esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The species of truffle (_Tuber_ [PERSON]) occur mostly in the Northern Hemisphere, and they are distributed across Asia, Europe, North Africa and North America [1]. They are fungi belonging to the truffle genus (Ascomycota), which form below-ground ascocarps or fruiting bodies (hypogeous fungi) [2]. Among edible fungi, truffle may represent the best known and most expensive ones in commercial value due to their organoleptic qualities. It is well known that _T. melanosporum_ Vittad., the Perigord black truffle from France, and _T. magnatum_ Pico., the Piedmont white truffle from Italy, are considered as the most valuable species.  
Truffles are abundant in some regions of China, especially in the southwest. However, it was not until the 1980s that research on truffles in China started [3]. During the past three decades, more truffle species were discovered in China [4], and this indicated that edible truffle diversity is much richer in natural resources than expected. In China, truffles can adapt to a wide range of soil conditions, and they are mainly associated with forest woods, like _Pinus. yumanensis_ and _P. armandiu, etc._[5]. In these woods, most truffle species are harvested from November to the following March.  
About 200 volatiles in total have been reported in the literature for the entire truffle species investigated from different areas of the world. However, there was a clear distinction between Chinese and European truffle [6; 7]. The common methods for truffle volatile extraction included headspace analysis, dynamic headspace, purge-and-trap and stir bar sorptive extraction (SBSE) [6; 8; 9] and headspace solid-phase microextraction (HS-SPME) [10; 11], coupled with gas chromatography/mass spectrometry (GC/MS), comprehensive two-dimensional gas chromatography/flame ionization detector/mass spectrometry (GC \(\times\) GC-(FID/MS)) [12] and an electronic nose [13] as a method of discrimination, as well.  
In this research, a comparison of volatile compounds between the two kinds of Chinese truffle, black and white ones, had been established in terms of qualitative and semi-quantitative differences on volatile compounds. The method of direct solvent extraction/solvent-assisted flavor evaporation (DSE-SAFE) coupled with comprehensive two-dimensional gas chromatography/high resolution time-of-flight mass spectrometry (GC \(\times\) GC/HR-TOF/MS) was applied to identify aroma compounds. Meanwhile, the study also characterized the overall aroma profiles of samples by an electronic nose.
## 4 Enabling Technologies for FSO Communication
### Adaptive Optics
#### 4.9.1 Principles of Adaptive Optics  
AO systems are designed to correct phase distortions in the optical wavefront caused by AT. These distortions, resulting from variations in air temperature and pressure, can lead to beam spreading, scintillation, and other forms of signal degradation. The application of AO in turbulent conditions mitigates optical wavefront distortion, resulting in notable performance enhancements. Initially developed for astronomy and medical science, AO have gained prominence in imaging, FSO communications, and various military uses. In a turbulent medium, the AO correction process acts as a multiplicative spatial filter on the power spectrum. As defined in [380], AT causes specific aberrations and wavefront distortions, which are statistically characterized. The use of AO for correction is further analyzed through the Zernike polynomials representation in [8; 381]. Studies on AO correction with a deformable mirror (DM) as a spatial filter and the associated fitting errors are documented in [382; 383]. Research on the outer scale effect on wavefront distortion reveals significant impacts on AO systems [384]. Regarding communication systems, investigations have explored the decrease in the scintillation index for Gaussian beams in non-Kolmogorov turbulent atmospheres and the BER of IM systems with AO [385; 386]. These findingscollectively demonstrate the efficacy of AO in mitigating turbulence effects. Figure 13 illustrates a simplified AO system from the perspective of FSO. The key principles of AO include the following [277; 387]:  
1. Wavefront Sensing: * Shack-Hartmann Wavefront Sensor: One of the most common devices used in AO systems is the Shack-Hartmann wavefront sensor. It comprises a series of lenslets that focus portions of the incoming wavefront onto a detector. The displacements of the focal spots on the detector are used to reconstruct the wavefront distortions [387; 388; 389]. * Other Sensors: There are various other wavefront sensors, including curvature sensors and pyramid sensors, each with specific advantages depending on the application [390].
2. Wavefront Correction: * Deformable Mirrors: These mirrors have surfaces that can be precisely shaped using actuators to counteract the detected wavefront distortions. The most commonly used DMs are based on piezoelectric or micro-electro-mechanical systems (MEMS) technology [391; 392; 393]. * Spatial Light Modulators (SLMs): These devices modulate the phase of the light passing through them and can also be used for wavefront correction [394; 395; 396; 397; 398; 399].
3. Real-Time Feedback: * The AO system continuously monitors the wavefront distortions and adjusts the DM or SLM in real time to compensate for these distortions. This real-time feedback loop is crucial for maintaining high signal quality [277; 395; 397].
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 4 Numerical Results and Discussion
### System Evaluation  
In this section, we evaluate the performance and robustness of our proposed routing scheme. First, we compare our proposed EFRP with the Greedy approach which selects the next hop based on the shortest distance. Then, we validate our proposed protocol by comparing it with one of the most known routing protocols, i.e., DSR routing protocol.
## 2 Methodology
### Integrating ANFIS with GA Model  
In ANFIS, every input usually includes several membership functions (MFs) and every MF becomes a maximum somewhere. This process needs to be performed with the experience that the changes in the position of belonging functions can change the prediction accuracy. In order to optimize the position of MFs and increase the ANFIS accuracy through training process, GA is used. The hybrid model is used to determine more accuracy results for nonlinear problems and to improve the prediction of tunneling performance. The data points can be categorized into two parts, the training set and the testing set. The percentage of the training dataset can be used to create the ANFIS structure model, whereas the testing set can be used to evaluate the model's prediction. In the ANFIS model, training began to use the arranged dataset. The training dataset process permits the system to regulate the defined parameters as input or output in the system. The training set ends when the specified conditions to terminate the program are accepted. The premise and consequent parameters of ANFIS model are updated by the genetic algorithm. The premise parameters in the fuzzification layer denote (\(\sigma,\
u,b\)) in Equation (3) that belong to Gauss membership functions and the number of these parameters is equal to the sum of the parameters in all MFs. Consequent parameters are the ones that defined in the defuzzification layer (\(p\), \(q\), \(r\)) in Equation (6). For training ANFIS with (M) membership functions and (\(n\)) inputs, there are \(M^{n}\) fuzzy rules based on Jang [44]. Figure 2 demonstrates the procedures of the hybrid ANFIS-GA. The multi-objective fitness function has been selected as the evaluation criterion of the training result. The dataflow is presented as:  
1. The first step of the dataflow is to prepare the input parameters (cutter head torque, rotational speed of screw rate, cutter head rotation speed). Then, the corresponding output is set (advance rate).
2. Initializing the Genetic Algorithm. In this step, the population initialization and GA operators are configured.
3. In ANFIS configuration, the training and testing data are defined. The 80 percent of the input database is used for the model's training and the rest twenty percent is utilized for the model validation.
4. Define the number of membership functions and rules. The algorithm applies the combination of least-squares and the backpropagation method to train the fuzzy inference system and emulate the established dataset.
5. Evaluate the objective function. If the optimum criteria have not been achieved, the selection, crossover, and mutation are applied to define the new population that will be evaluated. If the criteria have been met, the solution is obtained.
# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
## 1 Introduction
Many existing works apply optical flow [12] and Local Binary Patterns from Three Orthogonal Panels (LBP-TOP) [8] algorithm to extract feature form dynamic micro-expression video sequences. The optical flow method reflects the close correlation between the frames by calculating the two adjacent frames. However, the changes between adjacent frames in micro-expressions are very weak and hence the algorithm does not reflect the changes of the facial  
Figure 1: One necessary morphological patch (NMP) of disgust, micro-expression sequence. active patches. The LBP-TOP algorithm analyzes image texture from temporal and spatial pattern. Texture is a feature that shows the spatial distribution property of pixels, and can convey the necessary information of micro-expression. In addition, it can also display the local structural information of facial images. Compared to ordinary expressions, micro-expressions call for less muscle motion to convey emotions and to evaluate the current emotional state we can we identify only some NMPs. To reduce the dimensionality and improve the recognition efficiency, we use the Entropy-Weight method to screen the NMPs, which are essential for micro-expression recognition from 18 active patches. The concept of entropy is first introduced into information theory by [PERSON], which describes the size of the average information amount of events. The entropy weight has been widely used in engineering, social and economic fields, whose basic idea is to determine objective weights according to the variability of indexes. In general, the smaller information entropy of an index indicates the greater the variability of the index value. Therefore, this index affects more in terms of comprehensive evaluation, and its weight will be greater. In order to assess the contribution of 18 active patches to micro-expression recognition, we use Entropy-Weight method to evaluate its weights. Entropy-Weight method can not only filter out NMPs from active patches but weight these patches, thus increasing the discriminative ability of our algorithm. Finally, the multi-class SVM classifier is used to identify these NMPs, and the recognition rate is obtained from CASME II and SMIC databases. The rest of this paper is organized as follows. The next section describes the related work on facial landmark detector and NMP selection, feature extraction, and weighting these necessary regions. Section 3 illustrates the particulars of the databases and discusses the experimental results in detail. Finally, Section 4 concludes the paper.
## 3 Design Methodology
### Nested Optimization with a Variable Number of Design Parameters  
Although conventional global-optimization algorithms proved to be useful for solving complex problems, they suffer from an unacceptable numerical cost, associated with the tremendous number of model evaluations required to find the optimal designs. On the other hand, deterministic algorithms (such as gradient or pattern search methods [36; 50]) are often characterized by a lower computational cost yet limited capability in terms of escaping from local minima. In this work, the problem is mitigated by performing a series of optimizations in a nested loop. The latter gradually increases the number of design variables, first to identify and then to exploit the region of the search space of interest. The analogy to this approach, from the perspective of the design objective space, is observation of the landscape from a high altitude where the amount of visible details is limited. With each iteration of the nested loop (that is associated with an increasing the number of parameters), the \"altitude\" decreases, which allows the algorithm to \"identify\" more details of the landscape and improve the design response.  
Let \(\mathbf{d}=[d_{1}\ldots\ d_{n}\ldots\ d_{N}]^{T}\), \(d_{n}=1,\ldots,L\), \(n=1,\ldots,N\) represent the set defining the number of spline parameters (positive integer values) used to represent the ground plane and radiator, respectively. In each step, the final \(d_{n}\) dimensional design \(\mathbf{x}_{dn}\), obtained in the \(n\)th nested iteration, is used as a starting point for the \(n+1\) step. Assuming that the vector \(\mathbf{d}\) is defined, \(n\) is set, and the initial design \(\mathbf{x}_{dn}^{*}\) is available, the nested optimization can be summarized as follows:  
1. Set \(\mathbf{x}_{dn+1}(0)\leftarrow\textit{sp}(\mathbf{x}_{dn}^{*}\mathbf{x}_{dn+1})\) (see Section 2);
2. Find design \(\mathbf{x}_{dn+1}\) using \(\mathbf{x}_{dn+1}(0)\) as a starting point for optimization;
3. If \(n=N\), END; otherwise, set \(n=n+1\) and go to 1.  
It should be noted that the size \(N\) of vector \(\mathbf{d}\) substantially affects the algorithm cost, as the optimization process is reset at each iteration \(n=1,\ldots,N\). On the other hand, the number of iterations and step size between \(d_{n}\) and \(d_{n+1}\) can be adjusted so as to mitigate the risk of getting stuck in a poor local optimum while managing the optimization cost at the acceptable level. Conceptual visualization of the objective function (a so-called feature) and design spaces for the structure, represented using the increasing number of parameters, is shown in Figure 2.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 3 Adaptive Bandwidth Tracking Strategy
### Theoretical Optimum LNBW  
Computing the partial derivative of \(\sigma_{PLL}\) to \(B_{L}\), we get \(\dfrac{\partial\sigma_{PLL}}{\partial B_{L}}\). When \(\dfrac{\partial\sigma_{PLL}}{\partial B_{L}}=0\), the value of \(B_{L}\) that make the \(\sigma_{PLL}\) minimal can be determined:  
\[B_{L\_optimal}=\sqrt[7]{\dfrac{173.8^{2}\pi^{2}J_{LOS}^{2}f_{L1}^{2}}{90^{2}c^ {2}}\cdot\dfrac{2T_{coth}\left(C/N_{0}\right)^{2}}{2T_{coth}\cdot C/N_{0}+1}} \tag{14}\]  
In practical SSV flight missions, both the C/N\({}_{0}\) and \(J_{LOS}\) are changing continuously. To achieve an optimal \(B_{L}\), a self-adaptive method should be utilized, and the adaptive KF-based algorithm is exactly such a method.
# EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing
## 4 Experimental Results
### Entity Descriptor and EDET in Industrial Application
#### 4.5.2 Application of EDET on the Fault Analysis  
In order to monitor the production line, a month of data were obtained before the fault parameters as a dataset, and each fault was divided into three stages: \"fault\", \"the fault will happen\", \"normal\". After marking the dataset, the fault analysis based on the deep learning model was transformed into a graph classification task based on a knowledge graph.  
Even if the same equipment is available between different production lines, their functions and monitoring parameters in the manufacturing process of different production lines may be different, so separate modeling and monitoring should be conducted for each production line. In the implementation of the monitoring of each production line, the operation process is fixed, namely, the following monitoring of different production lines is implemented by the system automation: obtain production line equipment data, obtain the attribute information of equipment models, components, and parameters using the entity node vector representation, input the whole production line serialized vector into the EDET model of native transformer architecture, and obtain the classification results through MLP classification. After one month of historical data training, the model is put online and, when a certain amount of real-time monitoring results and feedback information are accumulated after the launch, the new data are collected to continue the EDET model so as to achieve the effect of lasting training, continuous optimization, and man-machine collaboration.  
In order to achieve the purpose of rapid response of the system, the system also needs to build an offline knowledge base. As shown in Figure 12, the parameters of the Entity Description substructure on each scene are stored separately as a model.  
When the system listens to the production line, the monitoring data of the same device are also two different entities with independence and differentiation at different times, while the Entity Descriptor can retain the independent separability of the entity. This means that when the scene needs to realize the function of rapid response, we only need to input graph data to the Entity Descriptor stored in an offline knowledge base to generate a representation vector, and then the similarity between different scenes can be calculated by their representation. However, it should be noted that the parameters in the Entity Descriptor are also constantly updated under the action of continuous training. After each update, the parameters stored in the offline knowledge base also need to be changed accordingly.  
Figure 11: Industrial knowledge graph constructed by attributes selection conditions. Different colors represent different types of entities or attributes.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### Influence of Time-Delay Estimation Error on Sound Source Localization Performance
As shown in Table 5, the error rate of the distance from the sound source to the center of the microphone array is approximately 3%, the elevation angle error rate is approximately 1.5%, and the horizontal angle error rate is approximately 2.5%. Due to the limitations of the experimental site, the maximum array element spacing is 1 m. Following the analysis of Section 4.3, the sound source localization error will be lower and more appropriate for displaying the advantages of the fusion algorithm when the array element spacing is increased appropriately. In addition, taking the third experiment as an example, the performance of the algorithm is compared with references [24; 26], and the results are shown in Table 6.  
In Table 6, in terms of coordinate representation, [PERSON] and [PERSON] [24] performed poorly; although [PERSON] et al. [26] performed real-time 3D sound source mapping, they still did not know the location of the sound source; the fusion algorithm does this better. In terms of precision compensation, [PERSON] and [PERSON] [24] only stayed at the level of precision analysis, and there is no measure to improve the accuracy of sound source localization; [PERSON] et al. [26] provided a good initial guess for online optimization strategy through joint optimization. However, this measure is based on the sound source azimuth estimation algorithm. Therefore, when the sound source is at an extreme angle, it will still have a negative impact on the research of [PERSON] et al. [26]. In response to this impact, the accuracy compensation for sound source localization can be achieved using Equation (8). Although all three have analyzed the performance of the sound source localization algorithm, the experimental results show obvious differences among them. Since [PERSON] et al. [26] cannot determine the specific location of the sound source, the ranging and direction-finding error cannot be further given. When the acquisition card rate reaches one megabyte, the distance error rate measured by [PERSON] and [PERSON] [24] only reaches the minimum value of 3.75%; meanwhile, no measurement data is given in the measurement of angle. However, the distance and angle error rates measured by the fusion algorithm are 1.08% and 2.19% maximum, respectively. These error rates are still within the acceptable range when the elevation angle reaches an extreme angle of about 75\({}^{\circ}\). In summary, the sound source localization fusion algorithm of the three-plane five-element microphone array has higher precision, better stability, and an overall good performance in indoor experiments. By appropriately increasing the array element spacing in the three-plane array, compared with a single-plane array, one can measure the sound source position more accurately and stably.
# Ambient LED Light Noise Reduction Using Adaptive Differential Equalization in Li-Fi Wireless Link
## 2 Ambient Light Noise Model and Adaptive Differential Equalization
### Interference and Shot Noise Model of Fluorescent Lamp and Dimmable LED
[PERSON] and [PERSON] discovered that the second and third terms on the right-hand side of Equation (6), the interference noise generated by other types of LED lights, can be expressed using the Fourier series as Equations (7) and (8) [19; 20]:  
\[N_{1}(t)=\sqrt{2\left[a_{0}+\sum_{i=1}^{n1} \begin{array}{c}a_{i}\cos(2\pi(1 00i-50)t)\\ +b_{i}cos(2\pi(100i)t)\end{array} \right]} \tag{7}\]  
\[N_{2}(t)=\sqrt{2\left[c_{0}+\sum_{i=1}^{n2} \begin{array}{c}c_{i}\cos(2\pi(1 00i-50)t)\\ +e_{i}cos(2\pi(100i)t)\end{array} \right]} \tag{8}\]  
where \(a_{0}\) and \(c_{0}\) are the average DC value, \(a_{i}\), \(b_{i}\), \(c_{i}\), and \(e_{i}\) are the amplitudes of the 50 Hz and 100 Hz harmonics, respectively. \(n1\) and \(n2\) are the numbers of the harmonics that can affect the SNR of the data signal. Here \(a_{0}\) and \(c_{0}\) can be simply removed using a DC block as an RF device. However, it is not possible to remove \(a_{i}\) and \(b_{i}\) using a high-pass filter because the 50 Hz and 100 Hz harmonic distortions corresponding to \(a_{i}\) and \(b_{i}\) overlap the bandwidth of the NRZ-OOK signal. In addition, since shot noise generated by NRZ-OOK and interference noise also is generated over the entire band, it plays a role in reducing the signal-to-noise ratio of the NRZ-OOK signal. Hence, we propose the ADE technique to reduce the interference noise showing as harmonic distortion as well as two kinds of shot noise. As the distance between two light sources (fluorescent lamp and dimmable LED) generating interference noise and Rx increases, the intensity of interference noise increases because the optical power injected into Rx increases. On the other hand, as the distance between the white LED modulated by the NRZ-OOK signal and Rx gets closer the optical signal-to-noise ratio increases. In general, the optical power received in an optical wireless transmission is inversely proportional to the square of the distance. Therefore, it is desirable to implement a wireless environment where the distance between the white LED and Rx is small, and the distance to other light sources is increased if possible.
## 2 Accurate Identification Method of Fractured Rock Structure
### Concept and Determination Method of Occlusion Degree of Fractured Rock Masses
#### 2.3.4 Weak Structural Surface Characteristics  
The deformation and damage to the fractured rock mass are strongly controlled by the structural surface. Therefore, the characteristics of the weak structural surface are also the key influencing factors of the occlusion degree of the fractured rock body. The three main weak structural surfaces in the underground cavern group of the Liyang Pumped Storage Power Station are hard joints without filling, interlayer dislocation surfaces with mud film, and weak structural surfaces with thick mud. The mechanical properties of these three types of weak structural surfaces are weakened in order.