esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Guide Vane Scheduling Method Based on the Kinematic Model and Dual Schedule Curves
## 3 Analytical Model of the VGV Link Mechanism
### Parameters for Adjusting the Link Mechanism  
The 3-stage VGVs in this research are mechanically coupled to one actuation system, thus, the stroke command for generating a VIGV angle does not satisfy the vane angle schedule of the remaining VGV stages (first-stage and second-stage VGVs in this case). Therefore, the link mechanisms of the first-stage and second-stage VGVs need to be modified to satisfy the VGV schedule by adjusting their link parameters, such as bell crank length (\(L_{b}\)) and vertical link length (\(L_{c}\)).  
The bell crank length, an adjusting parameter, represents the distance between the rotation center of the bell crank and the mounting point of the vertical link. Adjusting the bell crank length changes  
Figure 5: Verification results of the link mechanism model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number in Figure 4** & **Joint** & **Parts** & **DOFs** \\ \hline \⃝1 & Spherical & Bell Crank, Vertical Link & 3 \\ \⃝2 & Revolute & Bell Crank, Ground & 1 \\ \⃝3 & Slide + Revolute & Clevis, Ground & 2 \\ \⃝4 & Spherical & Vertical Link, Clevis & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definitions of joints in ADAMS model.  
the relations of the vane angle and actuator stroke, as shown in Figure 6a. Increasing the bell crank length makes the rotational radius of the pivot point of the bell crank and vertical link longer with respect to the rotational center of the bell crank. Then, larger vane angle changes can be obtained with identical bell crank rotational motion (increase in the slope of the curve in Figure 6a).  
Changing the vertical link length changes the distance from the pivot point of the bell crank and vertical link to the pivot point of the unison ring and vertical link. Therefore, it changes the offset value of the curve representing the vane angle according to the actuator stroke (shown in Figure 6b).  
The curve of the vane angle shown in Figure 6 can be fitted to a linear regression model that consists of curve slope value (\(C_{slope}\)) and offset value (\(C_{offset}\)) as following equation.  
\[\theta_{l}\ =\ C_{slope}\times L_{x}+C_{offset} \tag{18}\]  
An increase of 1 mm in the bell crank causes 2% increase in the curve slope value, and an increase of 1 mm in the vertical link causes 1.6 degree decrease in the offset value, respectively. Therefore, we can obtain a solution of link adjusting parameters to satisfy the coupled rest of VGV stages (in this study, VGV1 and VGV2) schedule requirements when stroke commands have been already presented.
###### Abstract  
Cervical cancer affects a large portion of the female population, making the prediction of this disease using Machine Learning (ML) of utmost importance. ML algorithms can be integrated into complex, intelligent, agent-based systems that can offer decision support to resident medical doctors or even experienced medical doctors. For instance, an experienced medical doctor may diagnose a case but need expert support that related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influence on predictions made using ML algorithms. Cancer data, in general, and cervical cancer data, in particular, are frequently imbalanced. For this study, we chose a messy, real-life cervical cancer dataset available in the Kaggle repository that includes large amounts of missing and noisy values. To identify the best imbalanced technique for this medical dataset, the performances of eleven important resampling methods are compared, combined with the following state-of-the-art ML models that are frequently applied in predictive healthcare research: K-Nearest Neighbors (KNN) (with \(k\) values of 2 and 3), binary Logistic Regression (bLR), and Random Forest (RF). The studied resampling methods include seven undersampling methods and four oversampling methods. For this dataset, the imbalance ratio was 12.73, with a 95% confidence interval ranging from 9.23% to 16.22%. The obtained results show that resampling methods help improve the classification ability of prediction models applied to cervical cancer data. The applied oversampling techniques for handling imbalanced data generally outperformed the undersampling methods. The average balanced accuracy for oversampling was 77.44%, compared to 62.28% for undersampling. When detecting the minority class, oversampling achieved an average score of 60.80%, while undersampling scored 41.36%. The logistic regression classifier had the greatest impact on balanced techniques, while random forest achieved promising performance, even before applying balancing techniques. Initially, KNN2 outperformed KNN3 across all metrics, including balanced accuracy, for which KNN2 achieved 53.57%, compared to 52.71% for KNN3. However, after applying oversampling techniques, KNN3 significantly improved its balanced accuracy to 73.78%, while that of KNN2 increased to 63.89%. Additionally, KNN3 outperformed KNN2 in minority class performance, scoring 55.72% compared to KNN2's 33.93%.  
cervical cancer; sampling methods; imbalanced datasets; classification methods; prediction methods; K-nearest neighbors; logistic regression; random forest
## 3 Results
### Influence of Sociodemographic Data on Emotion Recognition for the Depression Group (H4)  
#### 3.4.1 Influence of Age  
The influence of age on the depression group was studied from two different perspectives. The first analysis considered age to be a discontinuous variable, and the second one considered it to be a continuous variable.  
In the first analysis, the age of the depression group was converted into a discontinuous variable by grouping the sample into three age categories: young (\(n=9\)), middle-aged (\(n=27\)), and elderly (\(n=18\)). The mean scores for each group were 84.19%, 78.63%, and 61.97%, and the mean reaction time that each group took to provide a response to the DVFs presented was 4.00 s, 4.07 s, and 6.04 s, respectively. The influence of the age group on emotion recognition was determined by the Kruskal-Wallis test, which showed significant differences among the three age groups for recognition scores (\(X^{2}(2)=15.487\), \(p<0.001\)) and reaction times (\(X^{2}(2)=14.091,\ p=0.001\)). Regarding recognition scores, significant differences were found between elderly and young (\(p=0.002\)) and elderly and middle-aged (\(p=0.004\)), being that the elderly group had the lowest recognition scores in both cases. The reaction times' comparison showed similar results, with the highest reaction times for the elderly group and significant differences in relation to the young (\(p=0.013\)) and middle-aged (\(p=0.002\)) groups.  
The second analysis of the influence of age as a continuous variable on emotion recognition was carried out via [PERSON]'s rank correlation coefficient. This analysis showed an inverse correlation between age and recognition scores (\(r=-0.628\), \(p<0.001\)) and a direct correlation between age and reaction times (\(r=-0.501\), \(p<0.001\)). Therefore, the older the participant, the more difficult it becomes to identify emotions, and the longer the reaction time becomes.
# CommuSpotter: Scene Text Spotting with Multi-Task Communication
## 4 Experiments
### Inference Speed  
The inference speed is compared with previous approaches, as shown in Table 7. Not all recent studies provide inference time statistics. Compared to MANGO [7], our method is a little slower but with better performance. As mentioned above, our method is more efficient at training and can be easily used in practical applications without complex modules and expensive data labeling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Detection**} & \multicolumn{2}{c}{**End-to-End**} \\ \cline{2-6}  & **P** & **R** & **F** & **None** & **Full** \\ \hline TextSnake [49] & 82.7 & 74.5 & 78.4 & - & - \\ Mask TextSpotter v1 [13] & 69.0 & 55.0 & 61.3 & 52.9 & 71.8 \\ TextNet [22] & 68.2 & 59.5 & 63.5 & 54.0 & - \\ Mask TextSpotter v2 [19] & 81.8 & 75.4 & 78.5 & 65.3 & 77.4 \\ Mask TextSpotter v3 [6] & - & - & - & 71.2 & 78.4 \\ Boundary [27] & 85.2 & 83.5 & 84.3 & 65.0 & 76.1 \\ CRAFTS [23] & 89.5 & 85.4 & 87.4 & 78.7 & - \\ ABCNet [8] & - & - & - & 64.2 & 75.7 \\ ABCNet v2 [26] & - & - & 87.0 & 70.4 & 78.1 \\ PAN++ [50] & - & - & - & 68.6 & 78.6 \\ MANGO [7] & - & - & - & 71.7 & 82.6 \\ SwinTextSpotter-R [14] & - & - & 87.2 & 72.4 & 83.0 \\ SwinTextSpotter [14] & - & - & 88.0 & 74.3 & 84.1 \\ GLASS [48] & - & - & - & 76.6 & 83.0 \\ SRSTS [29] & **92.0** & 83.0 & 87.2 & **78.8** & **86.3** \\ TTS [15] & - & - & - & 75.6 & 84.4 \\ \hline Ours & 90.4 & **91.5** & **90.1** & 73.0 & 83.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results on the Total-Text (TT) dataset. The detection results are measured by precision (P), recall (R), and F-score (F). The recognition F-scores include evaluation without lexicon as “None” and with lexicon as “Full”.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **ICDAR2015** & **Total-Text** \\ \hline Mask TextSpotter v2 [19] & 3.1 & - \\ Mask TextSpotter v3 [6] & 2.5 & - \\ ABCNet [8] & - & 6.9 \\ MANGO [7] & 4.3 & 4.3 \\ Ours & 2.9 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Frames per second (FPS) comparison on different inference datasets.
## 4 Results
The model was used to create a neural network that predicted the target based on multiple input values. The model was tuned with different parameters to generate the best result. One of them is known as random number seed. It was used to ensure repeatability across runs of the same experiment. The model was initially tuned with 6 random seeds, and the predicted accuracy was recorded as 79.0% while using the information gain-based feature selection method. It was observed that the model predicted its best results with an accuracy of 82.9% when the number of random seeds was 10 while utilising the relief-F based feature selection method. Complete results for the artificial neural network classifier with different feature selection methods are presented in Table 8.  
Among the five classifiers utilised in this study, the artificial neural network outperformed and obtained the highest accuracy of 82.9% while utilising the relief-F based feature selection technique, as shown in Figure 3. It was observed that ANN also performed efficiently while utilising other feature selection techniques. The performance of ANN was also good while evaluating other performance metrics. Figure 3: Performance comparison of models based on accuracy with utilisation of various feature selection techniques. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Feature Selection Method**} & \multicolumn{2}{c}{**Ensemble Size**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**ROC**} & \multirow{2}{*}{**RMSE**} & \multicolumn{4}{c}{**F-Measure**} \\ \cline{4-11} \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Size**} & & & & & & **Low** & **Medium** & **High** & **Weighted Average** \\ \hline \multirow{3}{*}{Information Gain} & 5 & 61.0 & 0.54 & 0.60 & 0.60 & 0.45 & 0.18 & 0.67 & 0.31 & 0.56 \\  & 10 & 62.5 & 0.60 & 0.61 & 0.59 & 0.46 & 0.25 & 0.74 & 0.28 & 0.61 \\  & **15** & **63.2** & **0.59** & **0.64** & **0.56** & **0.42** & **0.19** & **0.72** & **0.30** & **0.60** \\ \hline \multirow{3}{*}{Wrapper Method} & 5 & 66.0 & 0.56 & 0.65 & 0.64 & 0.38 & 0.23 & 0.71 & 0.26 & 0.58 \\  & 10 & 67.4 & 0.55 & 0.66 & 0.65 & 0.38 & 0.25 & 0.77 & 0.23 & 0.58 \\  & **15** & **68.5** & **0.52** & **0.52** & **0.70** & **0.40** & **0.19** & **0.63** & **0.21** & **0.52** \\ \hline \multirow{3}{*}{Relef-F} & 5 & 68.9 & 0.67 & 0.99 & 0.47 & 0.40 & 0.24 & 0.93 & 0.35 & 0.80 \\  & 10 & 70.5 & 0.62 & 0.69 & 0.62 & 0.38 & 0.25 & 0.72 & 0.31 & 0.62 \\  & **15** & **73.0** & **0.69** & **0.50** & **0.78** & **0.38** & **0.26** & **0.95** & **0.37** & **0.81** \\ \hline \multirow{3}{*}{LASSO} & 5 & **61.3** & **0.54** & **0.61** & **0.64** & **0.43** & **0.21** & **0.66** & **0.28** & **0.55** \\  & 10 & 58.4 & 0.51 & 0.58 & 0.52 & 0.40 & 0.18 & 0.63 & 0.25 & 0.52 \\  & 15 & 60.7 & 0.53 & 0.60 & 0.56 & 0.42 & 0.19 & 0.64 &
## 3 Best Relay Selection Design and Transmission Rate Analysis
### Best Relay Selection Design  
To achieve a better BRT, we select the SR\({}_{\text{b}}\) for signal transmission by comparing each node's SNR and global channel gain. The specific best relay selection process of the BMR transmission can be expressed as follows:  
**Step 1.** Let the received SNR threshold at the \(SR_{k}\) be \(\gamma_{th}\), the received SNR from the SU\({}_{1}\) to \(SR_{k}\) be \(\gamma_{1k}\), and the received SNR from the SU\({}_{2}\) to \(SR_{k}\) be \(\gamma_{2k}\). Then, by comparing \(\gamma_{1k}\) and \(\gamma_{2k}\) with \(\gamma_{th}\), the relay nodes that satisfy \(\{\gamma_{1k},\gamma_{2k}\}>\gamma_{th}\) will be the effective relay nodes. And these effective relay nodes can form a candidate relay set M\((SR_{k})\).  
**Step 2.** Let the channel gains from the SU\({}_{1}\) and SU\({}_{2}\) to \(SR_{k}\) be \(f_{k}^{2}\) and \(g_{k}^{2}\), respectively. Then, the relay nodes that satisfy the following conditions will be the reliable relay nodes. And these reliable relay nodes can form a candidate relay set N\((SR_{k})\).  
\[\text{N}(SR_{k})=\left\{\begin{aligned} A&=\left( \text{max}\Big{(}f_{k}^{2}\Big{)},\text{max}\Big{(}g_{k}^{2}\Big{)}\right) \text{ or}\\ B&=\left(\text{max}\Big{(}f_{k}^{2}\Big{)},g_{k}^{2} \text{ sub-maximum}\right)\text{ or}\\ C&=\left(\text{max}\Big{(}g_{k}^{2}\Big{)},f_{k}^{2} \text{ sub-maximum}\right)\end{aligned}\right\}\]  
**Step 3.** Let the relay node that is in both M\((SR_{k})\) and N\((SR_{k})\) be the best relay node. And these best relay nodes can form a best relay set B\((\text{SR}_{\text{b}})\).  
**Step 4.** With the B\((\text{SR}_{\text{b}})\), it can further have the following situations:  
(1) If B\((\text{SR}_{\text{b}})=\varnothing\), return to step 1 to re-select;  
(2) If B\((\text{SR}_{\text{b}})\
eq\varnothing\) and there is only a single relay node in B\((\text{SR}_{\text{b}})\), this node will be the best relay node SR\({}_{\text{b}}\);  
(3) If B\((\text{SR}_{\text{b}})\
eq\varnothing\) and there are multiple relay nodes in B\((\text{SR}_{\text{b}})\), the node with the maximum SNR in step 1 will be the best relay node SR\({}_{\text{b}}\).
## 3 Materials and Methods
### Methodology for Skin Lesion Detection Using Web-Based Tools for Machine Learning Models
These capabilities enable researchers and developers to pre-process and extract relevant features from medical images effectively, improving the performance of classifiers;
* **Integration with Other MATLAB Toolboxes:** MATLAB offers various toolboxes, such as the Statistics and Machine Learning Toolbox, which provide additional algorithms and functionality for machine learning tasks. This integration allows for seamless use of different algorithms and techniques to enhance classifier performance;
* **Customization and Flexibility:** MATLAB provides a flexible environment for developing and customizing classifiers. Users can easily modify and adapt algorithms, fine-tune parameters, and incorporate domain-specific knowledge into their classifiers. This flexibility is particularly valuable in medical imaging, where specific requirements and constraints often exist;
* **Extensive Documentation and Community Support:** MATLAB has a vast community of users and developers, which provides access to comprehensive documentation, tutorials, and forums. This support network facilitates learning, troubleshooting, and sharing knowledge, accelerating the development process and addressing challenges encountered in classifier development for medical images;
* **Visualization and Analysis Tools:** MATLAB offers robust visualization tools that enable users to visualize and analyze medical images and classifier outputs. These tools assist in understanding the behavior of the classifier, validating results, and gaining insights from the classification process;
* **Deployment Options:** MATLAB provides various options for deploying classifiers developed within the MATLAB environment. These include generating standalone executables, creating web applications, or integrating classifiers into other softwareframeworks or platforms. This flexibility allows for the seamless integration of MATLAB classifiers into existing medical imaging workflows or systems;
* **Research and Academic Support**: MATLAB is widely used in the research and academic communities, and many medical imaging studies and papers employ MATLAB for developing classifiers. This popularity leads to a wealth of research resources, benchmark databases, and code examples that can serve as references or starting points for developing classifiers in medical imaging. The workflow used to evaluate the creation of models for the Google Teachable Machine, Azure Machine Learning, Google Vertex AI, and SalesForce Einstein is in Figure 16.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Technical Considerations
#### 3.3.5 IoT-Based Network Bandwidth  
The use of BC has grown in popularity, as has the number of nodes. Only some nodes are coupled in a P2P architectural network, and the present network can meet bandwidth needs. In the future, the number of nodes in BC will rapidly expand. Because of the enormous number of interconnections and broadcasts between nodes, broadcast storms can form quickly, consuming too much network capacity. As a result, the IoT network's performance may be impaired, if not completely shut down [76]. Edge computing can handle dispersed node communication. The challenge of network bandwidth needs for large-scale deployment of BCs is expected to be overcome in IoT [76].
## 8 Experimental Results
### Discussion and Design Notes  
From the validation with users, the following conclusions can be drawn. The evaluation of Tasks 1-3 has been done using the _high-fidelity_ representation based on volume in order to offer as much detail as possible to the user. Therefore, the related result will be conditioned by both the representation and the hardware limitations. First, regarding the _interpretation_ of 3D figures in the mid-air context, the concept of _inspecting_ seems not to have the same meaning for all participants. Some participants understood that they had to put their hand over the Ultrahaptics and to move it up and down, forming a mental image of the 3D figure formed by 2D horizontal slices. However, other users tried to use their hand to explore the faces of the figures. With all the participants sharing the same background and the same task statement, this discrepancy attracts attention. When developing future _Harptic_ systems (Haptic + AR) in which users have to explore the shape of a 3D model, in order to improve the process, it is advisable to previously indicate to the user the correct way to proceed. Second, almost all participants stated during Task 1 that haptic feedback itself was not enough to guess 3D models. However, during the course of Task 2, it has been shown that participants actually performed well during the divination process. A set of figures that seem more easily interpretable by users has been identified (_torus, house, cube, cone, and arrow_). Third, we have found that support through haptic feedback on relatively complicated AR tasks (such as establishing a certain height based on visual estimates) helps during the process. When working with interaction metaphors (as in the case of the Simon's buttons), it seems not useful to imitate an operation of the real world through haptic feedback. Therefore, when developing future metaphors, they should focus on user perception and not on replicating physical procedures known by the user. Throughout the testing process, the limitations imposed by the hardware have been present. Some users, for example, had problem because they moved their hand so fast or out of range that the integrated Leap Motion was not able to properly detect it. When taking into consideration some specific parameters, such as inspection times or resizing task accuracy, some users performed differently than others. It is difficult to determine if this is due to a single cause (e.g., previous experience on AR, inspection mode, etc.), but it is true that there may be hidden factors affecting the users acceptance.
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 3 Results and Discussions
### The Influence of the Ringer Solution on the Cyclic voltammetry Curve, CV, of the Zr2.5 Nb Alloy  
The results obtained from a measurement of cyclic voltammetry provide us with qualitative and quantitative information of the induced corrosive process, in different electrochemical conditions [21].  
Figure 6: Potentiodynamic polarization diagram for Zr2.5 Nb alloy immersed in [PERSON]’s solution plotted with a potential scanning rate of 3 mV/s.  
Cyclic voltammetry is initiated in the negative direction (\(-1.5\) V relative to the reference electrode) to the positive direction (+1.5 V relative to Ag/AgCl) with a scanning speed of 3 mV/s. The scanning direction is then reversed after the anodic current has reached the corresponding value at +1.5 V relative to Ag/AgCl to form a complete cycle. The recorded diagram for the Zr2.5 Nb alloy immersed in [PERSON]'s solution is shown linearly in Figure 7a and on the logarithmic scale of the current density in Figure 7b.  
According to Figure 7a, from the diagram obtained in the case of the Zr2.5 Nb sample immersed in [PERSON]'s artificial solution, it can be seen that, at the beginning of the transpassive segment, the current density shows a large increase from a potential of E = +1023 \(\pm\) 45 mV vs. Ag/AgCl and continues to increase until the potential is reversed at a current density of \(i\) = 534 \(\pm\) 11 \(\mu\)A cm\({}^{-2}\).  
The direction of the reverse potential shows a trajectory above the upward curve, indicating a high susceptibility to localized corrosion and a wide hysteresis that reveals a pitting corrosion.  
This phenomenon is mainly due to the abundant quantitative chloride content of the test solution, Ringer, which corresponds to the aggressive environment in the human body for all implant biomaterials [17].  
From Figure 7b, it can be seen that the current density recorded for the Zr2.5 Nb alloy immersed in [PERSON]'s solution has a high value and the pitting corrosion potential is situated at E = +1023 \(\pm\) 45 mV.  
The size of the specific hysteresis visible in the transpassive (anodic) part of the drawn curve (Figure 7b) is very large. This behavior reveals a lower pitting corrosion resistance for the Zr2.5 Nb alloy immersed in [PERSON]'s solution approximately 41 h after immersion.