esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sample Size Calculation  
The sample size calculation was conducted using G*Power (version 3.1.4), a software tool that employs established statistical methods. To detect meaningful differences between the two groups, a statistical power of 0.80 (80%) was targeted, along with a significance level (alpha) of 0.05. The anticipated effect size was estimated based on the study by [PERSON] et al. [22]. Given the complexity and multifaceted nature of the study's assessments, a larger sample size was deemed necessary to account for potential variations and confounding factors. Additionally, the inclusion of 60 subjects in each group would enhance the generalizability of the study's findings and increase the statistical robustness of the results. By meticulously considering these factors and employing a sample size of 60 subjects in each group, the study aimed to minimize the likelihood of type II errors, ensuring that meaningful differences and associations, if present, would be accurately detected and reported.
## 2 Concepts and Theory
### Bragg Scattering  
[PERSON] introduced a structure function to describe the structure of atmospheric turbulence [21; 22]. Based on [PERSON]'s theory, [PERSON] provided a link between \(\eta\), \(\lambda\), and \(C_{n}^{2}\) (the refractive index structure constant), which is called Bragg scattering as shown in Equation (3) [3]:  
\[\eta=0.38C_{n}^{2}\lambda^{-1/3} \tag{3}\]  
Bragg scattering is caused by turbulent inhomogeneities with sizes around one-half of \(\lambda\)[23; 24; 25; 26; 27]. \(Z\) in respect of Bragg scattering is given in Equation (4) [2]:  
\[Z=\frac{0.38C_{n}^{2}\lambda^{11/3}}{\pi^{5}K^{2}} \tag{4}\]  
Equation (4) establishes the relationship between the reflectivity factor \(Z\) and the atmospheric refractive index structure constant \(C_{n}^{2}\). By using Equation (4), the structure constant of the atmosphere can be calculated when Bragg scattering is caused by turbulence. The \(Z\) value of clear-air echoes is usually distributed between 10 dBZ and 20 dBZ, but the calculated value of \(C_{n}^{2}\) is larger than \(10^{-11}\) m\({}^{-2/3}\), which is far from the observable maximum value [2; 19]. This is why some scholars believe that clear-air echoes cannot be caused by Bragg scattering.  
Notably, Equation (4), like Equation (2), is highly simplified, and the effects of numerous factors need to be considered when using radar reflectivity factors to accurately calculate the structural constants. However, since the \(C_{n}^{2}\) values calculated from the \(Z\) values of clear-air echoes are several magnitudes away from the actual observed \(C_{n}^{2}\), [PERSON]'s view is overwhelmingly accepted. As a result, this has led a considerable number of scholars to deny, based on [PERSON]'s view, that clear-air echoes are produced by Bragg scattering.
## 3 Experimental Test-Rig and Data Acquisition
### Experimental Test-Rig Design  
The test-rig that was designed in this paper consisted of a two-stage single-acting reciprocating air compressor, sensors, and data acquisition system, as shown in Figure 6. Among them, the acceleration vibration sensor has a sensitivity of 50 pC/ms\({}^{-2}\), a frequency response of 0.2 Hz \(\sim\) 1.5 kHz, and it is mounted on the root of piston cylinder body, as shown in the A view of Figure 6. The current clamp has an accuracy of 1.5\(\%\pm\) 5 mv and a conversion ratio of 10 mv/A, which is clamped to the power supply wire of the AC motor. The experimental speed of the air compressor crankshaft is 900 r/min, and the maximum pressure that the RC can provide is 1.6 MPa.
## 4 Case Study
### Discussion  
According to Table 14, it is evident that the ranking of the importance of the ECs obtained by the QFD model proposed in this paper is consistent with the rankings obtained by traditional QFD, fuzzy QFD, and linguistic QFD methods. This to some extent verifies the accuracy and effectiveness of the QFD method proposed in this paper. However, compared with traditional QFD and its improved methods, the QFD method proposed in this paper has the following advantages:  
* The use of HFLTSs can effectively reflect various hesitant and uncertain linguistic expressions of QFD team members, avoiding information loss. This enables QFD team members to use more flexible and rich language to express their subjective judgments more realistically;
* An improved BWM method based on hesitant fuzzy language is proposed, in which the use of linguistic variables for reference comparison of alternatives and criteria is more valuable than the use of clear values in the decision-making process, and combined with 2-tuple, the evaluation information is ultimately expressed in the form of interval numbers, which has a higher comparative consistency, and thus the weights obtained are more reliable;
* The improved TOPSIS method combining hesitant fuzzy language and 2-tuple is proposed. Compared with the calculation of the one-dimensional numerical value, the algorithm combined with a 2-tuple better reflects the real decision-making opinions of experts, which makes the ranking of the importance of engineering characteristics more accurate and credible. And it is conducive to the correct decision-making for the planning objectives of engineering characteristics.  
However, it is necessary to acknowledge the research limitations of this work. Firstly, the use of hesitant fuzzy language may involve a certain degree of subjectivity and semantic uncertainty. Differences in the understanding of fuzzy terms among QFD team members may exist. Therefore, when constructing HFLTSs, it is important to consider these subjective differences, which could lead to model inconsistency. Secondly, the BWM method involves subjective judgments in weight allocation, and expert judgments may influence the final results, particularly in weight distribution, introducing subjectivity and inconsistency. Similarly, TOPSIS may introduce a degree of subjectivity when defining ideal and negative-ideal solutions. Thirdly, the validation of this method is based on a comparative analysis of selected cases in market segmentation assessment, which may not fully capture the complexity and diversity of real-world scenarios. Dealing with hesitant fuzzy language in real-world situations may require more complex mathematical calculations and models, potentially increasing computational complexity. This could pose a challenge, especially in large-scale and real-time applications, given limited resources.
## 5 Experiments
### Ablation Studies
The observations can be explained as follows: replacing one hot vector of relationship labels with contextual embeddings from knowledge graph helps the model to correctly predict the relationship class of the entities in the sentence. This positive result verifies the effectiveness of knowledge label incorporation and answers research question RQ1.2. From Table 5, the accuracy decreases when we concatenate random embeddings of the knowledge graph and descriptions instead of utilizing pretrained graph embeddings and on-task description embeddings, indicating the validity of the information contained in the knowledge labels. We further utilize the random embedding of knowledge graphs and descriptions, respectively. The results show that both the relationship description and topology help extract more informative context and beneficial clues for entity embeddings for classification, with the latter being more advantageous. Using meaningful description embeddings alone only slightly improves the original network without any embeddings, suggesting that the topology may provide more instructional cues. This is because topology is more consistent with the characteristics of a particular relationship. Replacing pretrained graph embeddings and description embeddings via different representation methods can achieve comparable performance. All these results answer RQ1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P** & **R** & **F1** \\ \hline KLA-SRE & 52.71 & 77.94 & 62.89 \\ \hline w/o relation Demb & 52.06 & 76.67 & 62.01 \\ w/o relation Gemb & 51.56 & 75.83 & 61.38 \\ w/o relation Gemb and relation Demb & 49.26 & 73.11 & 58.86 \\ w/o relation Gemb and entity Gemb & 50.34 & 74.25 & 60.00 \\ w/o relation Demb and entity Gemb & 50.52 & 74.10 & 60.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on validation set of NYT. w/o, Gemb, and Demb are the abbreviations of without, graph embeddings, and description embeddings. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & \(\mathbf{Acc.}\pm\mathbf{ci.}\) & **Std.** \\ \hline KLA-SRE & \(81.1\pm 1.1\) & 5.3 \\ \hline - Random Embeddings of Gemb and Demb & \(71.1\pm 1.0\) & 6.1 \\ - Random Embeddings of Gemb & \(75.4\pm 1.2\) & 7.3 \\ - Random Embeddings of Demb & \(79.2\pm 1.6\) & 7.3 \\ \hline - RESCAL [39] Gemb & \(80.5\pm 1.3\) & 6.7 \\ - DisMult [40] Gemb & \(80.9\pm 1.6\) & 6.9 \\ \hline - Glove Demb & \(79.8\pm 1.7\) & 7.2 \\ \hline - no Gemb and Demb & \(69.4\pm 1.8\) & 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on the FewRel dataset. Gemb and Demb are the abbreviations of graph embeddings and description embeddings.
## Appendix A Nmi
### Correlation Plot  
Figure 11 explains the correlation plot for the NMI metric, the ensemble model with multiple annotations on the x-axis, and the remaining models on the y-axis. When comparing the MA and SA models, the points in the Knee dataset are scattered around the line (similar to the GDC plot). The SI) dataset shows two patterns: first, the horizontal points in the bottom left, meaning the MA model outperforms the SA model; second, the remaining points are close to the line, meaning agreement between the models. The Bland-Altman plot shows the same conclusion when the MA scored +12% average bias compared to the SA. For the Lung and Heart datasets, the plot shows points that are very close to the scatter line, which indicates strong agreement between the models. Also, in the Lung dataset, the plot shows a robust positive linear correlation, meaning that both models make the same predictions across different ranges of data points. Furthermore, when the MA model was compared with MC versions, opposite to the GDC metric in the Lung dataset, there were no cluster points in the top right; instead, the points were widely scattered on the right side of the plot. This phenomenon is correct for all MC models, which indicates that the MA model scored higher NMI compared to the MC models (Bottom right is the area where MC prediction is declining). The box plot confirms this result in which the MA model's average and IQR were higher than the MC versions. For the SI) dataset, both the MA and MC models score low NMI, yet there is a high consistency among the models. For the Heart dataset, the MA model scored higher NMI compared to MC versions; also, points are spread more widely compared to the GDC metric, especially for higher dropout rates in which there is conflict between the models for a subset of samples. Finally, comparing the MA and regression models for the Heart dataset revealed that the MA model performed better, yet there is a positive correlation indicating consistency between the models. For the Lung dataset, the points are widely spread on the right side of the plot, which suggests the MA model scored high NMI for multiple samples in which regression scores the low NMI. Again, similar to MC models, both models score low yet consistent results for the SI) dataset.
## 2 Related Works
### Other Languages  
The authors in [25] use text preprocessing for event detection (ED) and sentiment analysis (SA). Their method extracts events and sentiments, labels events with frequent sentiments, and combines Network Analysis and ML to identify topics and determine sentiments. Evaluation uses Logistic Regression (LR) and Support Vector Machines (SVMs), with LR being faster. In study [26], the authors propose a sentiment analysis method using topic-document embeddings. They preprocess text, create word vectors, compute term weights, and extract main themes. Sentiment is detected using Logistic Regression (LOGREG) and Long Short-Term Memory (LSTM) models. Evaluation with accuracy, precision, and recall enhances understanding of sentiment in text data. [PERSON] et al. [27] propose the DocTopic2 Vec model for sentiment analysis, combining Doc2 Vec and Topic Modeling. They create word embeddings using Word2 Vec, fastText, and GloVe using game reviews, enriching Doc2 Vec with local context. Topic2 Vec embeddings are generated using Latent Dirichlet allocation (LDA), Non-Negative Matrix Factorization (NMF), and Latent Semantic Indexing (LSI). The DocTopic2 Vec model integrates these embeddings for improved accuracy. They design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Networks, which outperform traditional models in benchmarks.  
The authors in [28] propose Event Detection Sentiment Analysis Ensemble (EDSA-Ensemble), an architecture that enhances sentiment analysis and event detection on social media by combining these techniques. EDSA-Ensemble improves sentiment polarity detection for current events using Information Diffusion techniques, which account for both the period and topics of events to provide a comprehensive understanding of online social network dynamics. [PERSON] et al. [29] propose the Aspect Term Extraction and Sentiment Analysis using a heterogeneous ensemble learning model with BERT and BART (ATESA-BART) model, an ensemble learning approach for Aspect-Based Sentiment Analysis (ABSA). By dividing ABSA into Aspect Term Extraction and Aspect Term Sentiment Analysis, and using argmax multi-class classification with six transformer-based learners, the model combines predictions from multiple base learners. Initial experiments show that ATESA-B/ERT outperforms state-of-the-art solutions, effectively handling multiple aspects within reviews.
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results
### Validation
#### 3.4.2 Effect of the Homogenization Procedure  
Table 4 expresses the similarity values of the homogenized August and September images with respect to the July image. An increase in _SSIM_ was clearly observed (values ranged from 0.584 to 0.892), thus favoring the hypothesis that the procedure was able to slightly dilute some differences between acquisition dates. The increase in SSIM was due to the increase in the similarities in contrast (\(c\)), correlation (\(s\)) and luminance (\(l\)) in all bands. As before, the Near Infrared band (I) had the lowest similarity values on both dates. Analogous values and behavior were observed when comparing August and September homogenized images (data not shown), with _SSIM_ values of 0.846, 0.837 and 0.615, respectively, for the RGI bands.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 5 Results
### Results of the Models  
Table 5 presents the best results from each type of neural network we investigated using our un-augmented corpus and compares them with other works. The BiLSTM model performed the best of all systems that we developed in terms of precision and F\({}_{0.5}\). However, SimpleRNN performed better than LSTM in terms of precision and F\({}_{0.5}\). LSTM outperformed the other models in terms of recall. Unlike LSTM, SimpleRNN is not suitable for processing long sequences, such as text [39]. Therefore, we expected that LSTM would perform better in our experiments. However, one reason that SimpleRNN actually performed better than LSTM might be because most of the sentences in the training set are short, where the longest was only 17 words long. Additionally, because the task was considered simple (i.e., sequence labeling) and the training set was small, a simpler network was more suitable. LSTM could most probably perform better on more complex tasks and datasets [39].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Precision** & **Recall** & **F\({}_{0.5}\)** \\ \hline Baseline & 75.6 & 91.96 & 78.39 \\ SimpleRNN & 78.09 & 83.95 & 79.62 \\ LSTM & 76.21 & **93.80** & 79.16 \\ BiLSTM & **80.74** & 85.73 & **81.55** \\ \hline
[26] & 34.5 & 80.0 & - \\
[1] & 46.1 & 28.5 & 41.1 \\
[47] & 79.10 & 21.19 & 51.14 \\
[48] & 54.41 & 91.79 & - \\
[49] & 89.71 & 62.48 & 82.52 \\
[50] & 66.47 & 54.11 & 63.57 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation Results.  
Additionally, the fact that LSTM outperformed the others in recall is because LSTM was better than other models with regard to not considering correct words as mistakes. So, it was better at recognizing that correct words were in fact correct.  
LSTM, BiLSTM, and SimpleRNN all outperformed the baseline in precision and F0.5. However, the baseline had higher recall than SimpleRNN and BiLSTM.  
Table 6 shows the hyperparameter settings for each of the models in Table 5. We noticed that BiLSTM only needed one layer of size 25, the smallest sized model of the three, to perform the best. Implying that BiLSTM was more powerful in being able to select the most important structure in the input data to model while being small in size.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Dataset  
The dataset chosen for this study is Kvasir-Capsule, a large gastrointestinal dataset containing 47,238 labeled images and 117 videos [9]. The dataset was compiled through endoscopy images collected during examinations at a Norwegian hospital. Sample images can be seen in Figure 2. The dataset consists of a total of 14 significantly imbalanced classes, as indicated in Table 3. Of the 47,238 images, 34,338 belong to one class (Normal Mucosa) while the smallest class contains only 10 (Ampulla of Vater). Imbalance this high needs to be addressed for optimal model training. As mentioned in Section 2, [PERSON] et al. [19] were able to obtain great results with using only 20% of the Kvasir-Capsule dataset. Similarly, [PERSON] et al. [16] only used the top 9 classes of Kvasir-Capsule and then continued to extract only 500 images from each class. Based on this, under-sampling techniques were applied in this work as well to address these highly imbalanced data. A training subset was created with a total 4500 images, with 500 images from each of the top 9 classes (Normal Clean Mucosa, Ileococcal Valve, Reduced Mucosal View, Pylorus, Angiectasia, Ulcer, Foreign Body, Lymphangiectasia, and Erosion), as seen in Table 3.  
Figure 2: Sample images from all 14 classes of the Kvasir-Capsule dataset.
## 3 Data and Methods  
### 3D Geological Models  
Conventional fault interpretation relies on manual observation, which is time consuming and labor intensive in areas with complex structures. Ant tracking, an innovative approach that has emerged in recent years, can effectively identify horizon discontinuities [21; 22]. The concept originates from the foraging behavior of ants in nature. Ants choose routes based on pheromone concentration. As they pass, the pheromone concentration on the path increases, but it also evaporates over time. The formula for updating pheromone concentration on each path is as follows:  
\[\tau_{ij}(t+n)=(1-\rho)\tau_{ij}(t)+\Delta\tau_{ij} \tag{1}\]  
\[\Delta\tau_{ij}=\sum_{k=1}^{m}\Delta\tau_{ij}^{k} \tag{2}\]  
where \(\rho\) (\(0<\rho<1\)) is the evaporation coefficient of pheromones on the path; 1-\(\rho\) is the maintenance coefficient of pheromones on the path; \(\Delta\tau_{ij}^{k}\) is the amount of pheromones left by ant k from node i to node j (If ant k did not follow this route, then \(\Delta\tau_{ij}^{k}=0\)).  
Figure 3: Stratigraphic, lithological systems, and depositional environments in the study area [20].  
The principle of ant tracking involves using electronic ants to search for and label discontinuous values in a region, attracting neighboring ants to track along the outliers [23; 24]. Mutation values represent faults and fractures, and using this tracking marker intuitively identifies faults and fractures. There are two typical types of ant tracking: aggressive ant tracking and passive ant tracking. Aggressive ant tracking can identify more discontinuities, while passive ant tracking identifies fewer fractures and faults [25]. The outcomes of ant tracking are generally controlled by six parameters, determined based on geological backgrounds (Table 1). This method not only allows for subtle interpretation of fractures and faults but also effectively eliminates the subjective influence of manual fault interpretation. Considering the practicality and applicability of ant tracking, this technology was used in this research to assist in identifying faults and constructing detailed geological models of the entire study area (Figure 4).
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 5 Actuation Methods
### Piezoelectric Actuators  
Piezoelectric actuators are based on the characteristic properties of certain ceramic materials to induce reversible mechanical strain or force in the presence of an electric field applied to the material, and vice versa. In the presence of an electric field, the polarized molecules of the piezoelectric material tend to align themselves, thereby deforming the material structure. The magnitude of the force or strain generated in the material depends upon the geometry and electromechanical properties of the material. Among the various piezoelectric materials available, aluminum nitride (AlN) and lead zirconate titanate (PZT) are the most used materials in MEMS devices [106].  
Depending upon the application, it is possible to use a single disc, plate, or tubular structure of piezoelectric material. A cantilever-shaped piezoelectric actuator is another promising structure used to obtain a bending motion where two different materials are combined in a cantilever structure [107].  
Piezoelectric actuators are widely used in optical scanners, and fiber optic devices. Numerous micromirrors actuated using bulk piezoelectric actuators, and piezoelectric bender actuators are listed in [93]. It is possible to achieve 2-D motion by using a tube with split electrodes. The tubular piezoelectric actuator is mainly used to excite an optical fiber acting as a cantilever at the resonant frequency. By modulating the drive voltage in two pairs of electrodes, the desired scanning pattern from the fiber tip can be achieved. A large number of endoscopes are fabricated using this technique [84; 91; 108; 109; 110].  
Some researchers developed 2-D fiber optic scanning devices using a single pair of piezoelectric electrodes. In such cases, the actuation in a direction perpendicular to that generated by the actuator is obtained using the asymmetry of the optical fiber [63; 111]. [PERSON] et al. developed an endoscope obtaining the 2-D actuation of a fiber using two piezoelectric ceramic cantilever benders [112]. [PERSON] et al. developed a tubular piezoelectric-based 1.06-mm-diameter endoscopic catheter. It was one of the earliest scanning fiber endoscopes used for fluorescence imaging. A schematic diagram of the developed SFE is shown in Figure 17[64; 3].  
Figure 16: Schematic diagram of a thermally driven micro-electro-mechanical systems (MEMS) scanner (used with the permission of [69] © The Optical Society).