esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical System Construction
### Application of Grounded Theory in Data Classification and Structure Construction
#### 3.2.5 Confirmation of the Theoretical Model  
Based on the in-depth analysis and integration of the seven coding dimensions, this study systematically classifies the core categories into two types of cultural genes: dominant and recessive. The classification criteria are not only based on the frequency and manifestation of the dimensions but also take into account their actual role in the visual theme structure and cultural generation path.  
In the dominant gene part, four gene groups with strong visual recognition and core expression function that have appeared stably in the phoenix pattern for a long time were identified: Configuration element gene group (FG01): Emphasizing the morphological structure and composition pattern of the phoenix pattern element, it is the basic unit for constructing the \"phoenix pattern visual skeleton\"; Colour style gene group (FG02): Focusing on the construction mechanism of colour tonality, psychological perception, and symbolic style in the phoenix pattern, it plays a key role in style recognition and perceptual arousal; Media craft gene group (FG03): Focusing on the presentation of the phoenix pattern in the material and technical context, it reveals the structural adaptation logic between the phoenix pattern morphology and craft characteristics; Application context gene group (FG04): Reflecting the reconstruction of the phoenix pattern in different social contexts, it includes the visual translation path of various application scenarios, such as clothing, utensils, architecture, and graphics.  
In contrast, although the recessive genes are relatively weak in frequency performance, they play a key role in semantic potential and dynamic evolution. This study summarizes two types of core recessive gene groups: the semantic symbol gene group (FG05) covers the cultural motifs, image structures, and theme scenes behind the phoenix pattern and is the semantic source of the \"phoenix shape meaning\"; the variation and regeneration gene group (FG06) corresponds to the structural variation and semantic reorganization mechanism of the phoenix pattern elements in a cross-media and cross-era environment, providing theoretical support and a modelling basis for subsequent AI generative design and semantic mapping.  
Finally, based on the above six core gene groups, the research team systematically constructed a \"wind culture multidimensional gene pedigree model\" (Figure 7). The model effectively connects the phoenix pattern's visual skeleton and semantic core through a \"dominant-recessive\" double-layer structure. It has structured and hierarchical cultural expression capabilities and labelled coding logic for AI generation applications. The proposed model provides a basis for constructing the phoenix pattern database, element combination, and semantic retrieval but will also become an important theoretical framework to support cultural style migration, style bionics, and intelligent generation processes.
## 2 Numerical Calculation Method
### Euler Gas-Liquid Two-Phase Model
#### 2.1.1 Solve Assumptions  
[PERSON] et al. [31] employed both single-phase and two-phase methodologies to simulate nanofluids within microtubules. The findings of the investigation indicated that the two-phase model exhibited lower errors in comparison to the experimental data. Consequently, in numerical simulations of granular flows, the selection of a multiphase model tailored to the research subject is more precise than a single-phase alternative. Undericing weather conditions, the liquid water content (LWC) of water drops is generally only \(10^{-6}\) orders of magnitude [32], so the influence of the liquid phase on the air phase can be ignored when using Eulerian multiphase flow model to compute the flow field. Therefore, the airflow field can be solved first before the water-drop impact is calculated [29]. Wing ic involves phase change flow and heat transfer [33], the sizes of water drops in the air are randomly distributed, and its shape is also affected by the surrounding aerodynamic forces. Water drops may also interact with each other. Therefore, when using the Eulerian multiphase flow model to calculate the impact characteristics of water drops, the following assumptions are made [34]:  
1. The liquid water drop is spherical without transfiguration or fracture;
2. No water particle collision, agglomeration, or splash;
3. The particle phase momentum equation does not consider the viscosity term and pressure term;
4. There is no heat or mass transmission between water drop and gas;
5. The effect of turbulence on water drop can be left out;
6. The force exerted on the water drop is resistance and gravity, and all unstable forces can be ignored;
7. The resistance exerted on the water drop is stable.
## 8 Conclusions  
The interfaces of diffusion-bonded materials often include regions with mechanical properties that differ markedly from those of either parent material. The widths of these interfacial regions are often only a few micrometres in width, which can prevent their mechanical properties from being obtained by conventional mechanical tests.  
The high spatial resolution of nanoindentation enables the hardness and elastic modulus to be mapped across diffusion-bonded interfaces. Depending on the materials being bonded, the interfacial region may consist of a solid solution, which is formed as a result of interdiffusion across the interface, or intermetallic compounds. Of these, the latter often exhibit high hardness, although they can also be brittle; as a result, their presence can be detrimental to the integrity of the bond, often resulting in poor strengths when tested, for example, in tension or shear.  
A number of studies have shown that diffusion-bonded joints having the highest strengths are those with solid solutions formed at the interface, and intermetallic phases are absent. EPMA can reveal the extent of interdiffusion, which can enhance the bond via solid solution strengthening of the parent metals adjacent to the interface. Such interfaces would be expected to exhibit a continuous gradient in hardness rather than abrupt increases that are observed when intermetallic phases are present.  
When used in conjunction with elemental mapping of the interface, nanoindentation can provide information about the state of the bond, in particular the presence of any phases that could be disadvantageous to maintaining an adequate bond. For example, the presence of a solid solution is regarded as more desirable than intermetallic compounds, which often fail at lower loads. Such an approach offers a more efficacious means of assessing the strength of the bond than the more conventional tensile tests of bonded specimens which can be difficult, both in test specimen manufacture as well as in the carrying out of the tests. However, it can only be inferred that a good bond strength may be achieved.  
Interfacial indentation offers the potential for measuring the fracture toughness of the interface and thereby providing a measure of the bond strength. However, further work is needed in order assess this technique in more detail.  
This research received no external funding.  
The author is an employee of AWE. The author declares that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 2 Mechanism of High Sensitivity in Biomolecule Detection Based on a Single-Electron Transistor
The corresponding \(g_{m}\)-\(V_{g}\) characteristics showed that maximum \(g_{m}\) values appear at the \(V_{g}\) values near the left-side half maximum of each \(I_{d}\) peak in the \(I_{d}\)-\(V_{g}\) characteristics, and that the maximum \(g_{m}\) indeed increases with increasing \(V_{g}\) as anticipated above [22]. Finally, it should be noted that if the size of the Coulomb island can be smaller than that in the above-mentioned SET sensor [22], \(C_{g}\), \(C_{s}\), \(C_{d}\), \(C_{sub}\), and \(C_{\Sigma}\), are smaller in Equations (1) and (2). This leads to a larger \(g_{m}\). Furthermore, an extremely small island size leads to the SET operation in the quantum regime [29; 33] from the classical regime. Therefore, the maximum \(g_{m}\) of the Coulomb  
Figure 1:2 (a) Analytical drain current (\(I_{d}\))-gate voltage (\(V_{g}\)) characteristics of a single-electron transistor (SET) (solid curve). The inset shows the equivalent circuit used for the calculation. The broken curve is the analytical \(I_{d}\)-\(V_{g}\) characteristics when only the nanowire barrier region exists in the channel; (b) Analytical transconductance (\(g_{m}\))-\(V_{g}\) characteristics of an SET (solid curve). The broken curve is the analytical \(g_{m}\)-\(V_{g}\) characteristics when only the nanowire barrier region exists in the channel. Reproduced with permission from [22], Copyright 2013, AIP Publishing LLC. oscillations of the SET can be much larger than that of the corresponding FET with the same nanowire width at the same \(V_{d}\), even under the constant current through the tunnel junction with increasing \(V_{g}\).
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 1 Introduction  
Object detection plays an indispensable role in advanced visual tasks such as target tracking, image description, and scene understanding [1]. It is widely used in fields like intelligent security [2; 3], automatic defect detection [4; 5], autonomous driving [6; 7], and remote sensing image analysis [8; 9]. The YOLO (You Only Look Once) family [10; 11; 12; 13; 14; 15; 16; 17; 18; 19] of models, using end-to-end detection, directly output prediction results, and due to the excellent detection rate, they are a popular framework for industrial applications.  
While YOLO performs well in general-purpose object detection tasks, there are still some challenges in dense small object detection. Compared with conventional-sized objects, small objects have the disadvantages of fewer pixels, a smaller coverage area, and difficulty in feature information extraction. Small object feature extraction is easily affected by environmental factors in the actual scene; for example, in strong light, the textures of small objects are easily overexposed, affecting the extraction of their features. Scale variations and dense connections can also make the detection of small objects more difficult [20]. These models typically require larger parameters and still fall short of achieving high accuracy, often resulting in missed detections and false positives. Moreover, the feature extraction capabilities of these models are not sufficiently robust to handle the complexities of small objects in diverse and cluttered backgrounds.  
To address these limitations, we propose an improved YOLOv7 architecture tailored for dense small object detection. We have upgraded our model in several ways: (1) We have added a new detection layer to improve the recognition of small objects by focusing on finer details. (2) We have removed the detection layer for large target detection to speed up processing and make the model more effective. (3) We have integrated advanced feature fusion techniques to better capture features at different scales. (4) We have replaced olddownsampling techniques to preserve more details and enhance accuracy in detecting small objects. (5) We have optimized the number of network channels to further enhance the capability of small target detection. Comprehensive evaluations on the VisDrone2019 [21] data set highlight the superiority of this method compared to mainstream algorithms.  
The remainder of this paper is organized as follows. Section 2 reviews related work in the field of small object detection. Section 3 details the proposed improvements to the YOLOv7 architecture. Section 4 presents the experimental setup and analysis of results. Finally, Section 5 concludes the paper.
# Sc/SiC/Al Multilayer Optimization for Li K Spectroscopy
## 3 Results and Discussion
The multilayer periods range from 17 to 29 nm. As one can see, the Sc/SiC/Al multilayer has a calculated reflectivity of 35.4% with a bandwidth of 3.9 eV. The Be/Si/Al multilayer [11; 16] has a reflectivity of 31.8% with a bandwidth of 3.5 eV; its measured reflectance is 14% lower than the value obtained from the calculations. In the case of the Co/Mg multilayer [18; 28], the measured reflectance is reported to be 42.6%, which is 14% less than the calculated value, with a bandwidth of 8 eV. The reflectivity drop for Sc/SiC/Al is expected to be small based on the measurements at near-normal incidence, where the drop did not exceed 4%. This suggests that this system has smoother interfaces and less interdiffusion compared to the other presented multilayers. Thus, such a Sc/SiC/Al periodic multilayer can be introduced into the spectrometers equipping commercial SEMs and EPMAs. Following the Bragg law, n\(\lambda\) = 2d sin(\(\theta\)), where \(\lambda\) is the wavelength of the radiation, d is the period of the stack, \(\theta\) is the glancing angle of the incident beam on the sample surface, and n is the diffraction order, each emitted wavelength will be dispersed in one direction of the goniometer, corresponding, for the Li K emission, to the middle of the accessible angular range of EPMA. By scanning the angles, the various wavelengths will be sequentially detected and their various intensities collected, enabling the determination of the Li K emission band.
## 4 Experimental Results and Discussion
### Experiment 3  
In the third experiment, we proposed the ANP as the underlying prioritization method to prioritize SPs, resources, and tasks. The experiment was conducted on 8 SPs, 12 resources, and 16 tasks. Table 8 depicts the weights and ranks after conducting the prioritization process for the SPs' preferences.  
The calculated weights of the four prioritized criteria based on customers' preferences were as follows: reputation, 0.51%; trust, 0.32%; performance, 0.12%; cost, 0.05%. Table 9 shows the final weights and prioritization of the 16 tasks.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tendency** & **T** & **SP/Resource** \\ \hline \multirow{4}{*}{SP-Tendency} & 6 & SP1 \\  & 5 & SP1 \\  & 1 & SP1 \\  & 3 & SP3 \\  & 12 & SP3 \\ \hline \multirow{4}{*}{R-Tendency} & 9 & R5 \\  & 11 & R4 \\  & 2 & R5 \\  & 7 & R6 \\  & 10 & R3 \\  & 8 & R8 \\  & 4 & R3 \\ \hline \end{tabular}
\end{table}
Table 7: Categorization and assignment of tasks (Experiment 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**SP** & **Weight** & **Rank** & **R** & **Weight** & **Rank** \\ \hline
1 & 0.23 & & 1 & 0.16 & 1 \\  & & 1 & 2 & 0.08 & 7 \\ \hline
2 & 0.05 & 2 & 3 & 0.12 & 4 \\
3 & 0.20 & & 4 & 0.02 & 11 \\  & 2 & 5 & 0.11 & 5 \\
4 & 0.17 & 3 & 6 & 0.06 & 10 \\
5 & 0.11 & 5 & 7 & 0.14 & 2 \\  & & 8 & 0.00 & 12 \\ \hline
6 & 0.06 & & 9 & 0.13 & 3 \\  & & 7 & 10 & 0.06 & 9 \\ \hline
7 & 0.06 & 6 & 11 & 0.07 & 8 \\ \hline
8 & 0.12 & 4 & 12 & 0.10 & 6 \\ \hline \end{tabular}
\end{table}
Table 8: Preference-based ranking of SPs and their resources (Experiment 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**SP** & **Weight** & **Rank** \\ \hline
1 & 0.08 & 5 \\
2 & 0.12 & 2 \\ \hline
3 & 0.12 & 1 \\
4 & 0.03 & 14 \\
5 & 0.04 & 11 \\ \hline \end{tabular}
\end{table}
Table 9: Preference-based ranking of tasks (Experiment 3).  
The upper threshold in this experiment was 0.83 and the lower threshold was 0.12, which, as expected, allowed more tasks to be assigned to an SP regardless of the characteristics of its resources. The results shown in Table 10 illustrate the final assignment of all 16 tasks after applying Equations (1) and (2) to the prioritized SPs and resources.
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 7 Which Are the Medicinal Plants of Saudi Arabia That Might Demonstrate Anti-Mutagenesis?
### Echium arabicum  
The genus _Echium_ belongs to the family Boraginaceae. Several species of the family are used traditionally for medicinal purposes. _Echium arabicum_ ([PERSON], 1974) is a woody shrub, an annual biennial or perennial flowering plant. In several places, the plant is used as an ornamental plant and in gardens. Since the plant is hardy, it adapts well to the harsh climatic conditions of Saudi Arabia [85]. Predominantly, the plant is popular for its sedative, anti-inflammatory, antioxidant, and anxiolytic properties, and can be used for skin abrasions and insect/snake bites. As reported, the plant is known to contain several vital components responsible for its medicinal properties, and among them are naphthoquinones, flavonoids, terpenoids, and phenols [86]. The flavonoids isolated from _E. Arabicum_ extract such as luteolin-7-O-glucosides, myricetin and quercetin have shown antioxidant activity in the DPPH assay [87].
###### Abstract  
A plant disease diagnosis method that can be implemented with the resources of a mobile phone application, that does not have to be connected to a remote server, is presented and evaluated on citrus diseases. It can be used both by amateur gardeners and by professional agriculturists for early detection of diseases. The features used are extracted from photographs of plant parts like leaves or fruits and include the color, the relative area and the number of the lesion spots. These classification features, along with additional information like weather metadata, form disease signatures that can be easily defined by the end user (e.g., an agromist). These signatures are based on the statistical processing of a small number of representative training photographs. The extracted features of a test photograph are compared against the disease signatures in order to select the most likely disease. An important advantage of the proposed approach is that the diagnosis does not depend on the orientation, the scale or the resolution of the photograph. The experiments have been conducted under several light exposure conditions. The accuracy was experimentally measured between 70% and 99%. An acceptable accuracy higher than 90% can be achieved in most of the cases since the lesion spots can recognized interactively with high precision.  
plant disease; smart phone application; image processing; classification; segmentation; citrus diseases +
Footnote †: journal: Medical Imaging
###### Abstract  
Device-to-device (D2D) communication is a promising technique for direct communication to enhance the performance of cellular networks. In order to improve the system throughput and utilization of spectrum resource, a resource allocation mechanism for D2D underlaid communication is proposed in this paper where D2D pairs reuse the resource blocks (RBs) of cellular uplink users, adopting a matching matrix to disclose the results of resource allocation. Details of the proposed resource allocation mechanism focused are listed as: the transmit power of D2D pairs are determined by themselves with the distributed power control method, and D2D pairs are assigned to different clusters that are the intended user sets of RBs, according to the threshold of the signal-to-interference-plus-noise ratio (SINR). The weighted efficiency interference-aware (WE-I-A) algorithm is proposed and applied subsequently to promote the system throughput by optimizing the matching of D2D pairs and RBs, where each D2D pair is weighted based on the SINR to compete for the priority of RBs fairly. Simulation results demonstrate that the proposed algorithm contributes to a good performance on the system throughput even if the uplink state is limited.  
D2D communication; resource allocation; SINR; throughput +
Footnote †: journal:  
Article
# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
## 1 Introduction
Recent works of great scientific impact have been developed using FAST software related to wind turbine control [33, 34, 35, 36]. In [37], a particle swarm optimization (PSO) and Pareto front-based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. In this work, an adaptive wind turbine control structure operating on the pitch variable in the nominal zone is developed. The proposed control strategy mainly combines two control loops, the first one to maintain the generator speed at its nominal value, and the second one to reduce the tower f-a displacements. The three elements of the control scheme are the following ones: a gain scheduling proportional-integral (PI) controller that regulates the turbine speed by actuating on the pitch angle, an adaptive feedforward that compensates for the changes in wind speed, and an active tower damping control that generates an extra pitch control component proportional to the tower f-a velocity for reducing the tower's structural fatigue. Because of the non-linearity of the system, these three proposed components are adaptive by gain scheduling according to the generator speed and/or the wind speed. An identification of six linear models at different operating points has been performed for tuning these controllers. For each identified model, a PI controller with fore-aft control and feedforward compensation is tuned by means of a multi-objective genetic algorithm (MGA) using two objective functions: one related to the generator speed error and other one related to the tower f-a displacements. To perform the optimization, the wind turbine model is simulated using Matlab/Simulink with the assistance of FAST (Fatigue, Aerodynamics, Structures, and Turbulence) software [38; 39]. The different obtained solutions are analyzed with multiple criteria decision-making (MCDM) methods, and an average solution of the chosen MCDM methods is calculated as the optimal solution for each operating point. The paper is organized as follows: Section 2 introduces a preliminary background about Pareto optimization and multiple criteria decision-making methods. In Section 3, the proposed adaptive control scheme is described. Section 4 shows a comparative analysis between the proposed controllers and a baseline controller. Finally, the conclusions are summarized in Section 5.