esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Dental Anomalies: General Prevalence, Gender, the Number of Anomalies and the Tenth Involved  
In the current study, we found that (150) 29.24% of the subjects in our sample, displayed at least one dental anomaly. The most common dental anomaly was tooth impaction (13.5%), followed by missing teeth (7.4%) and ectopic tooth eruption (5.8%), whereas fusion and transmigration were found to be the least common, with a prevalence of 0.2% each. The prevalence of dental anomalies was significantly greater in males (\(p<0.001\)). These results are similar to the findings reported among the Jewish population in Israel [31], where no significant differences were found between males and females. In another study carried out among the Turkish population, it was found that the general prevalence of dental anomalies was 14.3%, whereas tooth impaction was 5.5% which was the most common anomaly thus is similar to our study, with no significant difference between males and females [41]. However, another study conducted in Turkey reported that impaction  
Figure 5: Percentage of dental anomalies among men and women according to relative marriages (All percentages were rounded off to the nearest integer).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **With Consanguinity** & **Without Consanguinity** & **Total** \\ \hline
**With a dental anomaly** & 45 & 105 & 150 \\ \hline
**Without a dental anomaly** & 34 & 329 & 363 \\ \hline
**Total** & 79 & 434 & 513 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The prevalence of dental anomalies by consanguinity groups.  
(46.51%) was the most widespread anomaly [42]. In addition, a study carried out among the Indian population presented a higher prevalence in men than in women, and the overall prevalence of dental anomalies was 57.43%, where hyperdontia was the most common anomaly (0.1-3.8%) [43]. Another study that was conducted in India to determine the prevalence of impacted maxillary amines found that males were more commonly affected by impacted maxillary amines than were females [44].  
Two studies of the Arab population in Israel found a prevalence of 3.7% of impacted amines, with higher prevalence in females [29] as well as 1.1% of missing maxillary lateral incisors [30] Other anomalies were not investigated earlier in the Arab population in Israel. As can be seen, different populations demonstrate different results. The awareness of the high prevalence of dental anomalies in general, and impactions in particular, in our population should be highly considered in early diagnosis, preventive, and interceptive treatments.  
Although the sample size and the sampling study technique affects the results of different studies, variations in the prevalence of dental anomalies among different studies may indicate the involvement of genetic, racial, and environmental factors.
## 6 Simulation Results and Discussion  
For the evaluation of proposed protocols, different simulations have been performed in terms of DA, EC in the DA of the nodes and EC of the network during transmission of DPs with affordable E2E delay. The proposed protocols outperformed in alleviating the void holes and interference (experiences during packets transmission). Therefore, comparative analysis has been performed to validate the performance of the proposed protocols with other existing state-of-the-art proactive and reactive routing protocols.  
In the next subsections, simulation parameters are discussed. Afterwards, the simulation results are presented and elaborated with detailed reasoning.
## 3 Results
### By-Products and Volatile Profiles  
The data for the main by-products and the volatile compounds in PW and PWL are reported in Table 3. The results of PW trials indicated that _L. thermotolerans_ and _K. unispora_ in mixed fermentation increased some aroma compounds compared to pure culture. _L. thermotolerans_/_S. cerevisiae_ significantly increased the n-propanol, isobutanol, and amylic alcohol, while _K. unispora_/_S. cerevisiae_ significantly increased ethyl butyrate in comparison with the other trials.  
Figure 2: Principal component analysis of amino acid content of final beers. (**a**) Pils worn (PW): the variance explained by principal component analysis (PCA) analysis is PC 1 55.8% X-axis and PC 2 18.7% Y-axis. (**b**) Pils worn + lentil (PWL): the variance explained by principal component analysis (PCA) analysis is PC 1 88.4% X-axis and PC 2 10.3% Y-axis.  
A different trend was shown in PWL, where the _K. unispora_ pure culture increased ethyl acetate, while isobutanol and isoamyl acetate were comparable to that exhibited by _K. unispora_/_S. cerevisiae_, and _L. thermotolerans_/_S. cerevisiae_, respectively. Moreover, _L. thermotolerans_/_S. cerevisiae_ led to a significant increase in \(\beta\)-phenyl ethanol compared to the other trials. _L. thermotolerans_ showed a significant enhancement of acetaldehyde content, while _S. cerevisiae_ was characterized by ethyl butyrate, ethyl acetate, and amylic and isoamylic alcohol.  
To assess the overall effects of yeast strains, modalities of inoculum, and different sorts used, the data of the by-products, volatile compounds were analyzed by PCA (Figure 3).  
The graphic representation of PCA of the fermentation products responsible for beer's aroma showed a clear separation of beers obtained by two different sorts. The PW trials were in the upper left quadrant while PWL trials were in the lower right quadrant, showing a less homogeneous distribution. Indeed, the fermentation with _S. cerevisiae_ pure culture was in the upper right quadrant. The two non-_Saccharomyces_ in pure and mixed fermentations were in the opposite quadrant (lower right quadrant). Furthermore, PCA analysis showed a separation between pure and mixed culture, highlighting an effect of non-_Saccharomyces_ strains and their inoculation modality on the volatile composition of beer. In PWL NCY (pure and mixed fermentations) strongly characterized the aromatic profile resulting in more distance in the graphical distribution by _S. cerevisiae_.
## 5 Conclusions and Future Work  
In this research, our goal was to analyze how stressed speech influences the Speaker Identification systems performance. We have identified a problem, stressed speech in the testing stage affects negatively when SI systems are trained only with neutral speech.  
As for the case of match and mismatch conditions, in the mixed setting--using neutral and stressed original utterances--the SI system achieves a 96.05% of accuracy, a satisfactory rate for this type of tasks, demonstrating that the set of features chosen for the task is adequate.  
In the preliminary experiments for data substitution, depending on the difference between the synthetic data and the original one used for training, some substitutions outperform the results achieved by original data. Besides, the modifications over the pitch of the speaker work better when we include synthetically stressed samples for training, than when we include the modifications in speech speed. However, when we use super synthetic stressed samples for training, the sets modified by changes in speed achieve better results.  
Regarding the experiments for augmenting the database with artificial stress, we can conclude that the generation of different synthetically stressed utterances of speech by modifications in pitch and speed, and their addition to the database, enlarges meaningfully the instances to work with, improving substantially the results achieved by the Speaker Identification system with a 99.45% of accuracy.
# Unravelling the Molecular Regulation Mechanisms of Slow Ripening Trait in _Prunus persica_
## 4 Materials and Methods  
### Vegetal Material and Phenotyping  
An F2 population with 151 siblings previously used in fruit quality trait studies [14] was assessed to perform transcriptomic analysis between normal and slow ripening individuals. This population was obtained from the self-pollination of the nectarine cultivar Venus (_Prunus persica_ (L.) Batsch cv. Venus). The cultivar Venus was obtained from the intra-specific cross between Stark Red Gold and Flamekist at the INIA-Rayentue facilities (VI Region, Rengo, Chile) and no permissions were necessary to collect plant material. This cultivar produces freestone melting yellow-fleshed nectarines. The Venus \(\times\) Venus population (V \(\times\) V) consists of 6-year-old trees grown on G \(\times\) N rootstock in an experimental orchard located at 34\({}^{\circ}\)24\({}^{\prime}\) S latitude and 70\({}^{\circ}\)50\({}^{\prime}\) W longitude (INIA-Rayentue).  
Physiological fruit parameters and calculated averages for normal and slow ripening individuals were measured using nine fruit at seven fruit developmental stages determined as a number of days after 1 September (DASF). The measured dates were 37, 51, 65, 87, 99, 112, and 120 DASF for T1, T2, T3, T4, T5, T6, and T7, respectively. Photographic capture of the analyzed fruits was made using a static camera with the same light and photographic parameters, and then the weight, flesh firmness, and soluble solids content were measured. Two siblings were selected to obtain fruit material for transcriptomic analysis: one early ripening individuals and one slow ripening individual. Three replicates were selected for each individual at each analyzed developmental stage (T1, T2, T3, and T7) and collected to perform fruit RNA extractions and transcriptomic analysis (2 selected individuals \(\times\) 3 replicates \(\times\) 4 developmental stages = 24 samples).
# Benefits and Impact of Joint Metric of AOA/RSS/TOF on Indoor Localization Error
## 3 Error Bound Analysis
### Metric of Double Measurements  
#### 3.2.1 Metric of AOA/RSS  
When the metrics of AOA and RSS are selected to conduct the indoor localization, the metric of AOA/RSS follows the bivariate normal distribution. The PDF of the metric of AOA/RSS with respect to \(\theta_{i}\) is:  
\[{f_{\theta_{i}}}\left(\text{A/R}\right)=\prod\limits_{k=1}^{m}{\frac{1}{{2\pi \sigma_{1}\sigma_{3}\sqrt{1-{{\rho_{13}}}^{2}}}}\exp\left({-\frac{{{\sigma_{1 3}}}}{{2(1-{{\rho_{13}}}^{2})}}}\right)} \tag{22}\]where \(\rho_{13}\) is the correlation coefficient with respect to \(\mathbf{P}_{lk}\) and \(\mathbf{\alpha}_{ik}\), \(\alpha_{13}=\frac{\xi_{31}{}^{2}}{\sigma_{1}{}^{2}}+\frac{\xi_{32}{}^{2}}{\sigma _{3}{}^{2}}-2\rho_{13}\frac{\xi_{31}}{\sigma_{1}}\frac{\xi_{33}}{\sigma_{3}}\). From (5), we can obtain:  
\[\left\{\begin{array}{l}J_{xx}\left(\text{A/R}\right)=\frac{I_{xx}\left(\text{ RSS}\right)}{1-\rho_{13}{}^{2}}+\frac{I_{xx}\left(AOA\right)}{1-\rho_{13}{}^{2}}- \frac{2\rho_{13}\sqrt{\rho_{1}\rho_{3}}}{1-\rho_{13}{}^{2}}\sum\limits_{k=1}^{ m}\frac{\sin\alpha_{ik}\cos\alpha_{ik}}{d_{ik}{}^{2}}\\ J_{xy}\left(\text{A/R}\right)=\frac{I_{yy}\left(\text{RSS}\right)}{1-\rho_{13}{ }^{2}}+\frac{I_{yy}\left(AOA\right)}{1-\rho_{13}{}^{2}}-\frac{\rho_{13}\sqrt{ \rho_{1}\rho_{3}}}{1-\rho_{13}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin^{2}\alpha _{ik}-\cos^{2}\alpha_{ik}}{d_{ik}{}^{2}}\\ J_{yx}\left(\text{A/R}\right)=\frac{I_{yx}\left(\text{RSS}\right)}{1-\rho_{13}{ }^{2}}+\frac{I_{yx}\left(AOA\right)}{1-\rho_{13}{}^{2}}+\frac{\rho_{13}\sqrt{ \rho_{1}\rho_{3}}}{1-\rho_{13}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin^{2}\alpha _{ik}-\cos^{2}\alpha_{ik}}{d_{ik}{}^{2}}\\ J_{yy}\left(\text{A/R}\right)=\frac{I_{yy}\left(\text{RSS}\right)}{1-\rho_{13}{ }^{2}}+\frac{I_{yy}\left(AOA\right)}{1-\rho_{13}{}^{2}}+\frac{2\rho_{13}\sqrt{ \rho_{1}\rho_{3}}}{1-\rho_{13}{}^{2}}\sum\limits_{k=1}^{m}\frac{\sin\alpha_{ik }\cos\alpha_{ik}}{d_{ik}{}^{2}}\end{array}\right. \tag{23}\]  
Therefore, based on (8), the error bound by using the metric of AOA/RSS with respect to \(\theta_{i}\) is:  
\[V_{m}\left(\text{A/R}\right)=\frac{J_{xx}\left(\text{A/R}\right)+J_{yy}\left( \text{A/R}\right)}{J_{xx}\left(\text{A/R}\right)\cdot J_{yy}\left(\text{A/R} \right)-J_{xy}\left(\text{A/R}\right)^{2}} \tag{24}\]
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
###### Abstract
They also provided datasets with different formats, demonstrating the impact of GPS spoofing on aerial navigation systems and helping cyber-security researchers to combat this threat. The SatUAV dataset [15] includes a large amount of high-resolution aerial photography captured from 13 cities around the world, where 605 realistic scene photos with heterogeneous ground features were taken using a real UAV, while an additional 362 photos were added to the dataset from the senseFly website. Log file GPS spoofing datasets [16; 17] comprise high-dimensional sensor readings, primarily from GPS and IMU modules, collected during UAV flights in both benign and spoofed scenarios. They often require the implementation of advanced feature selection techniques due to their complexity. The OpenSky Network dataset [18] contains samples of real-world Air Traffic Surveillance (ATS) data, distributed all over the globe. Another dataset provided by [19] involves the characteristics of GPS signals extracted from an eight-channel GPS receiver mounted on a real UAV while performing real missions. It also contains three types of GPS spoofing attacks simulated using MATLAB. In this paper, we propose a supervised ML-based detection system to identify GPS spoofing attacks and classify each attack type according to its sophistication level. Tree-based machine learning algorithms are employed, namely a basic decision tree model, a bagging ensemble model known as random forest, and a boosting ensemble model named extreme gradient boosting. The dataset used contains 13 features collected from a multi-channel GPS receiver mounted on an autonomous vehicle through multiple missions. As a preprocessing step, our work involves handling non-stationary features and converting the original dataset dimensions into a more suitable format for our models. We conduct a comparison between the proposed classifiers and the existing work in terms of the accuracy, precision, recall, and F1-score metrics. The remainder of this work is structured as follows: In Section 2, we discuss previous works that address the problem. In Section 3, we provide the methodology used to design our machine learning classifiers. Section 4 presents the obtained results. Finally, Section 5 concludes the paper.
# Design, Experiment, and Improvement of a Quasi-Zero-Stiffness Vibration Isolation System
## 2 The QZS Vibration Isolation System
### Analysis of the Stiffness of the Two Coupled Magnet Springs  
Three main geometrical parameters affect the stiffness of the two coupled magnet springs: the height of the ring magnet, the radial gap between the inner ring magnet and outer ring magnet, and the radial thickness of the ring magnet.  
As shown in Figure 3, when the radial gap between the two ring magnets increased while other geometrical parameters were invariable, the absolute value of the negative stiffness at the equilibrium position decreased and the curve became increasingly more uniform. However, if the absolute value of the negative stiffness at the equilibrium position was too small, the static deformation became too large and did not comply with the vibration isolation requirement.  
The results of the negative stiffness for changing the radial thickness of the inner ring magnet are shown in Figure 4. It was found that the absolute value of the negative stiffness increased for the larger radial thickness and its nonlinearity increased.  
The influence of the height of the ring magnets on the negative stiffness spring was divided into three cases for investigation. First, as shown in Figure 5a, when the height of the two ring magnets was the same, the difference in the absolute value of each curve at the equilibrium position and the nonlinearity variation near the equilibrium position was very small. Second, as shown in Figure 5b, with the increase of the height of the outer ring magnet, the absolute value of stiffness decreased, leading to the number of peaks changing from one to two and the distance between the two peaks becoming larger. The nonlinearity of the curve changed from strong to weak and then became strong in the process of increasing the height of the outer ring magnet. Finally, as shown in Figure 5c,d, if the difference in height of the two ring magnets was kept the same and the heights of the inner and outer ring magnets changed, the absolute value of the negative stiffness at the equilibrium position and the nonlinearity near the equilibrium position did not change a lot.  
Figure 4: The influence of the radial thickness.  
Figure 3: The influence of the radial gap.
## 1 Introduction  
### Problem Statement  
The recent growth of the mobile Internet has brought a tremendous increase in the number and types of data services, creating much more challenging requirements for 5G communications. Meanwhile, the development of the 5G network relies highly on the improvement of WSNs, which are comprised of a large number of cheap devices that can be used to observe and preprocess neighboring physical and environmental changes. To construct an efficient path through the tremendous amount of base stations, millimeter wave technology is one of the critical parts of 5G technology [1; 2; 3]. Therefore, for 5G networks, it is essential to know the channel impulse response (CIR) of the millimeter waves, i.e., to estimate the channel pattern between different base stations. Luckily, the technological breakthrough of micro-electro-mechanical systems (MEMS) has promoted the application of large scale WSNs [4; 5; 6]. Thanks to the advantageous properties of self-organization and real-time data preprocessing, WSNs are a promising technology that can contribute to future 5G communications [7; 8; 9]. However, there are some challenges underlying this realization, since it is still difficult to deploy certain sensor networks to execute specific tasks. One of the challenges is limited energy resources, as it is energy-consuming to deploy many sensors awaiting task assignment and computing duties. It is, therefore, imperative to develop energy efficient algorithms in WSNs.  
A central problem in WSNs technology is its use of data collected by a network of spatially distributed sensors to estimate channel parameters. Sensors can cooperate to send their individual raw or preprocessed data to a data fusion center (FC) when requested. The FC then makes use of the collected data to extract the desired information. This process differs from the traditional centralized system, given that in a centralized system, all data are available and can be accessed in real time. Since the centralized estimation problem is no longer an issue, the existing works are all considered to be decentralized estimation.
# Wedge Design and Dynamic Characteristics Analysis of Unequal-Length Wedge Slant Support Clutch
## 3 Analysis of Dynamic Characteristics of Differential Engagement of Slant Support Clutch
### Analysis of Simulation Results
#### 3.2.2 Two Sets of Unequal-Length Wedge Slant Support Clutch
The average response time decreased by approximately 34%, average engagement time decreased by approximately 17%, and average instantaneous angular acceleration of the outer ring decreased by approximately 34%.
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology
### Implementation
#### 5.3.5 Video and Audio Synchronization  
Video and audio media files are independent and must be played back during synchronization. The playback position of the video can be obtained and set using the JavaScript's HTMLediaElement.currentTime property. Meanwhile, the Web Audio API does not provide a way to directly obtain the playback position of audio. Web3602 indirectly detects the  
Figure 18: Visualization procedure of audio objects.  
playback position of audio by recording the value of the AudioContext.currentTime property when the AudioBufferSourceNode.start() and AudioBufferSourceNode.stop() methods are used. At the start of playback and when the playback position returns to the beginning owing to loop playback, the playback position of the video is forcibly synchronized based on the calculated playback position of the audio. During playback, the difference between the playback position of the audio and video is detected and synchronized in the A-Frame camera object's tick handler, which is called 60-120 times per second. Instantaneous changes in the playback position of the video or audio degrade the playback quality, which is undesirable. If the playback speed of at least one of the video and audio is changed to match the playback position of both, the playback position will synchronize without degrading the playback quality. Changing the playback speed of the video can be easily set using the JavaScript property (HTMLMediaElement.playbackRate). However, changing the playback speed of the audio requires complex processing owing to the pitch conversion required. Web360\({}^{2}\) synchronizes video and audio by doubling or halving the playback speed of the video when the time difference between the audio and video playback positions exceeds a threshold value. The threshold value for this time difference is 0.1 s. However, if the time difference between the video and audio is more than 1 s, the playback position of the video is forcibly synchronized with that of the audio.
## 1 Introduction
However, others suggest a diminution in cortical activity when the cognitive task load increases [26]. Many studies use the fNIR to analyze the prefrontal cortex [18]. Given the applicability of fNIR in recording the brain activity in real-time task performance and the importance of the prefrontal cortex in motor control and cognitive functions, we have chosen the fNIR to measure the changes in hemoglobin concentrations in the prefrontal cortex in this study. It is important to combine the analysis of cortical activity derived from fNIR signals with postural control analysis to study neuromotor control processes, so as to predict risk factors for falls and the developing cognitive diseases [18]. With this in mind, and considering the ambiguous results of dual-task studies, this study aimed to better under stand the influence of attentionally demanding cognitive tasks with different difficulty levels during a simple postural task (static postural standing) using a force plate for the center of pressure analysis. Most of these studies assessed stability postural using center of pressure measures, fNIR for prefrontal cortex activation analysis, and dual-task cost (DTC) to analyze the dual-task interference (determine the cognitive task interference on stability postural). Using smartphones during postural and walking tasks while performing secondary tasks is common in daily life. However, few studies evaluated the effect of smartphone use on postural stability while performing upright standing [27]. For this reason, and to contribute to an ecological approach to studying dual-task performance, we examined dual-task performance on postural control by maintaining a quiet standing posture while performing a smartphone cognitive task with different difficulty levels. Furthermore, smartphone use is more frequent among young adults [28], and many studies carried out in this group have shown that excessive smartphone use has negative health effects [29; 30]. Therefore, it is essential to study the cognitive functions and postural control in young adults to implement early strategies to help reduce accidents or injuries using smartphones and cognitive disturbances at more advanced ages. We hypothesized that: (i) the young adults would demonstrate a decline in postural control performance and an increase in prefrontal cortex activity when performing the difficult dual-task compared to the easy dual-task and postural single-task; (ii) the dual-task cost in postural stability would be higher when young adults were performing a difficult cognitive task than an easy cognitive task.