esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Complex distributed parameter systems (DPSs) are prevalent in numerous industrial processes. However, the nonlinear spatiotemporal dynamics inherent in DPS present significant challenges for accurate modeling. In this paper, an innovative online three-dimensional (3D) fuzzy multi-output support vector regression learning method is proposed for DPS modeling. The proposed method employs spatial fuzzy basis functions from the 3D fuzzy model as kernel functions, enabling direct construction of a comprehensive fuzzy rule base. Parameters \(C\) and \(\varepsilon\) in the 3D fuzzy model adaptively adjust according to data sequence variations, effectively responding to system dynamics. Furthermore, a stochastic gradient descent algorithm has been implemented for real-time updating of learning parameters and bias terms. The proposed method was validated through two typical DPS and an actual rotary hearth furnace industrial system. The experimental results show the effectiveness of the proposed modeling method.  
distributed parameter system; fuzzy model; support vector regression; online model; stochastic gradient descent Article
## 4 Discussion
### Challenges
#### 4.1.2 Limited Water Sources and Increasing Population  
With the restricted natural water assets, providing resources for drinking water is getting challenging [17; 38]. Over the earth, 71% is covered with water. However, only 2.5% of this water is freshwater [2]. In many regions of the world, water demand has exceeded supply and more regions are expected to face the same soon [13]. Finding natural drinking water resources is becoming harder over time due to the increasing population of the world, which results in increased water consumption [13; 39]. Infact, the major sources of water are depleting rapidly [13]. Further, climate change and global warming has impacted the water resources significantly[13; 14; 39; 49]. Due to the effects of water resources by climate changes, aquaculture and agricultural farmers suffered from economic  
Figure 3: Main challenges.  
losses due to the impacts of climate changes on production alongside with the costs of recovering from the damage [14].
## 5 Simulation Result and Discussion
### Simulation Setup
#### 5.1.1 Dataset  
We conducted the experiments on the plantar thermogram database provided by [PERSON] et al. [21]. The datasets were taken using a thermal camera and consisted of separate left and right foot images. As mentioned by [PERSON] et al. [45], the diabetic foot depends on ipsilateral and contralateral temperature. To enable the CNNs to learn the disease, we put/combined the right foot image and the left foot image of each patient side by side, as shown in Figure 13. The images of positive diabetic patients were labeled as DM, while the negative ones were labeled as CONTROL. There were 142 positive DM patients and 45 negative CONTROL patients. Due to the limited number of datasets for deep learning and unbalanced classification problems emerging during the training process, we performed image augmentation by rotating, scaling, and translating the images, as performed in some of the literature [46; 47]. Image augmentation resulted in 1200 images each of positive and negative DM patients. The augmentation was performed more on images of negative CONTROL patients to balance the dataset. To deal with the stages in the simulation setup, we split the dataset into 75:5:10:10, which means 75% for training, 5% for validation stage 1, 10% for validation stage 2, and 10% for testing. Because MobileNetV2 and ShuffleNet required 224 \(\times\) 224 pixels for an input image, we resized the dataset. Table 1 shows detailed numbers of images in the dataset before and after augmentation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Images** & **Number of Images** \\ \hline \multicolumn{3}{c}{**Before Augmentation**} & **After Augmentation** \\ \hline CONTROL & 142 & 1200 \\ DM & 45 & 1200 \\
**Total** & 187 & 2400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of images before and after augmentation.  
Figure 12: The simulation steps.
## 2 The Role of Phytochemical in Oxidative Stress
### Case Study: Antioxidant Activity of Flavonoids
#### 2.2.1 Free-Radical Scavenging  
The antioxidant capacities of flavonoids can arise from free radical scavenging or direct ROS scavenging. Flavonoids are able to donate a hydrogen atom to neutralize free radicals. Also, flavonoids may act by single-electron transfer.  
In the literature, it is well-known that some flavonoids present higher antioxidant activity than others, which is directly related to their chemical structure [37; 39; 40]. For efficient radical scavenging, the required structural features of flavonoids are summarized as follows: (i) An \(o\)-dihydroxy (catechol) structure in the B ring for electron delocalization (Figure 2, ring a); (ii) hydroxyl groups at position 3 on the C ring and 5 and 7 on the A ring provide an increase in antioxidant activity (Figure 2, ring b), and (iii) a C\({}_{2}\)-C\({}_{3}\) double bond combined with the oxo-C\({}_{4}\) on the C ring (Figure 2, ring c).  
Figure 1: Basic structure of flavonoids.  
The most significant actor in scavenging free radicals is the B ring hydroxyl structure. The hydroxyl groups on this ring stabilize hydroxyl, peroxyl, and peroxynitrite radicals through the donation of hydrogen or electrons. Quercetin has shown to be a flavonoid expressing higher antioxidant activity due to the presence of hydroxyl groups and twisting angle of the B ring [41].  
The total number of hydroxyl groups in the flavonoid structure influences the mechanism of antioxidant activity. To evaluate the antioxidant activity of flavonoids' hydroxyl groups, the bond dissociation energy (BDE) is used, where the lower the BDE value is, the easier the dissociation of the hydroxyl bond and the reaction with free radicals [30; 42].
## 3 Jitter Modeling
### Jitter Components
#### 3.2.3. Random Jitter (RJ)  
RJ is caused by thermal noise, shot noise, and other high-order noise. It can be created by Gaussian white noise. And the statistical probability density function (PDF) for RJ is given by [26]  
\[f_{\text{RJ}}(\Delta t)=\frac{1}{\sqrt{2\pi\sigma}}\exp-\frac{\left(\Delta t- \mu\right)^{2}}{2\sigma^{2}} \tag{10}\]  
where \(f_{\text{RJ}}(\Delta t)\) is RJ amplitude of data bit \(n\) at sample time \(nT\), \(\mu\) is the mean of RJ, and \(\sigma\) is the standard deviation of RJ. In this method, the \(\sigma\) can be adjusted.
## 1 Introduction
* A dual-channel embedding method based on Word2 Vec and FastText is introduced to enhance generalization ability. Additionally, an embedding enhancement strategy is adopted to construct a 384-dimensional joint embedding space. * A Transformer classifier for multi-scale feature fusion is designed. By using an attention pooling fusion mechanism, the model dynamically evaluates feature importance through a learned weight matrix, reducing false rates and computational overhead. The rest of this work is structured as follows: Section 2 provides background research on the current IDS detection methods and the combination of LLMs and network traffic. Section 3 describes the joint embedding and Transformer model architecture and related theoretical knowledge. Section 4 details the experimental implementation. Section 5 analyzes the experimental results, and Section 6 draws conclusions and proposes prospects for future work.
# Exploring Surface-Driven Mechanisms for Low-Temperature Sintering of Nanoscale Copper
## 4 Conclusions  
The atomic mechanisms and mechanical properties of the nanoscale copper low-sintering process at various temperatures are investigated via molecular dynamic simulation. It reveals the behavioral characteristics of nanoparticles at different temperatures and identifies the significant role of low-temperature sintering mechanisms dominated by surface and the rapid surface dynamics of atoms.  
By using copper nanoparticles with different structures, we construct multiple models containing 16 distinct nanoparticles and sinter them under different conditions. The porosity changes and crystal structure transformations during the sintering process show the low-temperature sintering mechanism of copper nanoparticles. At a temperature lower than 600 K, the sintering process primarily relied on structural adjustments induced by residual stresses and the contact between nanoparticles. The nanoparticles within the system retain their initial contact morphology even after the sintering process is completed. After the temperature reaches 600 K, the surface-driven atomic fast motion is activated. The emergence of rapid surface dynamics facilitated further structural adjustments between and within the nanoparticles, ultimately leading to a significant reduction in porosity, indicating 600 K is a critical temperature for the low-temperature sintering of nanoscale copper.  
It is generally considered that the sintering process of nano-solder could occur at lower temperatures due to the effect of surface melting. Our previous work shows that the surface melting process does not commence at sintering temperatures of 800 K or below, where the surface remains solid. In this work, [PERSON]'s modulus and tensile strength align perfectly with the points of strength enhancement and the onset of rapid surface dynamic states at 600 K. These results are closely related to the rapid surface dynamic state in the low-temperature sintering mechanism, providing an important reference for the enhancement mechanism of mechanical properties in the nanoparticle sintering process.  
Conceptualization, X.X. and Y.Z.; methodology, Z.S.; software, J.L.; validation, J.L., Z.S., Z.L. and P.G.; investigation, J.L.; resources, Z.S.; writing--original draft preparation, J.L.; writing--review and editing, Z.S.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund Project of the State Key Laboratory of Intelligent Vehicle Safety Technology (IVSTSKL-202308) and the National Natural Science Foundation of China (No. 92373204).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 5 Pressureless Imbibition Experiment
### Results and Analysis
#### 5.3.2 Staged Characteristics of Imbibition  
Because the imbibition effect of silica sol in coal-measure mudstone is related to time and the contact surface area, the silica sol absorption rate can be defined as the quantity of silica sol increase in the unit time and unit area. Test results are shown in Figure 10.  
The paper introduces the nonlinear curve-fitting method to analyze the experimental data using Origin software. Silica sol absorption rate follows the power function:  
\[AR=0.0003t^{-0.63743},\;R^{2}=0.9954 \tag{12}\]  
Combined with Figure 10, the whole process can be divided into four stages: intense stage (0-1 min), rapid stage (1 min to 3 h), lag stage (3-212 h), and saturation stage (>212 h). In the intense stage, the accumulated silica sol absorption proportion increases sharply to 0.19% with an absorption rate of 3.31 \(\times\) 10\({}^{-5}\) to 2.36 \(\times\) 10\({}^{-5}\) g/mm\({}^{2}\)/s. In the rapid stage, the proportion increases rapidly to 0.50% with a rate of 2.58 \(\times\) 10\({}^{-7}\) to 1.33 \(\times\) 10\({}^{-6}\) g/mm\({}^{2}\)/s. Then, a long-drawn lag stage begins, and the proportion increases slowly to 2.57% with a rate of 2.46 \(\times\) 10\({}^{-8}\) to 7.91 \(\times\) 10\({}^{-8}\) g/mm\({}^{2}\)/s. The last stage is the saturation stage, when the accumulated silica sol is stabilized at 2.58%. These results are consistent with the capillary force test in the mercury injection. The capillary force of silica sol decreases with saturation level, and then the absorption rate slows down.
(1794) presents a significant challenge due to the pathogen's high genetic plasticity, with chemical control using synthetic fungicides being the most widely used strategy [6; 14; 15]. While synthetic fungicides effectively reduce the pre- and post-harvest fruit losses, their use has led to increased fungicide residues on produce, the emergence of fungicide-resistant mould strains, and has raised concerns regarding human health and the environmental impact [3; 4; 5]. Consequently, it is crucial to develop safe and effective alternative strategies for managing grey mould diseases in fruit crops [3; 16]. Antagonistic fungi have proven effective against _B. cinerea_, which is susceptible to suppression by microorganisms such as non-_Saccharomyces_ yeasts that produce antifungal compounds [3; 6; 17]. Compared to chemical control, the use of antagonist microorganisms offers several benefits, including the absence of toxic residues, environmental safety, ease of application, and cost-effectiveness [15; 18]. Yeasts exhibit valuable antifungal properties, including the secretion of killer toxins such as mycocins, the production of cell wall-degrading enzymes (chitinase, \(\beta\)-1,3-glucanase, protease, laminarinas, and peroxididases), the synthesis of volatile organic compounds (VOCs), rapid colony formation, growth within surface wounds, competition for nutrients and space, and the induction of host resistance [3; 13; 14]. Killer yeasts, including the _Meyerozyma guilliernondii_, _Subomyces pyralidae_ (formerly, _Candida pyralidae_), _Pichia kluyveri_, and _Hanseniaspora_ species, have shown antimicrobial activity against a range of fruit-spoilage fungi [17; 19; 20; 21]. Previous research by [PERSON] et al. [17] evaluated non-_Saccharomyces_ yeasts for their ability to inhibit mycelial growth of a strain of _B. cinerea_ in vitro and on apples, with results indicating species- and strain-dependent inhibitory effects. Building on this foundation, the present study sought to screen non-_Saccharomyces_ yeasts for extracellular enzyme activity and to assess the inhibition of the mycelial growth and spore germination of selected yeasts against three distinct _B. cinerea_ strains in vitro and in vivo on apples and strawberries. The yeasts that showed the most potential as biocontrol agents according to [PERSON] et al. [17] were used in this study to confirm their biocontrol activity against _B. cinerea_.
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 1 Introduction
From the point of view of the spacecraft dynamics along a DNKO, station keeping has also been studied in the context of three-body problem periodic orbits, which also suffer from deviations from nominal periodic behavior due to unmodeled physics. In this respect, the interested reader can refer to [PERSON]. [31; 32; 33]. In this paper, a scientific interplanetary probe equipped with a photonic solar sail is required to trace a circular DNKO at an angular velocity equal to the mean motion of Mars [16] so as to maintain a constant average phase shift with respect to the angular position of the Red Planet along its heliocentric orbit. As part of Mars exploration missions, it is worth mentioning the work of [PERSON] et al. [34], who proposed an Earth-Mars interplanetary communication relay in support of crewed missions to Mars. In fact, a Mars-synchronous DNKO, with its revolution period matching that of Mars, would be useful for remote sensing applications, allowing a scientific payload to continuously observe the high-latitude regions of Mars from a quasi-stationary position relative to the planet. Such a vantage point would offer unique possibilities to better understand some physical phenomena of great scientific interest that are specific to high-latitude areas, such as the existence of polygonal features, the occurrence of dust activities in the southern high-latitude regions, the mechanism capable of generating the lobate features located at high latitudes, or the origin of global-scale oscillations possibly due to waves in the atmosphere. Analogous to what has been recently discussed by the authors in Ref. [35], this paper also analyzes the rapid transfer trajectory from Earth to an assigned Mars-based DNKO as a function of typical solar sail performance characteristics, which are selected consistently with the propulsive requirements necessary for maintaining the target DNKO. In particular, the mission scenario is studied by neglecting the uncertainties related to both the solar sail optical parameters and the solar irradiance fluctuations [36; 37; 38]. This paper is structured as follows. Section 2 deals with the mathematical preliminaries. In particular, it first recovers the well-known thrust vector model of an ideal solar sail, that is, a flat solar sail with a perfectly reflecting coating, and then details the sail performance required to maintain a circular DNKO of given characteristics. Section 3 graphically illustrates the results previously presented and provides the distance of the spacecraft from Mars as a function of a set of design parameters. Section 4 addresses the transfer problem from the Earth to the circular DNKO, which is solved in an optimal framework by minimizing the total flight time, and shows the simulation results in two special scenarios taken as references. The final section contains the concluding remarks.
###### Abstract  
In this paper, we synthesize carbon nanotubes (CNTs) by using atmospheric pressure microwave plasma chemical vapor deposition (AMPCVD). In AMPCVD, a coaxial plasma generator provides 200 W 2.45 GHz microwave plasma at atmospheric pressure to decompose the precursor. A high-temperature tube furnace provides a suitable growth temperature for the deposition of CNTs. Optical fiber spectroscopy was used to measure the compositions of the argon-ethanol-hydrogen plasma. A comparative experiment of ethanol precursor decomposition, with and without plasma, was carried out to measure the role of the microwave plasma, showing that the 200 W microwave plasma can decompose 99% of ethanol precursor at any furnace temperature. CNTs were prepared on a stainless steel substrate by using the technology to decompose ethanol with the plasma power of 200 W at the temperatures of 500, 600, 700, and 800 \({}^{\circ}\)C; CNT growth increases with the increase in temperature. Prepared CNTs, analyzed by SEM and HRTEM, were shown to be multiwalled and tangled with each other. The measurement of XPS and Raman spectroscopy indicates that many oxygenated functional groups have attached to the surface of the CNTs.  
microwave plasma; AMPCVD; CNTs +
Footnote †: journal: Applied Sciences
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 1 Introduction
However, the aforementioned numerical methods either rely on various contact parameters of the immersion joint or are specific to the simplified components of the joint, which cannot fully reflect the mechanical properties of the joint on account of the complex composition. A 1/4.69 scale model experiment of a segmental joint was conducted on a settlement platform. Considering different vertical settlements, the distribution and transfer mechanisms of the shear force were investigated in the experiment. Moreover, the allowed settlements of the tunnel segments and the vertical shear failure features of the concrete shear keys were also studied [21]. However, this experiment focused on the vertical shear behaviors of the segmental joint, whose structure and components are different from those of the immersion joint. Although the shear behaviors of the immersed tunnel have received much attention, no sufficient studies have been performed with an emphasis on the vertical shear behaviors of the immersion joint with steel shear keys. In addition, vertical shear loads are occasionally accompanied by the transverse shear loads [22; 23]. Hence, a comprehensive study of the vertical shear behaviors is necessary. This paper presents an experiment of an immersion joint with steel shear keys to investigate the corresponding vertical shear behaviors under multidirectional loads. Two kinds of compression-shear tests are designed for this experiment. Different compression loads on the elements are considered to simulate different water pressures on the immersion joint at various water depths. The vertical shear stiffness and the ultimate shear capacity of the immersion joint are obtained to characterize the vertical shear behaviors. The failure phenomenon is also observed. Finally, conclusions and suggestions are outlined according to the experimental results.
## 4 Results and Discussion
### Experiment and Analysis  
#### 4.2.1 SRC Module Enhancements  
First, we trained a Pointnet++ [14] original network model on the newly created MLS point clouds dataset, and then added the SRC sampling module to Pointnet++ [14] to replace the original ball sampling module, trained a new model with the MLS point clouds dataset, and then selected the same area for segmentation testing and validation to obtain the test results. From Figure 8, we can see that the new network with the SRC module added effectively segmented the sparse road point clouds at more distant locations, and the sparse lane line point clouds data at the edges were also segmented. Compared with  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Object Types**} & \multicolumn{5}{c}{**Class IOU**} & \multicolumn{5}{c}{**Overall**} \\ \cline{2-7}  & **Road** & **Line** & **Lamp** & **Guard** & **Sign** & **Accuracy** & **mIOU** \\ \hline Syncretic & 0.991 & 0.662 & 0.385 & 0.999 & 0.983 & 0.991 & 0.726 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation results of the validation set of the Beijing Daxing airport loop, with single-class feature IOUs on the left and overall accuracy and mIOU on the right.  
Figure 7: Close-up view of the qualitative results of the three area blocks of the Beijing Daxing Airport loop. The road segmentation results are blue for the road surface, white for the lane lines, green for the signs, yellow for the guardrails, and cyan for the streetlights.  
the original already segmented near lane lines, SRC obtained a more accurate and precise segmentation effect.  
As can be seen from the bar comparison in Figure 9, the network testing accuracy with the addition of the SRC sampling module reached 98.7%, which is better than the original Pointnet++ [14] network. In the test, the mIOU of the new network model replaced with the SRC sampling module reached 69.8%, which is better than the 63.3% of the original deep learning network. It indicates that under the same conditions, our method prediction yields more accurate results, more precise segmentation of sparse point clouds, and a significant improvement for features with less point clouds data such as streetlights. In addition to the correctness, our method maintains high accuracy in road point clouds tests in different regions. Therefore, it is inferred that our proposed method of adaptively varying the sampling radius is easier to learn the features of inhomogeneous sparse MLS road point clouds.  
Figure 8: Visualization of segmentation results for the network with the adaptive radius sampling module (SRC) alone compared to the original Pointnet++. The improvement of the segmentation results for the distal sparse point clouds lane lines with the addition of the adaptive radius sampling module can be seen in the red dashed box.  
Figure 9: Analytical plot of the accuracy evaluation of the network with the adaptive radius sampling module (SRC) added alone versus the original Pointnet++.