esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design of the Impromptu Mobile Social Karaoke System
### Social Music Sharing Group Management and User Devices  
The process of sharing and streaming of group information by the server and devices is shown in Figure 6. The server plays the role of managing users and groups, creating a music group according to the client's request, and sequentially selecting the next person to sing. The user device can be set to the personal mode for listening and singing alone, but in the social mode, the user participates in a karaoke group and uses a karaoke service based on linkage with the server.  
In particular, in the social group mode, users and servers operate group control and music sharing services through communication. First, the user selects the social group mode, designates a singer and a song, and thereafter requests the group to participate in the session. In the singer mode, the search for songs, receiving the number of members in the user's group, singing, and requesting scores are conducted, and in the listening user mode, standby, receiving the number of members in the user's group, listening to songs, and evaluation are conducted. The server adds clients based on the group name and delivers this information to the members of the group. When group creation is completed, a person who will sing a song is selected and a message is delivered to the relevant user to be notified. When a song begins on the user's device, the server starts a voice streaming channel and streams the user's voice to other users. Thereafter, when the user has finished singing, the server requests an evaluation of the group users, calculates the average of the obtained scores, and delivers it to the person who sang the song. Then, the server checks whether all users in the group have sung, and if there is any user who has not sung, the server selects the next person who will sing in the group to sing, and, if not, the server terminates the session as all users in the group have sung.  
Figure 5: The structure for mobile social karaoke system.  
The karaoke system comprises several components, such as the user interface (UI), application logic, communication components, data storage cloud, external interfaces, security elements, monitoring system, and managing system. The role of the user interface (UI) is to facilitate easy interaction between the users and the karaoke system. It provides a panel for users to input commands and collect information. The communication components facilitate an easy exchange procedure between the different parts of the system. The storage cloud stores the data and manages their integrity, retransmission, and tenacity. The external interface property allows the karaoke system to connect with external services that extend the functionality of the system through external resources. Security elements protect against unauthorized access, data breaches, and vulnerabilities.
## 2 Related Work
For the first stage, they employ self-supervision for the audio data, generating a low-resolution image using a model adapted from PixelCNN [30]. Subsequently, a finer image is generated through a GAN conditioned on the low-resolution image and the audio features. For cross-model tasks, they achieved over 25% and 21% accuracy using diverse data from AudioSet [31] and VGGSound [32] datasets, respectively. Notably, unlike our approach, their method is partially self-supervised. Additionally, for the second stage, the GAN is also trained employing a perceptual loss using high-level feature maps from a pre-trained VGG network [33] which is a supervised model. [PERSON] et al. [34] present a S2I solution based on visual discrete representation learning. They employ self-supervision to train both a visual encoder and an image generator which is part of a GAN conditioned on the representations obtained from the visual encoder. They then train an audio encoder using contrastive loss to align the audio embedding to the anchored visual latent space. Their model's training and evaluation utilize the VGGSound [32] and VGGAS [35] datasets. Using the audio embeddings from the aligned encoder and a frozen generator, their system returns more than 83% of images with the correct sound-emitting source depicted. In contrast to our approach, their training is self-supervised, which, as discussed earlier, is prone to producing class-biased results. Furthermore, the studies conducted by [PERSON] et al. [36] and [PERSON] et al. [37] provide comprehensive surveys on state-of-the-art audiovisual-correlation learning methodologies.
## 4 Discussion of Test Results
### Displacement Ductility  
Displacement ductility reflects the deformation capacity of the specimens without significant degradation during the period from the yield state to the ultimate state, and it is related to the failure types of the specimens. There were two states used in calculation of the displacement ductility: the yield state and the ultimate state. However, the yield state of the specimens is not easy to determine because of the complexity of the failure mode and the inconsistency of the computing method. In this study, the energy equivalence method is adopted to calculate the yield state of the specimen, and the calculation method, shown in Figure 9, is also used in the literature [12; 27]. Therefore, the displacement ductility \(\mu\) of the specimens can be defined as the ratio of the ultimate displacement \(\Delta_{u}\) corresponding to the ultimate state to the yield displacement \(\Delta_{y}\) corresponding to the yield state.  
Figure 8: Comparison of envelope curves of specimens: (**a**) overall comparison, (**b**) positive and negative direction comparison.  
Table 4 provides a summary of the calculation results of the displacement ductility of the specimens. The term \(\overline{\mu}\) represents the average values of the displacement ductility \(\mu\) in both the positive and negative direction. It can be observed that the displacement ductility varied within a range of values between 3.34 and 6.92, which is clearly related to the section type of the specimens. This can be explained by the different failure modes corresponding to the crack patterns. A flexure and ductility failure occurred in the specimens with a rectangular section, while a shear and brittle failure occurred in the specimens with a T-shape section. With regard to the influence of the prestressing technology, it improves the displacement ductility of the specimens, with an increase of about 49% for the specimens with a rectangular section and 56% for the specimens with a T-shape section. This improvement is also proved by the crack patterns of the specimens. Thin and densely covered cracks occurred in the BMFT specimen, while several wider main cracks appeared in the BMFT specimen, as shown in Figure 6. This demonstrates that the prestressing technology improves the integrity of the specimens by applying stress to the masonry wall in advance, and it ensures that the specimens have a better deformation potential in terms of their seismic design.
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results
### Results
#### 4.3.1 2018 Data Science Bowl Dataset Comparison Results  
Nucleus segmentation plays an important role in biomedical image analysis. The 2018 Data Science Bowl dataset results are shown in Table 1, and the qualitative comparative results of the six models are shown in Figure 4. Compared with TransU-Net, MSDA-Net obtained an improvement of 0.7% Acc, 4.7% IoU, 0.7% Dice, 1.2% Precision, and Infer-T reduced the time by 1.32 s on the 2018 Data Science Bowl dataset. Compared with other models, the boundary details of our model are clearer.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 5 Experiments
### Time Cost and Model Size Comparison  
The training cost of all ensemble models is captured and analyzed in addition to the trained model size. Please note that the training time recorded in Tables 3-10 is captured using a MacBook Pro 2015 model with 8 GB RAM on a Core i5. As a general trend, it is observed that increasing the number of packets lengthens the training process. Among the five ensemble models, ET takes the shortest raining time, whereas GBT takes the longest. It is interesting to note that LGM has the third shortest training duration after ET and RF with the highest performance. For the case of model size, GBT has the trained model with the smallest size of 582 KB and ET has the largest trained model size of 15.9 MB. The LGBM has a trained model size of 1.6 MB and XGBT has only 636 KB as its trained model size. Furthermore, the prediction is monitored, and it is discovered that the testing time is insignificant, with the RF model taking the shortest time of 0.0065 s and the ET ensemble model taking the longest time of 0.025. LGBM takes 0.017 s to test 100 input samples. We emphasize that training and testing times are affected by a variety of factors, including hardware configuration, number of input instances, and features. Nonetheless, this analysis provides an intuitive understanding of the training and testing time, as well as the size of ensemble models.
## 3 Results and Discussion
### Model Validation
#### 3.1.3 Methanol Synthesis  
The flowsheet result of methanol synthesis in Aspen Plus is presented in Appendix D. The methanol synthesis simulation results are compared with the data obtained from the industrial methanol synthesis results [37]. Results obtained from the simulation and the corresponding industrial data are presented in Table 7. As the comparison between experimental and simulated results shows, the model produces results close to the reference data. The error between simulated results and experimental results is 0.7%. It is worth noting the discrepancy between the content of CO and CO\({}_{2}\); this is due to the synthetic pathway of methanol in the practical experiment where both of CO and CO\({}_{2}\) participate into the methanol synthesis reaction. However, in the simulation, CO participates into the water-gas shift reaction instead of methanol synthesis. There is a difference between the reference and simulated pressure. This is because the syngas volume is reduced during methanol synthesis process, resulting in the decrease of the partial pressure of H\({}_{2}\), CO, and CO\({}_{2}\). As a consequence, the side of the equilibrium with fewer moles is becoming more unfavorable [38]. Therefore, a higher reaction pressure is needed in the actual reaction to facilitate methanol synthesis. However, in the simulation, the methanol synthesis reactor pressure is fixed at 40 bar. The simulated and experimental results of the methanol yield are approximately similar. Therefore, the model can be considered as a valid model to simulate the methanol synthesis process.
## 3 Methods
### The Process of the ADMTCF
#### 3.1.2 The Strategy for Adding and Updating Templates  
There are three key points in the strategy for adding and updating templates. The first key point is that the first template image is the target image of the first frame. The second key point is that before reading the next image frame, we calculate the similarity between all template images and the current target image. The formulas for calculating the similarity are as Equations (1)-(3). \(I\) is the current object image. \(\overline{T}_{xy}\) is the pixel average of the current object image. \(T\) is the current template image. \(\overline{T}_{xy}\) is the pixel average of the current template image. \(R(x,y)\) is the similarity. The closer the value of \(R(x,y)\) is to 1, the more similar the current object image and current template image are.  
\[\overline{I}_{xy}=\frac{1}{mn}\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}I (x+i,y+j) \tag{1}\]  
\[\overline{T}_{xy}=\frac{1}{mn}\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}T (x,y) \tag{2}\]  
\[R(x,y)=\frac{\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}[I(x+i,y+j)- \overline{I}_{xy}][T(i,j)\overline{T}_{xy}]}{\sqrt{\sum\limits_{i=0}^{m-1}\sum \limits_{j=0}^{n-1}[I(x+i,y+j)-\overline{I}_{xy}]^{2}\sum\limits_{i=0}^{m-1} \sum\limits_{j=0}^{n-1}[T(i,j)-\overline{T}_{xy}]^{2}}} \tag{3}\]  
If the similarity between the currently selected template image and the target image is less than 80%, we add the current target image as a new template image until there are four sets of template images. If there are already four sets of template images, the template image most similar to the current target image will be replaced with the current target image. The third key point is that we set the template image that is most similar to the current target image as the template image of the tracking target in the next frame in each frame. Due to the strategy for adding and updating templates, the features of the template used in the  
Figure 3: There are three operations of pre-processing of ADMTCF: (**a**) the first one is to select the object in the first frame; (**b**) the second one is to convert the object’s image from the RGB color domain to the HSV color domain; (**c**) the third one is to carry out LBP encoding.  
tracking process maintain a high similarity with the current object image. Furthermore, the four sets of template images memorize the object images at different times. When the object image is deformed, the ADMTCF will select the most similar template among the four sets of templates as the tracking feature to achieve anti-deformation.
## 1 Introduction
Therefore, we proposed a novel loss differentiation algorithm based on heterogeneous ensemble learning. The algorithm employs ROTT along with average ROTTs, ROTT deviations, and IAT statistics (including maximums, minimums, and averages) as features for loss differentiation. The Relief method, combined with mutual information techniques, is utilized for feature selection. Q statistics and double failure (DF) value are used for the selection of individual classifiers in ensemble learning, including least-squares support vector machine (LSSVM), DT, logistic regression (LR), and KNN algorithm. The integration strategy of the heterogeneous ensemble learning model is designed using the stochastic gradient descent (SGD) method, with dynamically adjusted voting weight vectors for each classifier to enhance classification accuracy. The rest of this paper is organized as follows. We analyze the related works on loss differentiation algorithms from threshold determination-based loss differentiation and learning algorithm-based loss differentiation methods in Section 2. In Section 3, we employ the Relief and mutual information method to construct a feature set that distinguishes packet loss in LEO satellite networks. In Section 4, we propose a heterogeneous ensemble learning model for discriminating packet loss in satellite networks. In Section 5, the loss differentiation method based on heterogeneous ensemble learning (LDM-HEL), loss detection method (LDM)-satellite [11], and LLD method [12] are used as comparative experiments for simulation, which proves the superiority of the algorithm proposed in this paper. Finally, Section 6 concludes the paper.
## 3 Parameter Analysis of Object Determination  
### Correlation Coefficient  
Correlation coefficients (denoted by r) are used to measure the degree of correlation between the data and models. Usually, the object streak center occupies one or two lines of pixels, and we call the two types of object streaks Ob 1 and Ob 2, as shown in Figure 2a,b. We take the position of each pixel in the red dotted lines as the longitudinal center to conduct model fitting, and the correlation coefficients are shown in Figure 2c,d. Six pixels in different positions of the two objects, including object centers, object edges and image backgrounds, are focused on, and their data and Gaussian models are shown in Figure 3.  
Figure 2: Two types of object streaks and correlation coefficients between data and models in different positions (pixel a: center of Ob 1, pixel b: image background 1, pixel c: longitudinal edge of Ob 1, pixel d: image background 2, pixel e: center of Ob 2, pixel f: longitudinal edge of Ob 2). (a) Ob 1. (b) Ob 2. (c) Correlation coefficients of Ob 1. (d) Correlation coefficients of Ob 2.  
It can be seen that the correlation coefficients are more than 0.9 from the center of Ob 1 to the horizontal edges, which means that there is a high correlation. However, the correlation at the background 1 is weak and less than 0.5. Line1 and Line3 are the two vertical edges of Ob 1, and the Gaussian models are slightly mismatched with the data, whose correlation coefficients are usually less than 0.6 but more than that of the image background 1. However, there is a background noise at image background 2 where the correlation coefficient is also high. The centers of Ob 2 are Line2 and Line3, and most of the correlation coefficients are between 0.6 and 0.9. Line1 and Line4 belong to the two vertical edges, and the Gaussian models are heavily mismatched with the data, so there is no correlation between the two.  
If selected correlation coefficient thresholdings are low, objects with weak Gaussian morphology can be recognized well, but lots of false alarms appear, just like pixel d. Contrarily, if the thresholdings are high, the dim objects with low model correlation cannot be recognized. Therefore, the correlation coefficient thresholding is set to 0.5. On the premise that the objects and stars can be recognized, a few false alarms of background noise are considered to remove.
## 1 Introduction
The expression of anomalies has been reported according to ethnic and regional lines and the patterns of hypodontia and its frequency found to be variable for different populations [18; 19; 20; 21; 22; 23; 24; 25]. For example, the prevalence of congenitally missing teeth is lower in American blacks compared with American whites. The hypodontia reported prevalence of permanent teeth in Caucasian populations ranges between 2.3% and 11.3% [18], and less than 1% in the deciduous dentition [19; 20]. A prevalence of 2.6% has been reported for Arab orthodontic patients in Israel [21], 4.6% in Turkish orthodontic patients [22], 4.5% in the Norwegian population [23], 8.5% in Japanese orthodontic patients [24], and up to 14.7% in Hungarian orthodontic patients [25]. There are different opinions regarding the most frequently missing permanent tooth, excluding third molars. Some have concluded that this is the maxillary lateral incisor [22; 26], while others claim that agenesis of the mandibular second premolar has a higher prevalence [20; 27]. Currently, no extensive investigation on permanent tooth agenesis in the Israeli orthodontic population has been reported. The present study was aimed to conduct a retrospective wide survey in order:  
1. To evaluate the prevalence and patterns of permanent tooth agenesis and compare bilateral with unilateral agenesis in orthodontic individuals in a university clinic. 2. To demonstrate the clinical management of agenesis of maxillary lateral incisors specifically, because of esthetic concerns, and that of missing second premolars.
## 2 Principle of the Miniature Michelson Interferometer (MMI)
### Displacement Measurement of the Nanopositioning Stage  
Due to the space limit of the nanopositioning stage, a miniature Michelson interferometer (MMI) kit is developed for displacement feedback, as shown in Figure 3a. A collimated laser beam emitted from a laser diode is divided into a measurement beam and a reference beam by PBS1, as shown in Figure 1. The reference beam passes through a quarter waveplate (QWP2) and is reflected back by a fixed mirror (mirror 2). The measurement beam passes through QWP1 and is reflected back by the moving mirror (mirror 3). It should be noted that the polarization state of the measurement and reference beams are changed by 90\({}^{\circ}\) after passing QWP2 and QWP1 twice, which prevents the reflected beams from entering back into the laser diode. Those two reflected beams are combined at PBS1 and converted into left and right circularly polarized beams after passing through QWP3. The interference signals with a phase shift of 90\({}^{\circ}\) due to the phase control by two PBSs (PBS2 and PBS3) and a BS (BS3) are detected by four photodetectors (PD1-PD4), as shown in Figure 3b. The motion of the moving mirror 3 causes an optical path difference between the two reflected beams so as to produce interference. Analyzed by the Jones matrix, the intensity of each photodetector can be expressed as follows:  
\[I_{PD1}=I_{0}(1-\cos(\frac{2\pi\cdot\Delta y}{\lambda})), \tag{2}\]  
\[I_{PD2}=I_{0}(1+\cos(\frac{2\pi\cdot\Delta y}{\lambda})), \tag{3}\]  
Figure 2: Optical layout of the miniature autocollimator kit.  
\[I_{PD3} = I_{0}(1+\sin(\frac{2\pi\cdot\Delta y}{\lambda})), \tag{4}\] \[I_{PD4} = I_{0}(1-\sin(\frac{2\pi\cdot\Delta y}{\lambda})), \tag{5}\]  
where \(I_{0}\) and \(\lambda\) are the intensity and the wavelength of the laser beam, respectively, and \(\Delta y\) is the optical path difference of two reflected beams. The signal direct current (DC) offset can be eliminated by subtracting Equation (2) from Equation (3) for the cosine term, and Equation (5) from Equation (4) for the sine term. The phase change can be obtained by  
\[\Delta\Phi=\tan^{-1}(\frac{I_{PD1}-I_{PD2}}{I_{PD3}-I_{PD4}})=\frac{2\pi\cdot \Delta y}{\lambda}. \tag{6}\]  
Based on the optical design of the MMI kit, when mirror 3 has a motion of \(d\), the optical path difference of the two reflected beams \(\Delta z\) will be \(2d\). Thus, the displacement \(d\) of the moving mirror 3 can be calculated by  
\[d=\frac{\lambda}{2}(N+\frac{\Phi_{initial}-\Phi_{final}}{2\pi}), \tag{7}\]  
where \(N\) is the number of sinusoidal waves, and \(\Phi_{initial}\) and \(\Phi_{final}\) are the exact phases of initial and final incomplete wave cycles, respectively. It can be seen from Equation (7) that the interference waveform has the period of one-half laser wavelength. Thus, the accuracy of the laser wavelength is significantly important in the MMI kit.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 1 Introduction
SREM is designed by placing waypoints along the perimeter of an ellipse, allowing UAVs to move in straight lines between each waypoint. By defining the major axis as the relay axis, nodes are naturally aligned along this path. This design enables the analysis of communication metrics in a manner well-suited for relay scenarios and adaptable to diverse environments. The main contributions of this paper are as follows: (1) the proposal of a mobility model for relay network experiments; (2) empirical analysis of node spatial distribution using the Monte Carlo method; and (3) performance comparison with existing models, demonstrating that SREM is more suitable for relay network experiments. This paper is structured into six sections. Section 2 classifies existing mobility models used in FANET experiments into four categories and compares their respective strengths and weaknesses. Section 3 describes the design and implementation of the proposed SREM model, along with its potential applications. Section 4 analyzes the spatial distribution of nodes using numerical analysis and the Monte Carlo method. Section 5 evaluates the communication performance of SREM in comparison with existing models using the NS-3 simulator. Finally, Section 6 presents a discussion based on the overall findings and outlines future research directions by addressing the structural limitations of the current model.