esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
In this paper, a multimodal sentiment analysis method based on a graph neural network is proposed, which can adaptively learn the hierarchical relationship and weight distribution between modes and maintain high performance when some modes are missing. We transplant the modal fusion problem to heterogeneous graphs and transform it into a node fusion problem. We model the feature complementarity and dependence of multimodal data by automatically extracting meta-paths and graph convolution products, and add virtual nodes to aggregate modal information. On the MOSI and MOSEI datasets, accuracy improvements were 1.7% and 0%, respectively, and F1-score improvements were 1.54% and 4.9%, respectively, compared with the current best models. We also created a novel special scene dataset, including 10K data, images, and EMOJI text, and made fine-grained annotation, which is helpful in promoting the optimization of sentiment analysis tasks in social scenes.  
Limitations: This work has certain limitations, including the exclusion of other types of multimodal data, such as video and audio. Additionally, we did not conduct a detailed analysis of the information conveyed by different meta-paths, nor did we explore more comparative learning strategies or loss function designs.  
Future work: In the future, we plan to expand our dataset to include more diverse multimodal data sources. We will also explore more effective strategies for meta-path extraction and investigate novel graph convolution network structures to further enhance the performance of our method.  
Conceptualization, J.P. and Y.H.; methodology, J.P. and Y.C.; software, P.Z.; validation, J.P. and Y.H.; formal analysis, J.P. and Y.C.; investigation, J.P. and Y.L.; resources, Y.H. and Y.L.; data curation, Y.C., Y.L. and Q.Y.; writing--original draft preparation, Y.C. and Y.H.; writing--review and editing, J.P. and Q.Y.; visualization, P.Z.; supervision, Y.H.; project administration, Z.O.; funding acquisition, Z.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (contract number SGHEXT005 JJS2310134).  
Not applicable.  
If you have any questions about this dataset or its usage, or require further information, please contact us. Our dataset is available at [[https://github.com/MR-YQZ/Social-Media-MultiSent-Dataset-SM-MSD-](https://github.com/MR-YQZ/Social-Media-MultiSent-Dataset-SM-MSD-)]([https://github.com/MR-YQZ/Social-Media-MultiSent-Dataset-SM-MSD-](https://github.com/MR-YQZ/Social-Media-MultiSent-Dataset-SM-MSD-)) (accessed on 8 November 2024).  
Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] were employed by State Grid Hebei Information and Telecommunication Branch, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### DBN-Based Path-Tracking Deviation Pattern Identification Model and Dataset Preparation  
In the application of deep learning, related studies have shown that the structural properties of Deep Belief Networks (DBNs) are adapted to 1D or 2D feature learning [13; 14], which can effectively recognize and classify each deviation pattern.
## 3 Results
### Sensory Evaluation and Moisture Content
The samples demonstrated increased brittleness and a bitter taste. The sensory analysis revealed that freeze-dried berry samples exhibited favorable organoleptic characteristics when subjected to a sublimation duration of 20 h at a shelf temperature of 50 \({}^{\circ}\)C. The most visually appealing samples, along with good organoleptic properties, are depicted in Figure 2. To delve deeper into the impact of freeze-drying on the chemical composition for a comparative assessment, further studies were conducted on samples of berries freeze-dried at a shelf temperature of 50 \({}^{\circ}\)C for durations of 18, 20, and 22 h.  
Figure 1: Sensory evaluation of black current and sea buckthorn berries freeze-dried at shelf temperatures of 35 \({}^{\circ}\)C ((**a**)—black current, (**b**)—sea buckthorn), 40 \({}^{\circ}\)C ((**c**)—black current, (**d**)—sea buckthorn), 45 \({}^{\circ}\)C ((**e**)—black current, (**f**)—sea buckthorn), 50 \({}^{\circ}\)C ((**g**)—black current, (**h**)—sea buckthorn), 55 \({}^{\circ}\)C ((**i**)—black current, (**j**)—sea buckthorn), and sublimation duration of 18, 20, 22, and 24 h. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Name of Berry**} & \multicolumn{2}{c}{**Moisture Content of Fresh Berry, \%**} & \multirow{3}{*}{**Time, h**} & \multicolumn{4}{c}{**Temperature of Shelves, \({}^{\circ}\)C**} \\ \cline{3-8}  & & & **35** & **40** & **45** & **50** & **55** \\ \hline \multirow{3}{*}{Black current} & & 18 & 72.30 \(\pm\) 0.13 & 57.23 \(\pm\) 0.16 & 49.17 \(\pm\) 0.09 & 28.70 \(\pm\) 0.07 & 18.10 \(\pm\) 0.13 \\  & & 20 & 61.43 \(\pm\) 0.24 & 53.23 \(\pm\) 0.18 & 43.30 \(\pm\) 0.20 & 16.27 \(\pm\) 0.22 & 12.40 \(\pm\) 0.07 \\  & & 22 & 42.10 \(\pm\) 0.13 & 35.03 \(\pm\) 0.16 & 33.20 \(\pm\) 0.13 & 10.90 \(\pm\) 0.07 & 7.93 \(\pm\) 0.18 \\  & & 24 & 40.73 \(\pm\) 0.42 & 33.13 \(\pm\) 0.24 & 27.63 \(\pm\) 0.24 & 9.40 \(\pm\) 0.20 & 5.07 \(\pm\) 0.18 \\ \hline \multirow{3}{*}{Sea buckthorn} & & 18 & 83.27 \(\pm\) 0.16 & 71.07 \(\pm\) 0.09 & 62.37 \(\pm\) 0.16 & 36.50 \(\pm\) 0.20 & 9.30 \(\pm\) 0.13 \\  & & 20 & 74.40 \(\pm\) 0.07 & 64.33 \(\pm\) 0.16 & 43.10 \(\pm\) 0.07 & 17.20 \(\pm\) 0.07 & 7.10 \(\pm\) 0.07 \\ \cline{1-1}  & & 22 & 68.77 \(\pm\) 0.16 & 56.47 \(\pm\) 0.22 & 35.43 \(\pm\) 0.16 & 12.23 \(\pm\) 0.18 & 4.97 \(\pm\) 0.04 \\ \cline{1-1}  & & 24 & 53.90 \(\pm\) 0.13 & 47.90 \(\pm\) 0.31 & 29.80 \(\pm\) 0.18 & 10.50 \(\pm\) 0.24 & 3.50 \(\pm\) 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The moisture of fresh and freeze-dried black current and sea buckthorn berries, %.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 2 Experimental Overview
### In Situ Spraying Mortar Material Performance Experiment
#### 2.1.1 Raw Material  
The raw materials used included tap water and H-70 trenchless rehabilitation mortar produced by Wuhan CUG Trenchless Technology Research Institute Co., Ltd. H-70 is a kind of high-strength special mortar with the characteristics of ultra-high strength, wear resistance and corrosion resistance. H-70 is mainly composed of high-grade cement, refined quartz sand with strict gradation, microsilica powder, water-reducing agent, accelerator and polypropylene fiber. The water/material ratio by mass of H-70 is 1:6.
# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 2 Background
### ELM  
ELMs are a type of machine learning algorithm that fall under the umbrella of feedforward neural networks. ELMs were introduced as a simplified and computationally efficient alternative to traditional neural network training methods.  
The key idea behind ELMs is to randomly generate the weights connecting the input layer to the hidden layer, rather than optimizing them through an iterative learning process like gradient descent. This random weight initialization is what makes ELMs \"extreme\".  
Below is a step-by-step overview of how ELMs work:  
1. Input Layer: The ELM begins with an input layer that represents the features or attributes of the input data.
2. Hidden Layer: The hidden layer consists of randomly initialized neurons. Each neuron in the hidden layer has its own set of weights connecting it to the input layer.
3. Activation Function: An activation function is applied to the weighted sum of inputs for each neuron in the hidden layer. Common activation functions used in ELMs include sigmoid, tanh, or rectified linear unit (ReLU). The purpose of the activation function is to introduce nonlinearity into the model.
4. Output Layer: The output layer produces the final predictions or classifications. In classification problems, the output layer typically employs a softmax function to produce probabilities for each class. In regression problems, the output layer can use a linear activation function.
5. Random Weight Initialization: In ELMs, the weights connecting the input layer to the hidden layer are randomly generated. These weights are usually drawn from a uniform distribution within a specific range.
6. Calculate Hidden Layer Outputs: The random weights and the input data are multiplied, and the activation function is applied to the resulting weighted sum for each neuron in the hidden layer. This yields the output of the hidden layer.
7. Solve Output Weights: The output weights from the hidden layer to the output layer are calculated using a linear regression technique. This step involves finding the optimal values of the output weights that minimize the difference between the actual outputs and the desired outputs.
8. Prediction: Once the output weights are determined, the ELM can make predictions on new, unseen data by propagating the input through the network and computing the output using the learned weights.  
The main advantages of ELMs are their simplicity and computational efficiency. They can be particularly useful when dealing with large-scale datasets or time-constrained applications. However, it is worth noting that ELMs do not provide the same level of interpretability as traditional neural networks trained through iterative methods.  
Overall, ELMs offer a different approach to training neural networks by randomly initializing the hidden layer weights and solving the output weights through a linear regression process, making them an attractive option for certain applications.
# Advanced Metal Matrix Nanocomposites
## 4 Dispersion
### Cavitation in Composite Systems  
#### 4.2.1 Cavitation near Solid Surfaces  
In contrast from cavitation in pure liquids (no contamination), the cavitation threshold (at which liquids are stretched by negative pressure comparable to the tensile strength of the liquid) for real liquids is much smaller [21]. In this case, cavitation starts earlier and bubbles nucleate from the presence of submicroscopic air pockets. This process, termed, the heterogeneous nucleation, may also be promoted by the presence of impurities, such as a solid surface. Heterogeneous nucleation at a wettable surface occurs if the surface tension of the solid/vapor interface is smaller than the surface tension of the vapor/liquid interface. For example, under ultrasonic irradiation a bubble may nucleate and form at the solid/liquid interface on the wall of a substrate or on a particle within the liquid [21; 101].  
For modeling the heterogeneous nucleation, the bubble nucleus is usually considered to be a spherical cap on a flat substrate [109; 110]. Thus, the energy E(R) of a growing vapor bubble of radius R can be computed in the same way as that for the homogeneous nucleation case except that the interface-creating work is comprised of two parts: the gas/liquid part and gas/solid part. The energy E(R) includes the work for volume expansion, gas/liquid interface creation and gas/solid interface creation. The critical energy E(R\({}_{\rm C}\)) at a critical radius R\({}_{\rm C}\) can be obtained by [111]:  
\[{\rm E(R_{C})}=\frac{16\pi\gamma^{3}g}{3{\rm(P_{B}-P_{L})}^{2}} \tag{3}\]  
where \(\gamma\) is the surface tension of the liquid (or the vapor/liquid interfacial energy), and g is a shape factor of the gas bubble on the surface. P\({}_{\rm B}\) and P\({}_{\rm L}\) are the respective pressure of bubble interior and liquid. Near the solid surface, the enormous concentration of energy from the collapsing bubble may strongly impact the adjacent materials and structures [112]. This mechanism is, in fact, being used in a variety of industrial applications, such as cleaning, biomedical treatment, deagglomeration, and so on [112; 113].
## 3 Results
### Occurrence of Small Micro- and Nanoparticles in Antarctica
As for soot, it was detected in aerosols on Deception Island ([PERSON] et al., 2019) and in surface snow collected from 28 sites across a transect of 2000 km from the northern tip of Antarctica to the southern Ellsworth Mountains ([PERSON] et al., 2019): concentrations in the 2-4 ng/g range were obtained in the areas surrounding research facilities and popular shore tourist-landing sites, whereas considerably lower amounts (-1 ng/g) were measured in remote areas. The major concern related to the presence of soot in snow is ascribed to the accelerated melting and shrinking processes of the Antarctic snowpack. Concerning plastic particulate pollution, similar assumptions can be made. Even though Antarctica is considered uncontaminated land, the presence of micro- and nanoplastics has often been investigated in the last decade. [PERSON] et al. (2019) collected and analyzed by FT-IR several sediment samples from Terra Nova Bay during the 30 th Antarctic Expedition in the austral summer of 2015. Various plastic particles with lengths in the 0.3-22 mm range made of nine different polymeric materials were detected. Fibers were the most frequent type of small plastic debris detected, with a decreasing concentration farther from the [PERSON] research station. [PERSON] et al. (2019) analyzed sediments from intertidal and nearshore marine locations close to Rothera Research Station, Adelaide Island, in the west of the Antarctic Peninsula. Particles with characteristics similar to those commonly produced by clothes washing were recorded in sediment collected near the station sewage treatment plant outfall. In accordance with the above-mentioned findings, microplastic occurrence was also observed in Antarctic fish, as described by [PERSON] and coworkers (2019): polypropylene, polyamide, and polyethylene microparticles were present in fiber-like form in almost every investigated sample. The same polymers were the predominant plastics detected in sea ice samples collected by [PERSON] et al. (2019) during the austral spring of 2009, 12 km north of Casey Station. An average concentration of 11 particles per liter was measured, highlighting the relationship between their occurrence and anthropogenic settlement. As for snow samples, using FT-IR, [PERSON] et al. (2019) analyzed samples collected from 19 sites in the Ross Island region. Microplastics, mostly polyethylene terephthalate in fiber form, were present in all snow samples at an average concentration of 29 particles per liter. Micro- and nanoplastic pollution in Antarctic snow was also deeply discussed by [PERSON] et al. in a recent review ([PERSON] et al., 2019). The common thread of these studies is the growing human footprint in Antarctica: the continuous and careful monitoring of snow, ice, seawater, and biota is of paramount importance for preserving such a delicate environment.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 4 APT Analysis Model
### Threat Intelligence Model  
Threat intelligence models are frameworks for analyzing and assessing security threats, which help organizations and security teams identify and mitigate APT attacks [74; 75]. The following is an APT analysis process based on a threat intelligence model.  
* Collect intelligence: Collect APT-related intelligence from various sources, such as threat intelligence platforms, open-source intelligence, hacker forums, and social media.
* Analyze intelligence: Analyze the collected intelligence to determine information about APT attack targets, methods, intentions, threats, and other aspects. This can be done using different techniques such as text analysis, statistical analysis, data mining, etc.
* Confirm the attack: Based on the analysis results, determine if there is an APT attack.
* Evaluate the threat: Evaluate the level of threat and scope of impact of the APT attack to develop appropriate response measures.
* Respond and mitigate: Based on the evaluation results, develop and implement the corresponding response and mitigation measures to reduce or eliminate the threat of the APT attack.  
The authors of [75] used cyber threat intelligence (CTI) to analyze the full lifecycle of APT attacks, including collecting, analyzing, and evaluating intelligence and adopting intelligence to defend against APT attacks. The document provided an in-depth understanding and practical guidance through cases to help address APT attacks. Reference [76] used technical threat intelligence (TTI) to analyze APT attacks, providing technical analysis such as identifying attacker TTPs and revealing attack characteristics, patterns, and impactsto provide information for defense and response. [PERSON] et al. [77] introduced the use of open-source cyber threat intelligence (OSCTI) to analyze APT attacks, which not only helped detect and deal with APT attacks but also improved the understanding and analysis capabilities of threat intelligence to protect network security. In [78; 79], structured threat information expression (STIXTM) was used to achieve network threat intelligence and information sharing, addressing the hidden dangers of using a large amount of complex network security information today. The authors [80] used STRIDE to analyze and evaluate security threats faced by systems or applications, helping developers identify and mitigate potential security risks at the design stage and improving the security of systems or applications.  
To wrap up, analyzing APT attacks using a threat intelligence model is an important security task. It can help organizations understand attackers' behavior patterns, technical means, and attack goals and take targeted security measures to protect networks and sensitive data. It can also help organizations identify the types of APT attacks, evaluate the level of threat, and provide response measures. Furthermore, using a threat intelligence model to analyze APT attacks can establish a sound security system, improve security defense levels, and reduce risks and losses.
## 6 Performance Analysis
### Energy Performance
It is lower for the higher hops than the lower ones. For a WSN with a short-range wireless link and a higher number of hops, u will be very low. If the same values of u are used for both the WSN with short-range [1] and long-range [30] wireless links it will have very low (50% to 70%) energy efficiency, which is not practical. To improve the energy efficiency of traditional WSNs, various clustering and optimization algorithms are implemented with the added costs of SN hardware resources and processing energy. Therefore, the energy efficiency shown in Table 1 is calculated using different values for u; u \(=~{}0.1\) for a WSN with a long-range wireless link. For a WSN with a short-range wireless link with hops fewer than 5, u = 0.1, and with hops greater than 5, u \(=~{}0.05\). As the proposed LDAP is designed considering only one hop, u \(=~{}0.5\) is used for the WQM case. Even after optimization cis applied to the traditional WSNs, the proposed LDAP shows an improvement in terms of energy efficiency.
## 3 Experimental Procedure
### Discussion  
The relative error is within 10% by comparing the fatigue life test average value of the CFRP shaft tube under different load factors with the fatigue simulation results. Because the CFRP shaft tube test piece prepared may have some initial defects due to the preparation process and other reasons, the fatigue life obtained by simulation analysis is greater than that obtained by experiment. Ultrasonic scanning results show that during the three-point bending fatigue loading process, the fatigue damage mainly occurs in the area where the loading head contacts the CFRP shaft tube, conforming to the results of simulation analysis of fatigue damage evolution of CFRP shaft tube, and the accuracy of ultrasonic scanning analysis of fatigue damage evolution is verified. The FBG sensors pasted on the surface of the CFRP shaft tube can measure the strain information of each measurement point during the fatigue loading process and sense the stress of each measurement point. This verifies that it is feasible to use FBG sensors to monitor the three-point bending fatigue process of CFRP shaft tubes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Load** \\ **Factor** \\ \end{tabular} & \begin{tabular}{c} **Inflection** \\ **Point** \\ \end{tabular} & \begin{tabular}{c} **Experiment Strain** \\ **(Cycle Times)** \\ \end{tabular} & \begin{tabular}{c} **Simulation** \\ **Displacement** \\ **(Cycle Times)** \\ \end{tabular} & \
\begin{tabular}{c} **Experiment** \\ **Residual Stiffness** \\ **(Cycle Times)** \\ \end{tabular} &
\begin{tabular}{c} **Simulation** \\ **Residual Stiffness** \\ **(Cycle Times)** \\ \end{tabular} \\ \hline \multirow{2}{*}{0.7} & Point 1 & 19,300 & 20,459 & 39,080 & 41,065 \\  & Point 2 & 415,867 & 380,689 & 382,236 & 379,770 \\ \hline \multirow{2}{*}{0.75} & Point 1 & 19,780 & 21,609 & 39,571 & 39,811 \\  & Point 2 & 299,392 & 280,574 & 260,470 & 279,728 \\ \hline \multirow{2}{*}{0.8} & Point 1 & 19,881 & 20,344 & 39,937 & 40,125 \\  & Point 2 & 239,635 & 220,459 & 220,815 & 219,644 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Cycle times of simulation and experimental mechanical performance mutation points under different load factors.  
Figure 20: Strain–cycle number curve of each measuring point of CFRP shaft tube under different load factors.
## Results and Discussion
### Physical Evaluation  
The base for electrical measurements on fish is the fact that the cell membranes in fish muscle tissue are progressively disrupted by autolytic enzymatic degradation and, later, by microbial action, leading to a decrease of electrical resistance and capacity. Therefore, as time progresses, the amount of electric impulse detected will be lower [22]. During the three storage experiments, all fishes were analyzed during harvesting, handling, and storage in order to reduce the variability of equipment readings due to differences in biological condition and physical damage to the tissues. They could present lower or more variable dielectric properties, indicating apparently advanced spoilage [20].  
The changes in dielectric properties during storage on ice of the GrA samples are shown in Figure 4b, which shows the mean of all fishes from the three trials. At the beginning of storage, the TRM means values (>16) of GrA samples indicated \"very fresh fish\", and a relatively high negative correlation with storage time was observed (r\({}^{2}\) = 0.9768).  
The mean TRM values decreased almost linearly in the course of the first 11 days of the trial (16.0-8.0). Between 12 and 20 days, TRM values varied less (from 8.0 to 3.0), creating two distinct regions with different slopes. The first region showed a slope value of \(-\)0.809 (r\({}^{2}\) = 0.992), and the second region presented a value of \(-\)0.375 (r\({}^{2}\) = 0.997). The change in slope is a consequence of the two-phase quality-loss phenomenon explained by two different mechanisms of degradation, i.e., enzymatic autolysis and microbial action [23]. The change of the trend of the slope could indicate the mean value for consumption acceptability [23]. In this study, the slope change occurred between the mean values of 7 and 8, corresponding to 11-12 days of storage. The value of 8 as the limit for consumption is also used for other species [24; 25].  
The proposed value for consumption acceptability is also in agreement with the QIM values, which set sensory rejection between 11 and 12 days of storage. The TRM values were interpreted as follows: absolutely fresh fish was given a score of 16; fish in the commercially useful storage period received a score of 8 or more; scores below 7.5 would be indicative of low-quality products for consumption. The results presented here show that this instrument is useful for quality evaluation during the first 11-12 days of storage.