esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Pest and Disease Identification Based on RGB Image
#### 2.1.2 Deep Learning for Pest and Disease Recognition
[20] solves the problems of weak generalization ability and poor robustness of a single model and improves the accuracy of citrus disease image recognition. [PERSON] [21] studied the citrus leaf detection algorithm based on YOLOv4. Based on convolutional neural networks, features extracted from Densenet and Xception networks were fused to extract features for citrus red spider leaves, canker leaves, leaf miner leaves, and healthy leaves. Transfer learning is introduced for insufficient datasets. Figure 2: Common deep learning networks. The proposed fusion convolution classification model reduced the algorithm training time and improved the model detection accuracy. Some foreign scholars have also carried out corresponding research. [PERSON], M. et al. [22] proposed a hybrid method based on optimized weighted segmentation and feature selection to automatically detect and classify six citrus diseases. In the preprocessing stage, the top hat filter and Gaussian function are used to improve the contrast of the input image, which is helpful to spot segmentation of the disease and improve the segmentation accuracy. The feature selection methods based on PCA score, entropy, and skewness covariance vector were used to select the most prominent features, and the selected features were classified by multi-class SVM. [PERSON], [PERSON] et al. [23] proposed a deep convolutional-neural-network-integrated classifier for the identification of citrus pests, including citrus leaf miner, sooty mold, and pulvinaria. The diversity of data level, feature level, and classifier level was considered to increase the diversity of basic classifiers. In summary, the deep learning method can achieve relatively ideal results in disease identification. Table 1 compares the advantages and disadvantages of the above mentioned deep learning methods. Improving some modules of the model, using small sample transfer learning, and model fusion can enrich the network model, improve the robustness of the model, and improve the recognition accuracy. Figure 3: Flow chart of citrus pest and diseases identification in deep learning methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ref. ** & **Year** & **Purpose** & **Methods** & **Category** & **Metis** & **Demerits** & **Accuracy** \\ \hline
[11] & 2021 & Improve recognition accuracy & The ResNet34 model was used to classify the disease, and the depth separable convolutional network & Huanglongbing, schob, black spot, & The model is lightweight and the computation is reduced & Lack of multi-leaf, multi-fruit recognition & 88.79\% \\
[12] & 2020 & Achieve early diagnosis of citrus pests and diseases & Hyperspectral images were collected and input into VGG-16 and selfagite, scotyl, symbol, disphyseate & High accuracy & 98.75\% \\  & & & & & &
# Experimental Evaluation of a Takagi-Sugeno Fuzzy Controller for an EV3 Ballbot System +
## 2 Dynamical Model
### EV3 BRS's State Variable Equations  
From (9), by selecting as the state variables \(x_{1}=q_{1}\), \(x_{2}=q_{2}\), \(x_{3}=\dot{q}_{1}\), and \(x_{4}=\dot{q}_{2}\); the state variable equations are then formed as  
\[\begin{split}\dot{x}_{1}&=x_{3},\\ \dot{x}_{2}&=x_{4},\\ \dot{x}_{3}&=\frac{1}{\det(M(q))}[-m_{22}c_{11}x_{3}- m_{22}\dot{s}_{11}+c_{21}m_{12}x_{3}+m_{12}f_{B_{b}}x_{4}-m_{12}u],\\ \dot{x}_{4}&=\frac{1}{\det(M(q))}[m_{21}c_{11}x_{3}+ m_{21}\dot{s}_{11}-m_{11}c_{21}x_{3}-m_{11}f_{B_{b}}x_{4}+m_{11}u],\end{split} \tag{12}\]  
which can also be written as  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Units** \\ \hline Mass of the body & \(m_{B}\) & 0.7448 & kg \\ \hline Mass of the ball & \(m_{b}\) & 0.0139 & kg \\ \hline Radius of the wheel & \(R_{w}\) & 0.0222 & m \\ \hline Radius of the ball & \(R_{b}\) & 0.026 & m \\ \hline Body moment of inertia & \(I_{B}\) & \(\frac{m_{B}L^{2}}{3}\) & kg m\({}^{2}\) \\ \hline Ball moment of inertia & \(I_{b}\) & \(\frac{2m_{b}R_{b}^{2}}{3}\) & kg m\({}^{2}\) \\ \hline Height of the centre of mass & \(L\) & 0.155 & m \\ \hline Gravity acceleration & g & 9.81 & m/s\({}^{2}\) \\ \hline Gear ratio, motor ball & \(\delta\) & \(-\frac{R_{w}}{R_{b}}\) & – \\ \hline DC motor moment of inertia & \(I_{M}\) & \(1\times 10^{-5}\) & kg m\({}^{2}\) \\ \hline Friction between body and surface & \(f_{B_{g}}\) & 0 & Nms/rad \\ \hline Friction between body and ball & \(f_{B_{g}}\) & 0.0022 & Nms/rad \\ \hline DC motor torque constant & \(k_{t}\) & 0.317 & Nm/A \\ \hline DC motor resistance & \(R_{m}\) & 6.69 & \(\Omega\) \\ \hline DC motor back EMF constant & \(k_{b}\) & 0.468 & V s/rad \\ \hline \hline \end{tabular}
\end{table}
Table 1: EV3 BRS’s Parametric values.  
\[\begin{bmatrix}\dot{x}_{1}\\ \dot{x}_{2}\\ \dot{x}_{3}\\ \dot{x}_{4}\end{bmatrix} = \begin{bmatrix}0&0&1&0\\ 0&0&0&1\\ 0&0&\frac{1}{\det(M(q))}(m_{12}c_{21}-m_{22}c_{11})&\frac{1}{\det(M(q))}(m_{12} f_{\rm B_{b}})\\ 0&0&\frac{1}{\det(M(q))}(m_{21}c_{11}-m_{11}c_{21})&-\frac{1}{\det(M(q))}(m_{11} f_{\rm B_{b}})\end{bmatrix}x+\begin{bmatrix}0\\ 0\\ -\frac{m_{12}}{\det(M(q))}\\ \frac{m_{11}}{\det(M(q))}\end{bmatrix}u \tag{13}\] \[+ \begin{bmatrix}0\\ 0\\ -\frac{1}{\det(M(q))}(m_{22}g_{11})\\ \frac{1}{\det(M(q))}(m_{21}g_{11})\end{bmatrix}\]
## 5 Application of PGIP in Plant Disease Resistance Genetic Engineering
Expressing PGIP from pear in transgenic grape can improve its resistance against [PERSON]'s disease caused by _Xylella fastidiosa_[84]. The overexpression of PGIP2 from Chinese cabbage in transgenic cabbage or tobacco can enhance the resistance to soft rot bacteria [45]. PGIP from tomato stem extract can inhibit PGs from _Ralstonia solanacearum_[83]. A recent study on rice showed that the PGIP4-containing chromosome is related to the resistance of bacterial stripe disease, with increased susceptibility to bacterial stripe disease in rice when the _PGIP4_ is silenced by RNAi [78]. RNA-seq analysis revealed that the _OsPGIP1_-mediated resistance of bacterial leaf streak is induced by PG of _Xoc_ and other pathogenicity factors, \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gene** & **System for Expression** & **PG Inhibition** \\ \hline St. John’s wort & & _HypPGIP_ & _Agrobacteriumtium tunefaciens_[20] \\ (_Hypericum perforatum_) & & _Alternaria alternate, Colletotrichum nicotianae_[76] \\ Pepper & _CaPGIP1_ & & _Alternaria alternate, Colletotrichum nicotianae_[76] \\ (_Capsicum annuum_ L.) & _CaPGIP2_ & & _Alternaria alternate, Colletotrichum nicotianae_[76] \\ Rice & _OsPGIP2_ & & _Sclerotinia sclerootinam, SsPG3 and SsPG6_[74] \\ (_Oryza sativa_ L.) & _OsPGIP1_ & Rice & Sheath blight, _R Rhizoctonia solani_[73; 77; 77] \\ Pear & _OsPGIP4_ & Rice & _Xanthomonas oryzae_ pv. _oryzicola_[78] \\ (_Pyrus bretschneider_) & _pPGIP_ & Tomato & _Botrytis cinerea_[79] \\ Mungbean & _VrPGIP1_ & & _Callosobranchus chinensis_[14] the \\ (_Vigna radiata_ ([PERSON]) & _VrPGIP2_ & Mungbean & polygalacturonase in _Callosobranchus_ maculatus_[80] \\ Alfafla & _MsPGIP2_ & _Medicago sativa_ & _Pseudoregiza mediciamis_ (Lib.) Sacc. [15] \\ (_Mediicago sativa_) & _MPGIP_ & & [81] \\ Mulberry (_Morus alba_ L.) & _MdPGIP1_ & Tobacco and potato & _Fungal polygalacturonases_[82] \\ Apple (_Malus domestic_) & _GhPGIP1_ & _Gossypium hirsutum_, _Arabidopsis thaliana_ & _Verticillium dahliae, Fusarium oxysporum_[60] \\ (_Gossypium hirsutum_) & _BvPGIP1_ & _Nicotiana benthlamiana_ & _Fusarium solani, Botrytis cinerea_, and _R Rhizoctonia solani_[24] \\ Sugar beet (_Beta vulgaris_) & _BvPGIP2_ & _Nicotiana benthlamiana_ & _Fusarium solani, Botrytis cinerea_[24] \\ Tobacco & _NtPGIP_ & _Escherichia coli_ & _Phyotrophlora capsaicin_[65] \\ (_Nicotiana tabacum_) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PGIP tested in different systems against fungal PGs. and is primed by the activated expression of \(PR\) genes, cell wall defense-associated genes and regulators, and an accumulation of \(JA\)[85]. Increased PGIP in potato improved the resistance of _Ralstonia solanacearum_[86]. Overall, it is clear that PGIPs contribute to defense responses and inhibit bacterial disease [83].
# Improvement of Surface Roughness and Cracking Study of Nb\({}_{3}\)Sn SRF Films
## 3 Study on BEP Preprocessed Nb\({}_{3}\)Sn
### Experimental Setups of Different Pre-Polishing Methods  
BEP electrolyte consists of hydrofluoric (HF), sulfuric (H\({}_{2}\)SO\({}_{4}\)), and lactic acids (C\({}_{3}\)H\({}_{6}\)O\({}_{3}\)); the schematic diagram is shown in Figure 2. BCP, EP, and BEP were applied on Nb samples to study the relation of roughness between Nb substrate and the film. All the Nb samples were from the same batch for cavity fabrication and have a high Residual Resistivity Ratio (RRR) value of 300. Every Nb sample accepted preliminary mechanical polishing and 150 \(\upmu\)m BCP and received further BCP, EP, or BEP treatments.  
Figure 1: Coating temperature profile.  
Figure 2: Schematic diagram of BEP method, from [13].  
The BCP solution used is the traditional type of mixture of HF (40%), H3 PO4 (98%), and HNO3 (68%) in a volume ratio of 1:1:1. The EP solution also used the standard solution of HF (40%) and H2 SO4 (98%) in a volume ratio of 1:9, and the temperature of solution was controlled below 25 \({}^{\circ}\)C.  
The main components include a DC power supply, an aluminum cathode, a Teflon stirrer, and a water bath with magnetic stirring. There are many factors that affect the polishing results of BEP, including electrode variables, electrical parameters, solution variables, external environment variables, etc. More details can be found in Reference [13]. In our experiments, the distance between the cathode and the anode is 5 cm. We changed the bath temperature in the range of 21-30 \({}^{\circ}\)C; the ratio of acids, HF: H2 SO4: C3H6O3, in the range from 4:5:11 to 10:5:11; and the stirring frequency in the range of 10-15 Hz. Figure 3 displays the typical I-V curve of the BEP process, including three main periods, which are etching, oscillation, and polishing. The current density and polishing speed are proportional. The higher ratio of HF will generate a faster polishing rate, and the highest polishing rate of approximately 20 \(\upmu\)m/min is achieved, as shown in Figure 4. While the current is too large, the bad bubble corrosion will happen and destroy the smooth surface. The surface of Nb substrate using a relative larger polishing rate is smoother under eye observation. Figure 5 presents the niobium sample polished on the left half with about 10 \(\upmu\)m/min polishing rate, and it shines like a mirror.  
Figure 4: Polishing rates using solutions with different volume ratio of acids.  
Figure 3: The I-V curve during BEP experiment (**a**) and the curve of the polishing rate versus current density (**b**).
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 2 Numerical Method
### Code Validation  
In order to verify the reliability of numerical simulation, it is crucial to conduct validation. Due to the lack of real integrated experimental data for aircraft, this article selected the numerical simulation examples of the inlet of [PERSON] et al. [37] to verify the flow simulation capability of aircraft. This section selected experimental conditions with a flight Mach number of 4 and angles of attack of 4\({}^{\circ}\) and 12\({}^{\circ}\) for numerical simulation; as shown in Figure 2, the abscissa represented the dimensionless flow direction distance normalized by the characteristic length l of the aircraft, and the symbols Exp-1, 2, 3, and 4 denoted the measured pressure recovery coefficients at the upper and lower surfaces for angles of attack of 4 degrees and 12 degrees, respectively. In correspondence, the calculated pressure recovery coefficients are designated as cal-1, 2, 3, and 4, and the simulation results of the pressure recovery coefficient \(\text{C}_{\text{P}}\) on the upper and lower wall surfaces of the inlet are basically consistent with the experimental results. This indicates that the numerical simulation data for the compressed air entering the inlet through the aircraft's compression surface are reliable. Furthermore, due to the lack of experimental validation for gaseous kerosene injection, this section chose the ethylene transverse jet simulation scenarios depicted by [PERSON] et al. [38] to substantiate the fuel injection simulation aptitude. Throughout these jet simulation scenarios, an illustration of the jet was exhibited in Figure 3, air flowed through the flat plate from the inlet, and ethylene was injected from a nozzle with a diameter of 4.8 mm; the mesh was shown in Figure 4. Within Figure 5a, the horizontalaxis signified the distance aligning with the flow direction, while the vertical axis indicated the wall pressure. Conversely, in Figure 5b, the variable y embodied the elevation of the jet. A juxtaposition of the wall pressure and penetrative depth fitting curves with the empirical findings was conducted. The outcomes were nearly congruent with the empirical data, attesting to the precision of the jet mixing process. Combined with the two cases, these validations reaffirmed the feasibility of using this in-house code to evaluate the integrated internal and external flow fields of ramjet for fuel mixing.
## 3 Preliminaries  
### Energy Consumption Model  
Communication is the activity responsible for the bulk of the energy consumption in WSNs [27]. An energy consumption model used in Reference [27] is applied in this study (see Figure 1).  
Assumptions are that: the data can reach every node from its neighbors; the data contain information on distance and residual energy; the radio circuit in the sensor has a power control, and can expend the minimum required energy to reach the intended recipients; and radio circuit can be turned off to avoid receiving unintended transmissions. The transmission computation costs and receiving costs for a k-bit message at a certain distance \(d\) are shown as follows:  
Transmitting  
\[E_{T}(k,d)=E_{elec}\times k+E_{amp}\times k\times d^{2} \tag{1}\]  
Receiving  
\[E_{R}(k)=E_{elec}\times k+E_{BF}\times k \tag{2}\]  
Total energy cost  
\[E=E_{T}+E_{R} \tag{3}\]  
where \(E_{elec}=50 nI/bit\), \(E_{amp}=100 pI/bit/m^{2}\) for the transmitter amplifier, and \(E_{BF}=5 nI/bit\) when beamforming is used. \(d\) represents the distance of two nodes, and \(k\) represents the number of message bits.  
Thus, by decreasing the communication distance and the volume of data to transmit, energy can be saved.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 4 Network Indicator System Construction  
Due to the intricate physical and logical interconnections across the various levels of the IoV network, consequently, to effectively analyze the performance of the network, comprehend the task propagation patterns, and further clarify the relationship between the various levels of the system, it is imperative to establish a comprehensive assessment indication framework. This framework will facilitate the analysis of the IoV network's performance, check the level of congestion in a highly dynamic network, and enhance the scientific and anticipatory management of IoV networks.  
Based on Section 3, after completing the construction of the network traffic transmission model, the results of the forwarding algorithm can be obtained, and massive output data can be processed to generate multiple core indicators for the construction of the evaluation system. Given that IoV networks primarily serve users, the network performance significantly impacts their service experience, which varies according to users' priority. Therefore, from the task perspective, the successful task arrival rate, packet loss rate and delay are selected as the core indicators; from the network perspective, when the task is transmitted in the network, the load rate of the nodes and links are different, and both of them will indirectly affect user task service experiences. For example, when the vehicle initiating the service overloads due to failure or excessive link loads between it and the  
Figure 6: Multi-task forwarding model.  
Figure 7: Multi-node multi-task forwarding.  
next-hop node, it leads to task accumulation and packet loss. Therefore, using the node load rate, link load rate, and the actual total task traffic of the network as the core indicators can effectively reflect the blocking situation of the network for performance evaluation.  
In summary, considering that the analysis indicators should be scientific, reasonable and complete, from the perspective of network structure and performance, this paper adopts five indicators to analyze the network performance. These indices include packet loss rate, task arrival rate, link load rate, node load rate, and total network traffic.
# MLUTNet: A Neural Network for Memory Based Reconfigurable Logic Device Architecture
### Motivations and Contributions  
When deploying neural networks to MRLD-like programmable logic hardware, there are two most critical issues: storage cost and architecture conversion. Some works on limiting the numerical accuracy of neural networks have been presented, but how to structurally reorganize to cut the complexity and storage expense of the network is still a meaningful problem to be solved.  
Based on the starting point of solving the above problem, we propose a new neural network based on the MRLD structure named MLUTNet. In MLUTNet, we adopt a network topology similar to the MRLD structure to partition the middle layer of the network in order to reduce the storage expense of neural network deployment on hardware platforms effectively. The contributions of this paper are as follows:  
Figure 1: The structure of MRLD.  
* We propose MLUTNet, a novel neural network with an atypical structure. MLUTNet combines the advantages of two aspects: the efficient learning performance of neural networks and the similar structure of MRLD, which makes MLUTNet easy to implement on MRLD or other similar logical storage devices without much extra effort;
* We conducted experiments and compared the results with their MLUTNet versions on three different popular datasets using standard and binary neural networks as the baselines, respectively. Compared to a fully connected neural network of the same size, MLUTNet saves over 50% of the weight matrix storage space and also reduces the size of individual weight matrices efficiently, with acceptable performance loss in accuracy on the dataset;
* The method is simple and easy to implement with good scalability; the MLUTNet version of a particular network can be substituted for the original network at no additional cost to meet the need to reduce the size of the network. This is very friendly for subsequent extension studies.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance  
Antibiotic resistance has emerged in various locations around the world. The most recent meta-analysis indicated a rising trend from 2013 to 2023 [72; 73]. The highest prevalence was observed for metronidazoleae resistance, followed by clarithromycin, in the Southeast Asia, Europe, and Africa regions [72]. The prevalence of resistance to amoxicillin is low in most areas. However, it began to rise, particularly in DR Congo and Mongolia [74; 75; 76]. This outcome could be attributed to the presence of a biofilm. A prior investigation indicated that strains capable of biofilm formation exhibit a higher resistance level than those that do not form biofilms [75; 77]. Antibiotic resistance is defined as increasing the minimum inhibitory concentrations of antibiotics due to a permanent change in cells, such as mutation or the acquisition of resistance functions by horizontal gene transfer [24; 78].  
A study reported high biofilm formers to increase bacterial survival during antibiotic exposure [77]. The minimum bactericidal concentrations of CLR against cells in the biofilmwere higher (1.0 \(\upmu\)g/mL) than for planktonic cells (0.25 \(\upmu\)g/mL). _H. pylori_ CLR resistance mutations are more frequently observed in biofilms than in planktonic cells [66]. The mechanisms of antibiotic resistance by biofilm formation are explained as follows:
# Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
## 2 Integration and Mining of Electricity Market Data
### Electricity Information Data Mining Based on Clustering Algorithm  
With the rapid development of smart grids and the establishment of advanced measurement technologies, the accumulation of power data in distribution networks has grown exponentially over time. Behind the massive power data lies deeper connections, making it crucial to uncover the hidden meanings in the data. Simultaneously, with the widespread use of smart meters, it is possible to collect electricity consumption data from users at higher densities, capturing fluctuations in load data and better reflecting user electricity consumption behavior. Therefore, employing big data deep mining techniques for mining electricity consumption behavior can provide a better understanding of user individuality and guide the deep-level expansion of business for power grid companies [29; 30].  
Since users' massive electricity consumption data are stored at multiple time scales, such as yearly, monthly, and daily, and the data dimensions are large with redundant information, direct application of clustering methods would not only consume a significant amount of time for calculations but also severely affect the accuracy of clustering. Therefore, this section focuses on two aspects: data dimensionality reduction and clustering for large-scale electricity consumption information data mining. On the one hand, to address the issue of large data dimensions, this section adopts an approach based on information entropy segmentation aggregation approximation. This method reduces data dimensions while ensuring a re-expression of user load fluctuation characteristics, resulting in a re-expression sequence with variable time resolution for load data. On the other hand, for clustering of re-expressed sequences, this study combines GCN and spectral clustering algorithms [31; 32], which does not require setting cluster centers. The use of graph convolutional networks can process non-Euclidean data and effectively extract data features for learning, which is beneficial to capturing global information and thus better expressing the characteristics of nodes. At the same time, the feature matrix is used to further reduce the dimensionality of the data and reduce the computational complexity. Therefore, this paper uses spectral clustering algorithm to cluster end users, thereby extracting typical daily load curves and providing data support for the construction of end-user power supply demand reliability indicators.