esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory
## 3 Tracking Strategy
### Loop 3: Flatness-Based Tracking Control
#### 3.3.1 Flatness Theory  
Flat systems theory is a complex area of research in differential algebra and differential geometry. Differential flatness is introduced as follows [29]:  
A nonlinear system is given by:  
\[\dot{x}=f(x,u),\ x\in\Re^{n}\ \text{and}\ u\in\Re^{m}. \tag{18}\]  
This system is differentially flat if there is a vector, \(F\in\Re^{m}\), such that  
\[F=\xi\Big{(}x,u,\dot{u},\ldots,u^{(r-1)}\Big{)}, \tag{19}\]  
whose components are differentially independent, and two functions, \(\eta(.)\) and \(\Gamma(.)\), such that  
\[x=\eta\left(F,\dot{F},\ddot{F},\ldots,F^{(\alpha-1)}\right) \tag{20}\]  
\[u=\Gamma\Big{(}F,\dot{F},\ddot{F},\ldots,F^{(\alpha)}\Big{)}, \tag{21}\]  
where \(\alpha\) and \(r\) and are finite multi-indices and \(\tilde{\zeta}\), \(\eta,\ \text{and}\ \Gamma\) are vectors of smooth functions. The vector \(F\) that appears in this definition is called the flat output of the system. In other words, a flat system is a system whose state and control variables can be written according to this flat output and its derivatives. The open-loop flatness control given by Equation (21) is known as the Brunovosky control because it provides an exact linearization of the system. For a differentially flat system, when the desired trajectory, \(F_{d}\), is known, the desired state, \(x_{d}\), and the desired open-loop control, \(u_{d}\), can be defined as follows:  
\[x_{d}=\eta\left(F_{d},\dot{F}_{d},\ddot{F}_{d},\ldots,F_{d}^{(\alpha-1)} \right), \tag{22}\]  
\[u_{d}=\Gamma\Big{(}F_{d},\dot{F}_{d},\ddot{F}_{d},\ldots,F_{d}^{(\alpha)} \Big{)}. \tag{23}\]  
If the system is naturally stable, it will behave well and follow the desired trajectory. For unstable systems whose purpose is to accelerate convergence, it is necessary to add to this open-loop control a small closed-loop correction term to ensure trajectory tracking.  
In this work, a closed-loop flatness control is proposed. We denote it by FTC: flatness-based tracking control. This control contains two parts: the open loop control given by Equation (21) and a loop term, \(\vartheta\), which represents a linear control capable of stabilizing the obtained linearized system. The FTC is given as follows:  
\[u_{FTC}=I\Big{(}F_{d},\dot{F}_{d},\ddot{F}_{d},\ldots,\vartheta\Big{)}. \tag{24}\]  
\(\vartheta(t)\) represents the new command. When \(\frac{\partial\Gamma_{(.)}}{\partial F^{(a)}}\) is locally invertible, this leads to the following decoupled system:  
\[F^{(a)}=\vartheta, \tag{25}\]  
with  
\[\vartheta=F_{d}^{(a)}+\sum_{i=0}^{a-1}k_{i}\Big{(}F_{d}^{(i)}-F^{(i)}\Big{)}. \tag{26}\]  
Let \(K(p)=p^{\beta}+\sum_{i=0}^{\beta-1}k_{i}p^{i}\) be a diagonal matrix whose elements are polynomials with negative real part roots. This allows for asymptotic trajectory tracking with  
\[\lim_{t\to\infty}(F_{d}-F)=0. \tag{27}\]
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 4 Gabor-Based Speech Reconstruction Network
### Model II: GaborFea2 Speech
#### 4.2.1 GaborFea2 Speech-Input Sequence Dimension Testing  
The effect of input sequence length on speech reconstruction can be evaluated by extracting input sequences with different numbers of frames from the original videos. The _write_video_ function from the _movie.py_ package was used to extend the input sequences during the pre-processing stage of the GaborFea2 Speech model. By adjusting the frame extraction rate, the original rate of 25 fps was transformed to 85 and 130 fps, which increased the frame count of each video from 75 to 255 and 390 frames, respectively, where 390 matches the desired output sequence length. 100 videos from Speaker 1 were selected, the accuracy and PESQ scores of the predicted spectrograms was evaluated (more detail will be provided in the main experiments section), and a sample was randomly selected and its spectrogram and speech waveform plotted to provide a visual assessment of different input sequence lengths. These preliminary results indicated that the matching of input and output sequences is a critical factor in the success of our GaborFea2 Speech model. If the input sequence is only 75 frames, significantly shorter than the output sequence, the model cannot accurately predict the desired output, which results in unintligible speech. With the increase in input sequence length to 255 frames, which is still short of the output sequence, the model captures a significant portion of the input information, resulting in higher prediction accuracy and quality than with 75 frames. Upon extending the input sequence to 390 frames, which matches the length of the output sequence of the model, the model successfully predicts all essential speech information in the preliminary trials, resulting in improved prediction accuracy and PESQ scores. Therefore, this configuration is used in the experiments reported in the following sections.  
We also assessed whether input dimensionality affected the quality of reconstructed spectrograms and speech by varying input dimensionality while keeping the input sequence length constant at 390. By applying the time derivative method to input features, it was possible to increase the dimensionality of the input features in order to ensure that they accurately represent the data and capture the necessary information. With the addition of 1D, 2D, 3D, and 4D time derivative channels, the 7 Gabor features were expanded to 14, 21, 28, and 35. It was found that the accuracy of reconstructed spectrograms improves with increasing the number of input features. The highest accuracy was achieved by using 21 features. As a result of these preliminary trials, the GaborFea2 Speech model is configured to use 2-D time derivatives and is also interpolated to have an input sequence of 390 frames.
## 5 Analysis of Experimental Results
### Analysis of Soil Heavy-Metal Prediction Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & \(\lambda\) & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{Hg} & 0 & 0.0265 & 0.0007 & 0.857 \\  & 0.25 & 0.0207 & 0.0004 & 0.912 \\  & 0.5 & 0.0226 & 0.0005 & 0.896 \\  & 0.75 & 0.0223 & 0.0005 & 0.898 \\ \hline \multirow{4}{*}{Ni} & 0 & 1.095 & 4.973 & 0.837 \\  & 0.25 & 0.774 & 3.517 & 0.918 \\ \cline{1-1}  & 0.5 & 0.818 & 3.763 & 0.909 \\ \cline{1-1}  & 0.75 & 0.86 & 3.836 & 0.899 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error indicators for each heavy metal at different \(\lambda\). Figure 10: Fitting curves of predicted and actual values of Ni for different \(\lambda\). Figure 9: Fitted curves of predicted and actual values of Hg for different \(\lambda\). In Table 3, the RMSE, MAE, and R\({}^{2}\) of the SE-GCN model were superior to those of the GCN model, DCNN, Kriging, and RBF. In predicting Hg, the SE-GCN model had a 24.18% lower RMSE, 42.86% lower MAE, and R\({}^{2}\) was 0.064 lower than the GCN model; R\({}^{2}\) decreased by 0.064. Compared with the DCNN model, the RMSE of SE-GCN decreased by 5.48%, the MAE decreased by 20%, and R\({}^{2}\) decreased by 0.1. Compared with the Kriging model, the RMSE of SE-GCN decreased by 47.33%, the MAE decreased by 73.33%, and R\({}^{2}\) decreased by 0.226. Compared with the RBF model, in predicting Ni, the SE-GCN model had a 67.67% lower RMSE,  
From Figures 11 and 12, the absolute errors of the SE-GCN model predictions for Hg and Ni were smaller than those of the GCN, DCNN, Kriging, and RBF models. For Ni, the absolute error of the predictions of the SE-GCN model fluctuated less than those of the GCN, DCNN, Kriging, and RBF models. Thus, compared to GCN, DCNN, Kriging, and RBF models, the proposed SE-GCN model demonstrated higher accuracy and better performance in predicting soil heavy-metal content. In experiments conducted on the SE-GCN model, the training time and prediction time were recorded. The model exhibited stable training and prediction times. The average training time among 100 experiments was 2.71 s, and the average prediction time was 0.0044 s. These experiments were conducted in an environment with a CPU i7-12650H, 16 GB of memory, and running on Windows 11.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 6 Conclusions  
This study presented the development of a 3D-printed enclosed twist dielectric resonator antenna (DRA) designed to achieve circular polarization at a center frequency of 5.8 GHz. The design focused on meeting the requirements of unmanned aerial vehicle (UAV) applications, specifically in terms of bandwidth, gain, and compact volume.  
The results obtained from both simulation and measurement confirm that the antenna performs effectively in these areas, demonstrating that 3D printing can be a viable approach to producing antennas for UAV applications. The manufactured antenna achieved the  
Figure 16: Simulated results for the sensitivity analysis. (**a**) Displacement error. (**b**) Permititivity uncertainty.  
Figure 14: Results of the measured radiation pattern for the implementation of the LHCP implementation.  
Figure 15: Top view of the antenna misplacement sensitivity study.  
desired circular polarization and bandwidth characteristics, as well as adequate gain. However, several manufacturing and design challenges were encountered throughout the development process due to the inherent sensitivities associated with these antennas. Therefore, it is important to assess the materials and dimensionality of the antenna for their implementation.  
Conceptualization, A.A.-S. and F.P.; methodology, F.P.; validation, A.A.-S., M.D. and F.P.; investigation, A.A.-S.; resources, F.P. and M.D.; writing--original draft preparation, A.A.-S., M.D. and F.P.; writing--review and editing, A.A.-S., M.D. and F.P.; funding acquisition, F.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by ANID FONDEQUIP EQM220109, FONDECYT REGULAR 1221090, FONDEF IDEA ID231I0360, Anillo ATE220057 and ONRG N62909-23-1-2016.  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors would like to thank the Universidad Tecnica Federico Santa Maria, Chile, for the measurements in the anechoic chamber.  
The authors declare no conflicts of interest.
## 3 Results
### Genetic Risk Score for Obesity and Eating Behavior
Dependent variable: CEBQ score classified from p50. Figure 2: Associations between some independent SNPs and the anti-ingestion subscales of the CEBQ (N= 258). CEBQ-SR: response to satiety subscale; CEBQ-SE: slowness to eat subscale; CEBQ-EUE: emotional undereating subscale; CEBQ-FF: food demand subscale. A binary logistic regression analysis was applied. Values are OR (95% CI) for sex- and age-adjusted models. Significant association * \(p<0.05\), **\(p<0.01\), *** \(p<0.0001\). Independent variable: containing or not at least one risk allele of each SNP. Reference group = category having no risk allele of each SNP. Dependent variable: CEBQ score classified from p50. Associations were found with several SNPs except for the food fussiness (FF) subscale. Each risk allele of SNP rs11676272 (ADCY3) reduced satiety responsiveness (SR) by 2.390-fold (95% CI: 1.367-4.184; \(p<0.001\)). Similarly, each risk allele of SNP rs7566605 (INSIG2) reduced it by 1.627-fold (95% CI: 1.060-2.775; \(p=0.033\)). The SNPs rs4788099 (SH2B1), rs6857 (NECTIN2), and rs180172 (ADCY3) showed a decrease in slowness in eating (SE), or in other words, an increase in eating speed of 1.65 (95% CI: 1.258-2.945; \(p=0.010\)), 2.06 (95% CI: 1.179-3.709; \(p=0.008\)), and 2.37 times (95% CI: 1. 124-5.000; \(p=0.025\)) respectively. In contrast, for each copy of the risk allele of rs987237 (TFAP2B), intake speed was reduced, with an OR of 0.492 (95% CI: 0.263-0.921 \(p=0.042\)). Emotional undereating (EUE) was nominally associated with 4 SNPs. The risk alleles of SNPs rs55915917 (CRHR1) and rs7132908 (FAI2M) reduced the probability of emotional undereating (EUE) by 1.779 (95% CI: 1.044-3.030; \(p=0.014\)) and 1.751 (95% CI: 1.007-3.719; \(p=0.040\)) times, respectively. In contrast, the risk alleles corresponding to SNPs rs1421085 (FTO) and rs987237 (TFAP2B), indicated on the left side of the graph, were associated with emotional undereating anti-intake behavior, with an OR of 0.385 (95% CI: 0.189-0.781; \(p=0.009\)) and 0.391 (95% CI: 0.214-0.713; \(p=0.011\)), respectively. Finally, for each risk allele of SNP rs6857 (INSIG2), the lower food fussiness (FF) is increased by 2.303 times (1.045-5.142; \(p=0.023\)).
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Mobility Models in FANETs?
#### 2.4.2 Path-Planned Mobility Models  
The path-planned mobility model forms predefined routes used by UAVs. There are three common characteristics. Firstly, nodes are independent on each other. Secondly, nodes are memoryless, and so, the new speed and direction of the nodes are independent on their previous speed and direction. Thirdly, nodes can move at any speed within a range.  
* Semi-random circular movement (SRCM) allows a node to form a circular (or curve) route [55]. When the node arrives at its destination following the route, it stops for a short period of time, and then, it starts to move towards another randomly chosen destination using a newly predefined route. The circular routes have been shown to reduce collisions between UAVs. This model is suitable for multi-UAV scenario.
# A Visitor Assistance System Based on LoRa for Nature Forest Parks
###### Abstract  
Ecotourism activities are attracting more people each day, including national forest parks. Unfortunately, the number of incidents involving visitors to natural parks grows at the same pace. Among the most prevalent risks inside forests are getting lost and the occurrence of natural disasters. In this work, we propose a system for monitoring and assisting visitors of forest parks, based on a low power wide range wireless network, LoRa. The proposed visitor assisting system is composed of mobile terminals that communicate between them and with fixed infrastructure, using a protocol designed for exchanging visitor locations data. The infrastructure consists of wireless gateways distributed on the trails, the totems. User terminals, the mobile nodes, work collaboratively through a Delay and Disruption Tolerant Network (DTN), to cope with the possibility that the gateway infrastructure does not cover the whole trail. In addition to improvements and gains for minimizing risks, the proposal also brings contributions to the preservation of the environment, raising awareness of the influence of human presence in the natural environment and to the development of environmental education actions.  
ecotourism safety; forest radio propagation; monitoring system; cooperative positioning Article
## 4 Discussion
### Limitations  
The study possesses several notable strengths, including its focus on an understudied sport, track cycling, and its involvement of a large international sample, encompassing various disciplines and both genders. Moreover, the study's innovative approach combining quantitative retrospective data analysis with qualitative data collection through interviews with field experts adds depth to the research questions posed. However, it is crucial to acknowledge certain limitations. Firstly, the study's descriptive nature restricts its ability to account for causal processes that may contribute to athletes' success or dropouts. As a result, it is challenging to establish direct cause-and-effect relationships. Additionally, limiting the analysis to a specific time interval (e.g., 10 years) offers methodological advantages, but it may also lead to potential oversight of late success achievers or those whose accomplishments extended beyond the designated time frame. In addition, the separation of data from the junior and elite categories by two years might underestimate some achievers who achieved success later, and the inclusion of riders who left the junior category earlier may also affect the estimation of achievers. Despite these limitations, the study still highlights that achievers remain a minority in both categories. Furthermore, the variability in the competitive levels across different years introduces complexity in interpreting the significance of athletes' scores or rankings. The shifting competitive landscape may impact the weight attributed to an athlete's performance metrics in different periods, affecting the overall evaluation. Lastly, while involving high-level practitioners in the field provides valuable insights, it also presents challenges in recruitment and may limit the number of potential participants in the interviews, potentially constraining the overall depth and breadth of qualitative data collected. In summary, the study's strengths lie in its focus on an unexplored sport and the use of a diverse and large international sample. The combination of quantitative and qualitative methodologies enriches the investigation. However, limitations in the study's design, such as its descriptive nature, temporal constraints, and variability in competitive levels, should be considered when interpreting the results. Despite these limitations, the study contributes valuable insights to the understanding of talent development and success factors in track cycling.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Time-Related Characteristics
#### 4.1.2 Mean Excess Delay  
In the indoor environment, the excess delay varies with propagation distance and reflections. The length of multipath components directly depends on the environment characteristics between the transmitter and receiver. When a group of reflectors and scatterers are somewhat far away from the transmitter and receiver, the reflected multipath components experience somehow similar delays, i.e., the arrival delays will only slightly change. This is a phenomenon well known in the literature, where it is often referred to as \"multipath clustering\" [59]. The value of excess delay increases for the weaker reflected paths. Typically, the reflected paths from the nearby obstacles have small excess delays, while the paths reflected through remote high-rise buildings, have higher values of excess delay. The presence of a large reflector (physical objects with large size causing multipath components) causes multiple reflections, all characterized by similar delays, leading to cluster formation. The time dispersion parameter of the channel, mean excess delay, is computed for each received PDP for each operator and location. Figure 8 shows a boxplot with average of mean excess and uncertainty in terms of standard deviation (\(\sigma\)) delay for Op1 and Op2 at all measurement locations. The mean excess delay for Op1 at location 1, location 2, location 3, location 4, and location 8 is 36 ns, 68 ns, 4 ns, 103 ns, and 91 ns, respectively. For Op 2, the mean excess delay at location 1, location 2, location 4, location 5, location 6, location 7, and location 8 is 90 ns, 96 ns, 9 ns, 30 ns, 100 ns, 140 ns, and 150 ns respectively.  
The mean excess delay for Op1 at locations 1 and 3, while for Op 2 at locations 2, 4, and 5, is lower than at other locations. As the excess delays vary for distance and surroundings, the signal at these locations experiences weak reflections from nearby obstacles, leading to the low value of mean excess delay. In contrast, the signal at other locations undergoes strong reflections from obstacles such as nearby high-rise buildings or human traffic, leading to larger mean excess delay. By considering the locations where the beams are more directive, i.e., location 1 for Op1 and location 6 for Op2, it can be noticed that in the 5G system operating with SSB-based transmission, for Op1, the echoes arrived at the receiver with smaller mean excess delays but were relatively weaker. While in the 5G system operating with the wide beam-based transmission, for Op2, echoes arrived at the receiver with large excess delays but were relatively much stronger.
## 6 Influence of Tire Rotation on the Existing Range of Plane Wave in Tire Cavity  
In a previous analysis, a static tire is considered; however, the research aiming at the rotating tire is of greater significance. Here, the influence of tire rotation on the existing range of plane waves in the tire cavity is studied.  
When the tire is steadily rotating at a rotation speed of \(\Omega\) (Figure 1c), the medium in the tire moves with the tire. Then, the speed of the medium may be approximately regarded as  
\[v=\Omega\cdot R_{t} \tag{16}\]  
where \(\Omega\) is the angular velocity of tire rotation; \(R_{t}\) denotes the effective radius of the tire cavity, which is generally the radius of the cavity cross-section center.  
Obviously, the speed of the forward traveling wave increases by \(v\), while that of the backward traveling wave decreases by \(v\). According to Equations (12) and (13), the wave speed may change the attenuation characteristics and the existing range of higher-order evanescent waves. For the tires with speeds of 10, 20, and 30 m/s, respectively, the variations of the existing range of the high-order waves with acoustic wave frequency for the forward and backward traveling waves are calculated and shown in Figure 9.  
With the increase of tire speed, as far as the backward traveling wave is concerned, the existing range of higher-order waves becomes larger, while for the forward traveling waves, the existing range of higher-order waves decreases. The reason is that the tire rotation results in the increase of the velocity of the backward traveling wave, further in the decrease of the attenuation of the higher-order wave (which can be seen from Equation (13)), and vice versa for the forward traveling wave.  
By including the combined effects of forward and backward traveling waves, we can obtain the existing range of high-order waves with tire speed, as shown in Figure 10. We may observe that the range of high-order waves gradually increases with tire speed, but within the frequency range (200-250 Hz) of tire cavity resonance; tire speed has a slight effect on the existing range of high-order waves. Therefore, when studying the tire acoustic  
Figure 8: Relationship between \(\eta\) and \(L\).  
cavity resonance noise, the influence of tire rotation on the change of acoustic characteristics of the tire acoustic cavity may be ignored.