  
Figure 4: A noise filtering result in magnified radargram over Euripus Mons. Original radargram (**a**) and noise-filtered radargram after bilateral filtering (**b**). Results of background removal from original image (**c**) and background removal from bilateral filtered image (**d**).  
Figure 5: Original overlap between the radargram and clutter-gram (horizontal and vertical offset present) (**a**), overlap after registration of clutter-gram with radargram (**b**).  
vectors. The construction of relevant feature spaces is highly essential for the performance of the ML algorithm. Several features are generated and tested with approaches such as normalized difference, mean, Gabor filter and image entropy, etc. Finally, a three-channel feature space is constructed using the radargram, registered clutter-gram, and the normalized difference of the radargram and clutter-gram (refer to Figure 6). In order to enhance the robustness of the model and increase the number of training samples, an additional training data set was created using varied orientation and affine transforms on the original dataset. The augmentation was performed by rotating the images through a range of \(-5\) to \(+5\) degrees, mirror/left-right flip, resampling and image zooming. The overall training dataset consisted of 2160 images of size \(256\times 256\).
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 4 Algorithm Introduction and Simulation Analysis
### Algorithm Complexity Analysis  
In the GABPSO algorithm, the population size is \(U\), the number of iterations is \(I\), and the problem size is \(N\). For a single particle, the complexity of each operation is as follows:  
* Velocity update: each particle gets a new velocity based on (25), and the time complexity is \(O(1)\)
* Position update: each particle gets a new position based on (26), and the time complexity is \(O(N)\).
* Fitness calculation: each particle needs to be calculated on the basis of the decision matrix \(M\) to obtain the corresponding fitness, and the time complexity is \(O(N)\).
* Fitness evaluation: each particle is compared with the historical best particle, and the time complexity is \(O(1)\)
* Crossover and mutation: each particle performs a crossover and mutation operation with a certain probability, and the time complexity is \(O(1)\)  
Thus, for a single particle, the time complexity of one iteration is proportional to the problem size as \(O(N)\). Therefore, the time complexity of the algorithm is \(IUO(N)\)
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Study and Simulation of the Radio Links of a 4G System
#### 2.2.1 Cost 231 Hata and Okumura-Hata Models  
The model of Cost 231 Hata is used to calculate path loss in a UE system. The influence of different parameters on the Lx signal attenuation with the Cost231 Hata and Okumura-Hata model is given. We have given the variation of the distance between receiver and transmitter. The variation range of the distance separating the receiver and the transmitter is: [0.01 km, 10 km]. The other parameters are set as follows: h\({}_{\text{b}}\) = 80 m; h\({}_{\text{m}}\) = 2 m and F = 1000 MHz. The obtained results are presented in Figure 1:  
We note a proportional relationship between the separation distance and the attenuation (that the attenuation increases with the increase in the distance separating receiver and transmitter in the two media).  
The influence of the height of the eNB is presented in Figure 2. In this case, the range of variation of the height of the eNB is [20 m, 200 m]. The other parameters are set as follows: h\({}_{\text{m}}\) = 2 m; d = 2 km and F: 1000 MHz.  
We notice an anti-proportional relationship between the size of the eNB and the attenuation, i.e., if H\({}_{\text{b}}\) increases L\({}_{\text{x}}\) decreases.
## 3 Method
### Behind the Comparison: Usage of Spherical Harmonics as Structural Patterns  
Let us denote the partial reconstruction of function \(g\) for degree \(l\) as \(g_{l}\). The same denotation will be used for \(f\).  
Let us start with a toy example and assume that at degree \(l\) for function \(f\) has only one coefficient with order \(m\), say, \(l=3\) and \(m=2\); see Figure 1.  
The partial reconstruction \(f_{l}\) for degree \(l\) will be the spherical harmonic \(Y_{lm}\) multiplied by the corresponding coefficient \(f_{lm}\). Let us assume that this coefficient is equal to 1. Let us take a look at the visualization of a spherical harmonic \(Y_{lm}\). We can see that the number of extrema points on the harmonic's surface is not large. The reconstruction \(g_{l}\) will look geometrically (spatially) the same; hence, there is the same number of maxima for the original and rotated spherical harmonic. If the number of maxima is relatively small, we can find the mapping of the maxima of \(f_{l}\) to the maxima of \(g_{l}\) using an exhaustive search. We can preliminarily order these maxima by their values (length of vectors) and angles between different surface extrema points to speed up the process. This ordering would allow us to find the correct mapping faster with fewer combinations to be checked.  
If, in the toy example, the harmonic under consideration has a continuous rotation symmetry, like the spherical harmonic in order (3,0) (Figure 2, see a shape in the center of the last row), then the extrema and the saddle points of the harmonic surface could be sampled at the grid with the chosen grid step. The middle part (the \"equatorial\" line) will obtain limited points. Matching of respective extrema points at both objects could still be  
Figure 1: Spherical harmonic of order (3,2) with the extrema marked. The yellow areas correspond to the negative values of the function, and the blue ones correspond to the positive ones.  
performed using an exhaustive search. The accuracy of matching depends on the size of the grid step.  
Suppose several non-zero coefficients exist in the expansion for some degree \(l\) (Formula (4)). In that case, the number of surface extrema for each harmonic is limited. For the partial reconstruction (4), the number of extrema in the sum of weighted spherical harmonics participating will also be bounded. The same consideration is valid for the sum of all the degrees participating in the partial reconstruction (5).  
We will show that under some assumptions, the sum of all the partial reconstructions at all the shells mapped to the unit sphere will have the expected number of top-value extrema to be relatively small (on average, below thirty). We define top-value extrema as those that are among the largest by absolute value. A more formal definition will be given in Section 3.8.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 1 Introduction
[19] believed that the soil organic carbon content was closely related to LULC type, and conversion of forest land to cultivated land would cause the loss of soil organic carbon. In terms of land use transfer, [PERSON] et al. [20] found that the LULC change in the study area is dominated by cropland expansion, which gradually reduces the forest, grassland, and wetland areas. [PERSON] [21] showed that the LULC change in the Albertime rift region of northwest Rwanda was mainly due to the expansion of cultivated land driven by human activities in the area. Although these studies have evaluated the LULC situation of Rwanda from the macroscale to the microscale, research on the quantification of the factors driving LULC change in Rwanda and the future development trend of LULC is scarce. Therefore, it is particularly important to comprehensively understand the status of LULC transfer in Rwanda and its driving factors, and to forecast future use on this basis. Based on historical data of LULC in Rwanda, the transfer of LULC in different historical phases is analyzed and the factors driving LULC transfer in each phase are identified. This comprehensive understanding of the LULC driving factors is combined with demographic, topographic, climate, and locational factors to predict future LULC in Rwanda. The results of this study are expected to help enhance natural resource management and improve the living environment in Rwanda, thereby securing the health and prosperity of Rwandans in the future.
# Analysis of Lightweight Structure Mesh Topology of Geodesic Domes
## 5 Result from Numerical Analysis  
### Internal Forces  
Based on the results of internal forces obtained in the numerical program, the distribution of strut forces in the considered domes was developed.  
Figure 7 shows maps of axial forces in the dome struts modeled according to the first method.  
In the case of dividing the initial triangle of a regular octahedron according to the first method, the maximum internal compressive and tensile forces of the struts are similar in value. The struts, forming the first eight layers described at the nodes \(n_{w}\) < <2;8>, form tensioned rings; in the higher layers with \(n_{w}\) < <9;14>, alternating compression and tension of the struts occurs, depending on the place on the ring. In the upper parts with \(n_{w}\) > 14, only compressive forces occur. The struts resulting from the displacement of the side arms of the initial triangle along their entire length are compressed, obtaining maximum values of internal forces at the edges of the regular octahedron forming the dome.  
Maps of axial internal forces determined for the dome created according to the second method are shown in Figure 8.  
In the second dome analyzed, the maximum internal forces occur in the support zone. Most of the compressive forces are transferred by the struts generated on the straight line perpendicular to the base of the triangle forming the face of the regular octahedron, the struts generated on the straight lines perpendicular to the side walls of the triangle. Depending on their position relative to the base of the dome, they transfer compressive forces in the upper zone and tensile forces at the base, except for the ring forming the last layer of diagonal struts due to the transfer of compressive forces from struts perpendicular to the base of the starting triangle.
###### Abstract  
Governments and health authorities emphasize the importance of early detection of breast cancer, usually through mammography, to improve prognosis, increase therapeutic options and achieve optimum outcomes. Despite technological advances and the advent of full-field digital mammography (FFDM), diagnosis of breast abnormalities on mammographic images remains a challenge due to qualitative variations in different tissue types and densities. Highly accurate computer-aided diagnosis (CADx) systems could assist in the differentiation between normal and abnormal tissue and the classification of abnormal tissue as benign or malignant. In this paper, classical, advanced fuzzy sets and fusion techniques for image enhancement were combined with three different thresholding methods (Global, Otsu and type-2 fuzzy sets threshold) and three different classifying techniques (K-means, FCM and ANFIS) for the classification of breast masses on FFDM. The aim of this paper is to identify the performance of the advanced fuzzy sets, fuzzy sets type-2 segmentation, decisions based on K-means and FCM, and the ANFIS classifier. Sixty-three combinations were evaluated on ninety-seven digital mammographic masses (sixty-five benign and thirty-two malignant). The performance of the sixty-three combinations was evaluated by estimating the accuracy, the F1 score, and the area under the curve (AUC). LH-XWW enhancement method with Otsu thresholding and FCM classifier outperformed all other combinations with an accuracy of 95.17%, F1 score of 89.42% and AUC of 0.91. This algorithm seems to offer a promising CADx system for breast cancer diagnosis on FFDM.  
hybrid intelligent system; Fuzzy C-means; adaptive neuro-fuzzy inference system; K-means; type-2 fuzzy sets; image segmentation; mammographic mass decision tree classification; advanced fuzzy sets; Otsu's thresholding; pattern recognition; full-field digital mammography
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
## 5 Simulation Results
### Simulation 3  
To further verify the aforementioned anti-disturbance performance of the AFSMFTC method, unknown disturbances consisting of system parameter perturbations and external disturbances are introduced at 2 s after the beginning of a normal CBA landing. The unknown disturbances are described as  
\[d(t,x_{1},x_{2})=\begin{bmatrix}0.3(p+\phi)+0.05 sint\\ 0.25(q+\theta)+0.2 sint\\ 0.2(r+\beta)+0.1 sint\end{bmatrix} \tag{45}\]  
The simulation results are shown in Figures 10 and 11.  
Figure 10 shows that the conventional PID method cannot adjust the control parameters in real time after the introduction of disturbance signals; rather, it progressively generates divergent phenomena that eventually result in the CBA being uncontrollable. By contrast, the proposed AFSMFTC method approaches the boundary of unknown disturbances through an adaptive fuzzy sliding-mode controller and ensures that the CBA still has good path-tracking performance under various disturbances.  
Figure 11 shows that introducing unknown disturbances affects different flight state variables of the CBA from time to time, but different attitude angles and velocities can still be maintained in a small range of fluctuation, indicating good anti-disturbance capability of the AFSMFTC method.  
Figure 9: Response curves of flight variables of CBA in normal state: (**a**) Angle of attack; (**b**) Pitch angle; (**c**) Side-slip angle; (**d**) Roll angle; (**e**) Velocity.