esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Gibbs sampling
LSA is a promising method for constructing a latent semantic structure in textual data and identifying relevant documents that do not share common words. It also reduces the sizeable term matrix to a smaller one and provides a stable clustering space. LSA differs from standard NLP because it does not use dictionaries, knowledge bases, grammar or syntactic parsers. It accepts as input only raw text that has been split into meaningful paragraphs. In a matrix, LSA represents the text that is  
Figure 10: Graphical model illustration for DDM. described. Each column of the matrix refers to a passage where the word appears, and each word corresponds to one row in the table. The matrix cells show how often the term appears in the paragraph, as shown in Figure 11.  
Word EmbeddingIt is a neural network representation learning approach that be capture syntactic and semantic similarities between words [86]. Word embedding aims to map the words in unlabelled text data to a continuously valued low-dimensional space to capture the similarities between words [87]. It creates latent feature vectors for words to maintain their syntactic and semantic information. The efficiency of word representations relies on implicit relations between words in the corpus. The three common approaches for word embedding learning are Word2 Vec [86], Doc2 Vec and Glove [88]. Owing to vocabulary mismatches, the noisy nature of microblogging data, and a lower number of word co-occurrence in text data, applying these pre-trained word embedding algorithms to short text input is limited [89]. Most word embedding strategies can only learn one vector for each word [90]. Many words, however, have multiple meanings. For example, the word apple can have numerous semantics. When used in the statement 'I like eating apples', it refers to a type of food. It refers to the name of a technological corporation when it appears in the sentence 'We went to the Apple store yesterday'.
# Mechanical Properties of the Functionally Graded Lining for a Deep Buried Subway Tunnel
## 3 Variance analysis of influence factors  
The variance analysis of influence factors of radial displacement is summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Dof**} & \multirow{2}{*}{**Square of Deviance**} & \multirow{2}{*}{**F**} & \multicolumn{4}{c}{**Confidence**} & \multirow{2}{*}{**Significance**} \\ \cline{5-6}  & & & & & **99\%** & & **95\%** & **90\%** \\ \hline \multirow{4}{*}{0} & \(a\) & 2 & 0.270 & 26.061 & 99 & 19 & 9 & ** \\  & \(b\) & 2 & 0.198 & 19.123 & 99 & 19 & 9 & ** \\  & \(\lambda\) & 2 & 4.261 & 410.793 & 99 & 19 & 9 & ** \\  & Error & 2 & 0.010 & & & & & \\ \hline \multirow{4}{*}{15} & \(a\) & 2 & 0.137 & 28.193 & 99 & 19 & 9 & ** \\  & \(b\) & 2 & 0.075 & 15.339 & 99 & 19 & 9 & ** \\ \cline{1-1}  & \(\lambda\) & 2 & 1.449 & 297.711 & 99 & 19 & 9 & ** \\ \cline{1-1}  & Error & 2 & 0.005 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Variance analysis of influence factors of radial displacement.  
Figure 10: Intuitive analysis of influence factors of the radial displacement.Intuitive analysis of influence factors of the radial displacement.  
According to Table 5, when \(\alpha<45^{\circ}\), parameters \(a\) and \(\lambda\) have a very significant influence on the radial displacement of the functional gradient lining, and the significance of \(\lambda\) is greater than that of parameter \(a\). With the increase in \(\alpha\), the significance of parameter \(a\) on the displacement increases gradually, and that of parameter \(b\) on the displacement decreases gradually, but the significance of parameter \(a\) is always greater than that of parameter \(b\). When \(\alpha>45^{\circ}\), parameters \(b\) and \(\lambda\) have a very significant influence on the radial displacement of the functional gradient lining, and the significance of \(\lambda\) is greater than that of parameter \(b\). With the increase in \(\alpha\), the significance of parameter \(a\) on the displacement increases gradually, and that of parameter \(b\) on the displacement decreases gradually, but the significance of parameter \(a\) is always greater than that of parameter \(b\). At \(\alpha=45^{\circ}\), parameters \(a\) and \(b\) have no effect on the radial displacement, but \(\lambda\) has a significant effect on the radial displacement.  
In conclusion, for the functionally graded lining with linear elastic modulus distribution, in the part of \(\alpha<45^{\circ}\), the change in parameter \(a\) has a greater impact on the lining deformation. In the part of \(\alpha>45^{\circ}\), the change in parameter \(b\) has a greater impact on the lining deformation. Therefore, when the maximum deformation occurs at \(0^{\circ}\), the parameter \(a>b\). When the maximum deformation occurs at the position of \(90^{\circ}\), the parameter \(a<b\) can save material costs and reduce the structural deformation.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 4 The Application of GMOs to Agriculture and Food Production  
Following the development of biotechnological tools such as CRISPR-Cas, many research works aim to adapt them to potential industrial applications. Given the growing demand for food resources, agriculture is showing much interest in applying these technologies. The genetic modification of plants can be carried out by two mechanisms (Figure 5): adding genes to provide them with new traits or silencing particular genes by blocking the expression of undesirable features [15]. Since 1996, GM crops have been increasingly grown, with 190 million hectares worldwide in 2016 [14]. Agronomic traits, such as pesticide and virus resistance, herbicide and disease tolerance, and delayed ripening, were the first focus of GE in plants [87]. Most GM crops provide protection against insect herbivory and tolerance to herbicides such as glyphosate [88]. These two individual and combined traits represent >99% of land used to grow GM crops globally [89]. Nevertheless, scientists are interested in employing GE to create and produce plants with desirable characteristics such as superior quality or high nutritional content. For example, this includes crops with an enhanced fatty acid profile and biofortified food, such as Golden Rice. GM can also be useful for producing crops with commercial applications to promote human health, such as edible vaccines or pharmaceutical plants [87]. A review of some of the most relevant developed GM crops can be found below.  
Figure 5: An illustration of the two main genetic modification methods: (1) gene addition and (2) gene silencing. Icons made by [PERSON] and [PERSON] from www.flaticon.com (accessed on 1 December 2021). Adapted from [37].
## 2 Simulation of InSAR Parameters, Power, Coherence and Phase
### Effect on Power  
Figures 12 and 13 illustrate that the InSAR power variations exhibit unique trends as the forward scattering amplitude increases. Employing the previously specified parameters, these figures offer a concise yet illuminating examination of power dynamics in relation to variations in the imaginary component. In Figure 12, as the imaginary component rises from 0.175 to 1.7548, both power and the saturation point demonstrate a steady decline. This suggests that elevated imaginary values lead to a decrease in power, potentially owing to increased absorption or dispersion effects. Outside this range, when the imaginary component escalates from 1.7548 to 2.65, power persists in diminishing, albeit at a decelerated rate. Furthermore, the saturation point stabilizes, indicating that the system attains a threshold beyond which additional fluctuations in the imaginary component exert negligible impact on power levels.  
Figure 13 further elucidates this phenomenon by broadening the spectrum of forward scattering amplitudes. The observations validate that the rate of power reduction decreases at elevated levels, corroborating the notion that saturation effects become increasingly significant as the imaginary component exceeds 1.7548, supporting [PERSON]'s model [20]. This stabilization may be associated with the intrinsic scattering characteristics of the medium or certain interaction mechanisms within the interferometric system.  
Figure 11: Forward scattering amplitude’s effect (1.75 to 2.65) on coherence.  
Figure 10: Forward scattering amplitude’s effect (0.175 to 1.75) on coherence.  
These findings underscore the nonlinear correlation between forward scattering amplitude and InSAR power, highlighting the importance of the imaginary component in regulating power attenuation and saturation effects.
# Birds Detection in Natural Scenes Based on Improved Faster RCNN
## 1 Introduction  
Object detection is an important research direction in the field of computer vision. Tracking and protection of rare birds has always been one of the focuses in the field of animal protection. In recent years, with the mature application of Image Processing [1], Pattern Recognition [2] and Computer Vision Technology [3], it is possible to detect birds unattended in the wild. The Hengshui Lake Wetland Bird Sanctuary project in China uses high-definition cameras to photograph birds, including grey jays, egrets, gulls, and black-billed gulls. Because the camera is far away from the birds, some of the bird images are mainly small-scale birds. Bird subjects often only account for a small part of the whole image, which makes model detection very difficult [4, 5]. In addition, birds live in groups, which leads to the phenomenon of birds blocking each other in some pictures. Although the research on bird detection algorithms in ideal situations has made great progress, bird detection in real natural conditions is affected by many factors. Therefore, the research of bird detection under real natural conditions has an important value.  
Since the great success of AlexNet [6] in the ImageNet [7] image classification competition in 2012, CNN has been widely used in the field of computer vision. In recent years, various new CNN structures have been proposed, which makes the object detection algorithm develop rapidly [8]. At present, object detection algorithms can be divided into two categories, the two-stage object detection algorithm represented by Faster RCNN [9] and the single-stage object detection algorithm represented by YOLO [10] and SSD [11]. In order to solve the problem that it is difficult to identify small birds in natural scenes, this paper proposes an improved Faster RCNN model to detect bird species.  
This paper makes the following contributions:* Convolution features are extracted by ResNet-50 [12], and the feature maps of different convolution layers are Multi-scale fusion [13];
* K-means [14] clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset;
* Soft NMS method [15] is used to solve the problem of occlusion of birds, and multi-scale training is used to improve the generalization ability of the model in the training stage.
## 6 Conclusions  
1. The true stress level of Inconel 718 superalloy decreases with increasing temperature or decreasing strain rate. The true stress varies along with strain highly non-linearly, which represents the non-linear variation of the comprehensive effects of different action mechanisms including work hardening, dynamic recovery, dynamic recrystallization, and the interaction of intermetallic precipitation phases.
2. An ANN model taking the deformation temperature (\(T\)), strain rate (\(\dot{\epsilon}\)) and true strain (\(\epsilon\)) as input variables and the true stress (\(\sigma\)) as output variable was constructed for the compression flow behaviors of Inconel 718 superalloy. The evaluation via the indicators of correlation coefficient (\(R\)), average absolute relative error (\(AARE\)) and relative error (\(\delta\)) revealed that the present ANN model has admirable performance in describing and predicting the flow behaviors.
3. The continuous mapping relationship within the temperature range of 1103-1403 K, the strain rate range of 0.01-10 s\({}^{-1}\), and the strain range of 0.05-0.9 was constructed. Such a constitutive relationship can provide abundant and accurate stress-strain data in extensive scope for the FE model of Inconel 718 superalloy.
4. The simulated isothermal compression tests under the deformation conditions of 1153 K and 0.1 s\({}^{-1}\), 1303 K and 0.1 s\({}^{-1}\), 1153 K and 1 s\({}^{-1}\) and 1253 K and 1 s\({}^{-1}\) were conducted in the FE solver. The comparisons between the simulated stroke-load curves based on the FEM-implanted ANN model and the FEM that imported the training stress-strain data revealed the fact that the FE simulation adopting the FE model-implanted ANN model describing the constitutive relationship model can significantly improve the numerical simulation accuracy of hot forming processes.  
Figure 15: Comparisons between the predicted and experimental stroke-load curves under the deformation conditions of **(a)** 1153 K and 0.1 s\({}^{-1}\); **(b)** 1303 K and 0.1 s\({}^{-1}\); **(c)** 1153 K and 1 s\({}^{-1}\) and **(d)** 1253 K and 1 s\({}^{-1}\).  
**Author Contributions**: [PERSON] and [PERSON] conceived and designed the experiments, performed the experiments and analyzed the data; [PERSON] contributed Program code writing; [PERSON] conducted numerical simulations and paper writing; [PERSON] is responsible for the language check of paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
###### Abstract  
Image deraining ensures the visual quality of images to prompt ship detection for visual perception systems of unmanned surface vessels. However, due to the insufficiency of captured rain streaks features and global information, current image deraining methods often face the issues of rain streaks remaining and image blurring. Consider that the visual perception system captures the same useful information during rainy and hazy days, and only the way in which the image degrades is different. In addition, rainy days are usually accompanied by hazy days at the same time. In this paper, a two-stage and two-channel attention single image deraining network is proposed. Firstly, the subpixel convolution up-sampling module is introduced to increase the range of captured features and improve the image clarity. Secondly, the attention mechanism is integrated with the pyramid multi-scale pooling layer, so that the network can accumulate context information in a local to global way to avoid the loss of global information. In addition, a new composite loss function is designed, in which a regular term loss is introduced to maintain the smoothness and a perceptual loss function is employed to overcome the problem of large differences in the output of the loss function due to outliers. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our model in both quantitative assessments and visual quality by comparing with other state-of-the-art methods. Furthermore, the proposed deraining network is incorporated into the visual perception system and the detection accuracy of ships on rainy seas can be effectively improved.  
image deraining; image dehazing; convolutional neural network; subpixel convolution module +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 State of the Art
The advancements in the data collection and subsequent data interpretation allow for the rapid geometry detection and visualisation of wave propagation diffraction when utilising ultrasound inspection methods [6]. Also, enhancements in sensor technology, robotics and artificial intelligence have made the introduction of robots in the railway industry more appealing [26]. [PERSON] et al. developed an automated robotic inspection system for the application of detecting and measuring cracks in rail tracks [26]. To date, there has only been limited research into the novel concept of integrating sensors within robotic end effectors. One such study that has been investigated evaluated the use of a 2D vision camera and 3D laser line scanner to identify and locate areas requiring repair on a rail track specimen [27]. Within the context of automated sensing, this investigation integrates an ultrasonic detection technology, Epoch(tm) 650, with an automated robotic system, KUKA KR 16, specifically for the enhanced detection of railway track flaws. To provide an informed assessment of the developed system, a comparison was experimentally performed via a direct manual-use system. This work is a starting point for the creation of a knowledge repository for the enhancement of rail safety via automated railway track inspection. The paper is organised as follows. Section 3 presents the experimental procedure. In Section 4, the experimental results are presented. Section 5 presents a discussion on the suitability of the system to detect flaws. Finally, in Section 6, the main conclusions from the conducted study are presented.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Chaotic System
#### 2.1.3 Calculating 2D Logistic Chaos  
The equation of motion of two-dimensional logistic chaos is as follows [11]:  
\[\left\{\begin{array}{l}x_{n+1}=u_{1}x_{n}(1-x_{n})+\lambda_{1}y_{n}^{2}\\ y_{n+1}=u_{2}y_{n}(1-y_{n})+\lambda_{2}(x_{n}^{2}+x_{n}y_{n})\end{array}\right. \tag{3}\]where \(u_{1}\), \(u_{2}\), \(\lambda_{1}\), and \(\lambda_{2}\) are control parameters, \(x_{n}\) and \(y_{n}\) are state variables. When the parameter values are \(2.75<u_{1}\leq 3.4\), \(2.75<u_{2}\leq 3.45\), \(0.15<\lambda_{1}\leq 0.21\), \(0.13<\lambda_{2}\leq 0.15\), and the initial values are \(0<x_{1}\leq 1\), \(0<y_{1}\leq 1\), 2D logistic chaos is in a chaotic state.  
The parameters are set to \(u_{2}=3.1\), \(\lambda_{1}=0.18\), \(\lambda_{2}=0.14\), the initial values are set to \(x_{1}=0.123\), \(y_{1}=0.567\) to draw the bifurcation diagram of the system, and then \(u_{1}=3.0\) is set to draw the attractor phase diagram, as shown in Figures 2 and 3 respectively. The chaotic sequences are randomly distributed in the value interval, and there are fewer period-doubling bifurcations. The chaotic characteristics are outstanding.
## 4 Experimental Analysis and Results
### Experimental Setup
On ANERcorp, the proposed model achieves an F1-score of 93.0%, significantly outperforming the best Arabic-specific baseline, XLM-R + CRF, by 2.4 percentage points and surpassing LUKE and Seq2 Seq Span-based NER by even larger margins. Similarly, on ACE 2005, which contains complex nested entities, the proposed model achieves an F1-score of 89.6%, surpassing GlobalPointer (the previous state-of-the-art), LUKE, and Seq2 Seq Span-based NER, highlighting the effectiveness of the proposed framework in capturing Arabic's rich morphology and nested structures. These results validate the contributions of our novel components, such as the Hybrid Feature Fusion Layer and Compound Span Representation Layer, confirming the importance of combining Arabic-specific linguistic features with flexible, generalized modeling capabilities. The consistent improvement across both datasets emphasizes the robustness, adaptability, and generalizability of the proposed framework. As shown in Figure 3 and Table 4, the proposed framework consistently outperforms all baselines across the three datasets: ANERcorp, ACE 2005, and the custom biomedical dataset. The improvements are particularly significant in the ACE 2005 dataset, which contains complex nested entities, demonstrating the proposed model's superior capability to capture hierarchical structures in Arabic text. Compared to LUKE and Seq2 Seq Span-based NER, the proposed framework achieves a notable performance gain in F1-score, confirming the importance of incorporating Arabic-specific morphological features and lexicon filtering mechanisms alongside generalized modeling capabilities.
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
## 5 Conclusions  
This study developed a modified soil-pile interface model to investigate the dynamic behavior of piles in liquefaction-susceptible slopes. The proposed model was validated by comparing it with centrifuge test results, and a parametric study was performed, varying the slope inclination and the amplitude of the input motion. The following conclusions were drawn:  
1. A soil-pile interface model that considered liquefaction effects was proposed. The proposed interface model modified the conventional model by reducing the friction angle of the normal spring by up to 5% of the initial value as the excess pore pressure increased. The simulation results based on conventional models considerably overestimated or underestimated the experimental residual bending moment, while the proposed model showed good agreement with the experimental results throughout the entire time history, not just the residual value.
2. Based on the validated numerical model, parametric studies on the effects of slope inclination and the amplitude of input motions were conducted. As the slope inclination increased, the area of sliding soil increased, causing a larger bending moment in the piles. Lateral displacement of the soils and a bending moment of the piles occurred with sufficient input amplitude to induce full liquefaction of the model ground. It was observed that input amplitudes larger than the sufficient input amplitude to trigger the failure of a liquefied slope did not increase the bending moment of the pile any further.  
Conceptualization, S.-R.K.; Methodology, B.-S.Y.; Formal analysis, B.-S.Y.; Data curation, B.-S.Y. and N.X.T.; Writing-original draft, B.-S.Y.; Writing-review & editing, N.X.T. and S.-R.K.; Supervision, S.-R.K. All authors have read and agreed to the published version of the manuscript.  
Research for this paper was carried out under the KICT Research Program (project no. 20230400-001, Database construction for ground liquefaction assessment based on AI technology) funded by the Ministry of Science and ICT.  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository.  
The authors declare no conflict of interest.  
Figure 17: Residual bending moment profiles at (**a**) group-down and (**b**) group-up.
## 3 Mitigation Scheme for SAR
### Adaptive Filtering  
The adaptive filtering methods manage to construct a suitable filter and separate the interference in a certain domain, which can be realized in time, frequency, spatial, polarization, or by multi- domain joint analysis.