esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
## 3 Results
### Traffic Flow as a Function of Gap Profile  
In this section, we present the effect of gap profile parameters on merging success for the case of road velocity \(v_{s}=100\) km/h and varying onset gap speeds (\(v_{g}\)supping) and platoons' sizes.  
Figure 5 presents the obtained vehicle flow rate in the road as a function of merging position (\(x_{merge}\)), maneuvering section size (\(\%d_{acc}\)) and merging gap (\(g_{merge}\)) for linear (left) and sigmoid (right) gap profiles.  
It is well-shown that the most prominent factor in the resulting flow is the merging position. As the preparation section becomes longer, the traffic flow increases (and more vehicles merge). Point A in the sigmoid graph (right) is most related to human drivers' behavior with a late merge (i.e., late gapping) and zipper strategy, where there is a small preparation section, the gap provided for each vehicle is small, and the gapping is abrupt. It is apparent that for autonomous vehicles, this strategy may be inferior to the other merging possibilities. For example, point C (in both graphs), where the maximal flow is obtained,  
Figure 4: Flow rate (blue line) and merging success (orange line) as a function of inlet density.  
Figure 5: Obtained traffic flow [vel./h] for various control parameters. Left: linear gap profile; Right: sigmoid gap profile. The marked points A and B represents late merge and zipper strategy while point C represent early platoon merging strategy.  
represents cases with large preparation sections, large gaps for platoon merging and large stabilization sections that enable gap accumulation.  
There is a slight benefit to the linear profile over the error function profile in the obtained flow, and therefore, we show only the results for the linear gap profiles. Larger stabilization sections improve the flow rate, as shown by comparing point B to point A, where a smaller maneuvering section indicates a larger stabilization section, which is essential for cases where the gapping is abrupt (i.e., in the sigmoid profile).  
The merging percentage rate increases with the preparation section (\(x_{merge}\)) and with the relative portion of the stabilization section (\(\%d_{stb}\)), as presented in Figure 6. Lower onset gap speeds result in higher merging percentages as well.
# Time-Dependent, Two-Photon Rabi Frequency Shift Observed in Multi-Frequency Raman Spectra
## 6 Conclusions  
Overall, the comparison between experimental and numerical results in this study has provided strong evidence for Raman scattering of resonant interactions between different dressed states in the generation of red-shifted spectra in multi-frequency Raman generation. The agreement between the experimental FROG spectrograms and the numerical simulations based on the two-photon Bloch equations highlights the significance of the two-photon Rabi frequency-shifted levels in this process. The double-pulse model offers a more comprehensive understanding of the phases associated with the Raman orders, holding the potential for significant improvements in our final pulse compression efforts. This newfound insight could enable us to attain cleaner, single-cycle pulses within the transient regime, a key step toward enhancing our experimental capabilities and achieving precise control over the generation of ultra-short pulses.  
Moving forward, our next steps will involve investigating the spectra as a function of input pulse durations and fiber lengths. This investigation will allow us to gain a deeper understanding of the role of propagation in the observed phenomena. Furthermore, we are planning to expand our measurements to include the second anti-Stokes order. Analyzing the second anti-Stokes spectra will provide additional insights into the underlying dynamics and propagation effects. We are aware that our simple approach to optimizing the simulated model does not ensure that we reach the global minimum. In the future, we will use numerical optimization techniques that allow us to find the global minimum and possibly have more variables in the system. We will also look to compare our results with a theoretical model of the time-varying two-photon resonant process [18].  
Conceptualization and methodology, Z.X. and D.S.; validation, formal analysis, and writing--review and editing, Z.X., Z.J. and D.S.; visualization and coding, Z.X. and Z.J.; data curation and original draft preparation, Z.X.; supervision, project administration, and funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSERC (Natural Sciences and Engineering Research Council of Canada).  
Contact at z92 [EMAIL_ADDRESS] for supporting data.  
The authors gratefully acknowledge helpful discussions with [PERSON] and funding support from the Natural Sciences and Engineering Research Council.  
The authors declare no conflict of interest.
## 2 Navigation and Localization
### Sensor Fusion
Test results demonstrated that the system proposed in this work is more reliable than LC approach since the error on the trajectory--particularly when approaching or leaving the hydrophones array--was up to 50% lower. Artificial vision is also being fused with INS to improve its performance. [PERSON] et al. [94] addressed autonomous navigation for AUVs. They used artificial vision fused with an IMU on a LC algorithm. Parallel Tracking and Mapping (PTAM) was implemented to localize the vehicle respect to a visual map, using a single camera--15 fps, 640 \(\times\) 480 px. Then, an EKF was used to fuse the visual information with data from an IMU, to recover the scale of the map and improve the pose estimation. In this work, fully autonomous trajectory tracking was successfully achieved and compared using standalone PTAM and the sensor fusion approach. Results showed that the trajectory followed by the vehicle using sensor fusion has errors not bigger than 20 cm whilst the standalone PTAM drift up to 60 cm. The EKF is the most widely used nonlinear filtering approach in TC schemes. EKF is based on a simple linear approximation to the nonlinear equations. However, there are too many unknown disturbance factors at underwater, and they cannot be established in suitable mathematical models in the kinematic equation. Other alternatives to a traditional EKF have been explored. [PERSON] et al. [95] proposed a multi-model EKF integrated navigation algorithm. It was designed to solve the harsh underwater environment problems. This algorithm, based on the probabilistic data association theory, was compared with standard EKF in a lake trial using an AUV equipped with an IMU, an AHR, and an LBL system with four acoustic beacons. Results showed a better performance by the multi-model EKF since the error between true positions and estimations were less than 12 m. The algorithm showed to be able to overcome disturbances that produced peaks of over 400 m on traditional EKF estimations. [PERSON] et al. [96] worked on another alternative to an EKF for TC SINS/LBL navigation systems. Instead of applying an EKF they used a near-real-time (NRT) Bayesian framework. They compared NRT framework with EKF approaches with an accurate and a poor initialization. Results showed a better performance by the NRT solution with an 80% reduction of the measurement residuals with a poor accurate yaw error initialization. The main alternatives for sensor fusion based on an INS are summarized in Figure 14.
# Survey on Unmanned Aerial Vehicle for Mars Exploration: Deployment Use Case
## 4 Simulation Results and Discussion
### Environmental Conditions
#### 4.1.2 Gravity  
Gravity is the force exerted on the object to pull it towards the centre of the planetary body. Two major forces, i.e., drift and weight affect the gravitational force. The weight of a flying vehicle is the force of gravity acting to pull the UAV to the ground and resolve via lift. Lift and gravity are two opposite forces. It is evident that for designing a prototype UAV, decreased weight and an increased lift are the two major goals to be achieved. Based on [PERSON]'s theory of universal gravitation, when talking about a spherical body, such as a planet, the gravitational force is directly proportional to the planet's mass and inversely proportional to the square of the radius of the planetary body. Equations (1) and (2) are based on [PERSON]'s theory of universal gravitation and shows the formula for the gravitational force of Mars [95]. Table 6 shows the notation and parametric values of Equation (1) [96].  
\[\mathrm{g}=\mathrm{Gm}/\mathrm{r}^{2} \tag{1}\]  
\[\mathrm{g}=3.711\ \mathrm{m}/\mathrm{s}^{2} \tag{2}\]  
Figure 4: Aerodynamics of the UAV.  
where:  
g is the gravity of the Mars  
G is the gravitational constant  
m is the mass of the planet Mars  
r is the radius of the Mars
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 3 Material and Method
### Principal Components of Wavelet Coherence  
To further improve the classification of ASD using 2D matrices of WC analysis, we applied singular value decomposition (SVD) to extract the significant coherent components from the WC matrix [42]. This technique is known as principal wavelet coherence (PWC) and is illustrated in Figure 4. SVD basically decomposed the WC matrix into three matrices: \(U\), \(S\) and \(V\) as follows  
\[WC_{(TP_{i}\times F)}=U_{(TP_{i}\times TP_{i})}.S_{(TP_{i}\times F)}.V_{(F\times F )}^{T} \tag{9}\]  
Here, the semi-unitary matrices \(U\) and \(V\) are real orthogonal matrices, and matrix \(S\) is real pseudo diagonal elements with non-negative diagonal entries. The measure \(S\) is used to determine when a single or a particular group of SVD components is dominant in the WC matrix. The diagonal entries \(S\) are called the singular values of the original WC matrix and are arranged in decreasing order. The \(u_{i}\) and \(v_{i}\) columns of \(U\) and \(V\) are called the left-singular and right-singular vectors of the matrix, respectively. The \(s_{i}\) values are ordered so that the one with the highest magnitude has the smallest index \(i\). It is assumed that most of the coherence values of the SVD matrices are contained in the first principal component of the SVD. Hence, the output of the PWC stage is WC matrices reconstructed using first principal components. This means that apart from the first singular value, the rest of the singular values are set to zero. PWC is based on the development of our previous method for identifying ASD based on WC of rs-fMRI BOLD signals [32].  
In the next step, the FC matrices based on SFC and DFC are separately converted into 2D images of size \(224\times 224\) to be used as input to CNN for ASD classification. A total of 90% of the images per each type are used for training and validation, and the remaining 10% are used for blind testing. The steps to prepare the images for ASD classification are shown in Figure 4.  
Figure 4: The formation of principal wavelet coherence (PWC) images as the input feature for training and testing of CNN. Here, \(TP_{i}\) indicates the length of the BOLD signals, and F is the number of frequency bands from 0.01 Hz to 0.08 Hz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{p}\)-Value} & \multicolumn{2}{c}{**WCF**} & \multicolumn{2}{c}{**PCC**} \\ \cline{2-5}  & **FC\({}_{\mathbf{s}}\)** & **AccU** & **FC\({}_{\mathbf{s}}\)** & **AccU** \\ \hline
0.01 & 51 & 91.1 \(\pm\) 0.3 & 47 & 90.3 \(\pm\) 0.6 \\
0.02 & 93 & 93.2 \(\pm\) 0.2 & 87 & 91.8 \(\pm\) 0.4 \\
0.03 & 119 & 92.1 \(\pm\) 0.2 & 131 & 86.5 \(\pm\) 0.8 \\
0.04 & 179 & 91.9 \(\pm\) 0.1 & 168 & 82.7 \(\pm\) 0.9 \\
0.05 & 212 & 93.7 \(\pm\) 0.3 & 204 & 87.6 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of significant FC patterns of WCF and PCC based on classification accuracy (%) evaluated for 10-fold CV at different \(p\)-values (0.01–0.05) using ANOVA-SVM algorithm.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
###### Abstract  
Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS\({}^{2}\) (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS\({}^{2}\)/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS\({}^{2}\)/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services.  
energy-efficient IoT; environmental intelligence; bounded-error sensor data; lossless compression +
Footnote †: journal: Applied Sciences
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 3 Technical Approach
### Ear Data Coarse Segmentation and Preprocessing  
The ear images used in this paper are coarsely extracted from human side face images utilizing an ear detection algorithm based on Faster R-CNN frameworks [23]. The proposed algorithm demonstrates an impressive capability on recognizing an ear from an input image. All of the ears in UND-J2 and CASIA database have been detected correctly without any manual intervention.  
As shown in Figure 2, once the 2D ear region image is extracted, the corresponding 3D ear data can be segmented from the co-registered 3D data. However, the extracted ear region obviously contains masses of non-ear data such as face skin and hair data which will lead to a bad performance of the ICP-based algorithm in the recognition work. As such, the normalization of ear data is necessary before recognition.  
Data in UND database are collected using Minolta Vivid 910 laser scanner (Konica Minolta, Marunouchi Center Building, 1-6-1 Marunouchi, Chiyoda-ku, Tokyo, Japan) and saved as scanning point clouds. It is inevitable that there are some noise, outliers, and missing data on the surface. Therefore, the gridfit code from the mathworks website [34] is applied to resample and smoothly transform the point cloud into a triangulation mesh.  
Figure 1: The full automatic 3D ear recognition system. ICP, Iterative Closet Point; LSV, Local Surface Variation.  
Figure 2: Ear detection and coarse segmentation: (**a**) Ear region detected; (**b**) Coarsely segmented ear; (**c**) Corresponding 3D ear data.
## 3 Main Results  
### Reference Information Processing  
Firstly, transformations between the inertial coordinate system \(\alpha xy\) and the velocity coordinate systems (VOSs) \(\left\{\vartheta^{k}x^{k}y^{k}\middle|k\in\mathcal{N}_{a}\right\}\) as shown in Figure 1 are introduced for nonlinear controller design. We use \(\{\mathbf{\mathcal{T}}_{0i}|i\in\mathcal{N}_{a}\}\) to indicate the transformation from \(\alpha xy\) to VOS, where \(\mathbf{\mathcal{T}}_{0i}=\mathbf{T}(\mathbf{\mathcal{\mathcal{\mathcal{\mathcal{\mathcal{ \mathcal{\mathcal{\mathcal{\mathcal{\mathcal{\mathcal{\mathcal{\mathcal{ \mathcal{\mathcal{ \mathcal{   \mathcal{ }}}}}}}}}}}}}}} )\).  
Assume that the positions can be communicated among agents, we can directly compute \(\mathbf{q}_{t}^{j}\) with \(D_{1}\Big{(}\mathbf{X}_{t}^{j}\Big{)}=f\Big{(}\mathbf{q}_{t}^{j}\Big{)}\). Then \(\mathbf{u}_{t}^{j}\) can be estimated by \(D_{1}\Big{(}\mathbf{q}_{t}^{j}\Big{)}\). However, for the measured information, it is more convenient to estimate \(\left(\mathbf{q}_{t}^{j},\mathbf{u}_{t}^{j}\right)\) in VOSs. The relation between \(\widetilde{\mathbf{X}}_{ij}^{t}\) and \(\mathbf{q}_{t}^{j}\) can be indicated as:  
\[D_{1}\left(\mathbf{\mathcal{T}}_{0i}\widetilde{\mathbf{X}}_{ij}^{t}\right)=\left[\begin{array} []{c}v_{t}^{j}\cos\psi_{r}^{j}-v_{t}^{j}\\ v_{t}^{j}\sin\psi_{r}^{j}\end{array}\right]+D_{1}(\mathbf{\mathcal{T}}_{0i}) \widetilde{\mathbf{X}}_{ij}^{t} \tag{6}\]  
where \(\psi_{r}^{i}=g\left(\psi_{t}^{i}-\psi_{t}^{j}\right)\), and \(g(\mathbf{\varphi})=\mathbf{\varphi}\) if \(|\mathbf{\varphi}|\leq\pi\), otherwise \(g(\mathbf{\varphi})=g(\mathbf{\varphi}-sign(\mathbf{\varphi})*2\pi)\), where \(sign(\cdot)\) is the sign function.  
Referring to (6), \(v_{t}^{j}\) can be estimated by  
\[v_{t}^{j}=\lVert D_{1}\left(\mathbf{\mathcal{T}}_{0i}\widetilde{\mathbf{X}}_{ij}^{t} \right)-D_{1}(\mathbf{\mathcal{T}}_{0i})\widetilde{\mathbf{X}}_{ij}^{t}+\left[\begin{array} []{c}v_{t}^{j}\\ 0\end{array}\right]\rVert_{2} \tag{7}\]  
Since we obtain \(v_{t}^{j}\), \(\psi_{t}^{j}\) can be computed later. Thus \(\widetilde{\mathbf{q}}_{t}^{j}\) is estimated; similarly, we can obtain \(\widetilde{\mathbf{u}}_{t}^{j}\). If more information can be interacted, such as \(\mathbf{q}_{t}^{j}\) or \(\mathbf{u}_{t}^{j}\), then unbiased estimations of \(\mathbf{q}_{t}^{j}\) or \(\mathbf{u}_{t}^{j}\) can be realized with the above differential models and extended Kalman filter (EKF).
## 3 Proposed Multiple Watermarking Algorithm  
With the aim of incorporating multiple watermarks and ensuring resilience against common attacks, a novel algorithm for multiple watermarking is proposed. Figure 4 shows the proposed embedding procedure based on MQIM. First, the coordinate vertices of the cover vector geographic data are quantized into non-overlapping quantization intervals using up-down MQIM. Each embedding watermark is embedded into the corresponding quantization interval to avoid them disturbing each other. Then, during the processing of watermark detection, all watermarks are extracted, and correlation detection is undertaken to detect multiple watermarks.
## 2 Data and Methods
### Annual Analysis  
The second question we asked is which MODIS product(s) best explains the annual variations in flux tower GPP. We calculated the annual mean of each MODIS product as the sum of the available values divided by the total number of available values. We then analyzed the regressions between flux tower annual total GPP and the annual mean of each MODIS product for the data organized into the following groups: all sites, non-tropical sites only, and tropical sites only. Similarly to the short-term analysis, we varied the composite period from 8, 16 to 32-days. We did not composite MOD17 because this would have caused to neglect its biophysically-induced reductions (e.g., reductions induced by drought periods rather than by cloud or aerosol contamination) and therefore lead to an overestimation of annual GPP.  
Although annually integrated vegetation indices are sometimes used for this kind of analyses (e.g., [PERSON] _et al._[6]), we preferred to use annual mean vegetation indices. Satellite data are plagued by frequent missing data due to cloud contamination, high aerosol, _etc._, causing the annually integrated indices to be affected by the number of valid data. Also, the use of mean values facilitates the comparisons across deciduous and evergreen forest types.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 2 Material and Methods
### Experimental Setup  
In this study, the same modeling framework as in [15] was used. It is based on SURFX Version 8.0, and the same data assimilation method based on the simplified extended Kalman filter was performed. SSM is assimilated on a daily basis, while LAI is assimilated on a 10-day basis. This study differs from [15] because the assimilated SSM was provided by Copernicus Global Land Service instead of the ESA-Climate Change Initiative. Another difference is that we used the three-layer version of ISBA, while [15] used the diffusive representation with ten layers of soil. This study follows the works of [12] and [14], but a wider spatial domain is considered (the Euro-Mediterranean area from 25\({}^{\circ}\)N-75.5\({}^{\circ}\)N and from 11.75\({}^{\circ}\)W-62.5\({}^{\circ}\)E) over an extended period of time (from 2007-2015).  
In this study, the ISBA-A-gs model was forced with ERA-Interim reanalysis surface atmospheric variables [58]: precipitation, solar radiation (shortwave and longwave), wind speed, surface pressure, air temperature, CO\({}_{2}\) air concentration and air humidity. These forcings are available at the global scale on a 0.5\({}^{\circ}\) resolution grid every 3 h from 1989 until the present (with a one-month delay). In order to allow the system to reach equilibrium, all experiments were initialized by performing a 5-year spinup.
## 2 Background Knowledge
### Probabilistic Reasoning with Bayes Theorem  
As we saw above, the interpretation of a rule in diagnostic systems is as follows:  
\[\begin{array}{l}\text{if e (evidence)}\\ \text{then h (hypothesis)}\end{array}\]  
which is exactly the diagnostic task of an expert: given some medical evidence, derive a corresponding hypothesis (disease). This is a case of abductive reasoning, which does not assure a 100% correct conclusion. That is, a hypothesis is concluded with some probability. The answer to the question of whether we can compute that probability is the Bayes theorem, expressed by the following formula:  
\[p(h/e)=\frac{p(e/h)\times p(h)}{p(e)}\]  
where _p(h/e)_ is a _conditional_ or _posterior probability_, which represents the probability that hypothesis \(h\) holds given that evidence \(e\) holds (or is observed). A more convenient formula (from a computational point of view) for the [PERSON] theorem is the following:  
\[p(h/e)=\frac{p(e/h)\times p(h)}{p(e/h)\times p(h)+p(e/\
eg h)\times p(\
eg h)}\]  
given that \(e\) depends on the mutually exclusive \(h\) and \(\
eg h\).  
Given that in reality there are multiple evidence (e.g., symptoms) and more than one hypothesis (disease), [PERSON] developed a generalization of the above theorem for \(n\) elements of evidence and \(m\) hypotheses, as follows:  
\[p(h_{i}/e_{1}e_{2}\ldots e_{n})=\frac{p(e_{1}/h_{i})\times p(e_{2}/h_{i})\times \ldots\times p(e_{n}/h_{i})\times p(h)}{\sum_{k=1}^{n}p(e_{1}/h_{k})\times p(e_ {2}/h_{k})\times\ldots\times p(e_{n}/h_{k})\times p(h_{k})}\]  
which holds when \(h_{1}\), \(h_{2}\), \(\ldots\), \(h_{\text{m}}\) are mutually exclusive and exhaustive, and \(e_{1}\), \(e_{2}\), \(\ldots\), \(e_{n}\) are mutually exclusive, exhaustive, and conditionally independent of any \(h_{i}\). In practice, the required probabilities are given by the experts. Then, all conditional probabilities \(p(h_{i}/e_{1}e_{2}\ldots e_{n})\) are calculated, and the hypothesis with the largest conditional probability is proposed as the conclusion. For an extensive treatment, see [13] (ch. 3).