esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 5 Experimental Results
### Trajectory-Tracking Speed Experiment  
Besides accuracy, speed is also an essential factor. Thus, the verification experiment was conducted. According to Figures 14 and 15, and Table 3, the average speed, task time and speed standard deviation of the improved PP are better than those of the original PP. The speed performance of the Double-L-shaped path increases by 11.2% and the speed of the S-shaped path increases by 5.6%. The performance of the improved PP is similar MCP method. This experiment proves that the improved PP performs tasks more efficiently.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trajectory-Tracking Algorithm** & \begin{tabular}{c} **Maximum Error** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Error (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Standard Deviation** \\ **of Error (mm)** \\ \end{tabular} \\ \hline \hline \multicolumn{4}{c}{**Double-L-shaped path (14.9 m)**} \\ MPC & 35.959 & 14.644 & \(\pm\)0.131 \\ PP & 160.215 & 48.158 & \(\pm\)0.289 \\ Our improved PP & **35.967** & **14.892** & **\(\pm\)0.223** \\ \hline \hline \multicolumn{4}{c}{**S-shaped path (8.2 m)**} \\ MPC & 34.282 & 19.329 & \(\pm\)0.449 \\ PP & 202.026 & 91.625 & \(\pm\)0.885 \\ Our improved PP & **44.609** & **15.742** & **\(\pm\)0.330** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of Trajectory-Tracking Accuracy Experiment. MPC stands for Model Predictive Control method, PP stands for Pure Pursuit method.  
Figure 14: The speed comparison between MPC, original PP and proposed improved PP methods in Double-L-shaped path: (**a**) MPC speed curve. (**b**) Original PP speed curve. (**c**) Improved PP speed curve.  
Figure 15: The speed comparison between MPC, original PP and proposed improved PP methods in S-shaped path: (**a**) MPC speed curve. (**b**) Original PP speed curve. (**c**) Improved PP speed curve.
## 2 Materials and Methods
### Data Analysis  
The kinematic and force data were obtained by the optical motion-capture system and then exported and saved as c3d files. Then, the hip, knee, and ankle joint angles, moments, power, and ground reaction force (GRF) were calculated using visual 3D software (V5, C-Motion, Bethesda, MD, USA). The raw kinematic data were filtered with a low-pass (Butterworth) filter with frequency of 20 Hz [10]. The threshold of the vertical ground reaction force (vGRF) data was set as 10 N.  
The stance phase, from initial contact (heel strike) to final lift-off from the force-plate by the dominant limb, was determined by the vGRF value. During this phase, there were three vGRF peaks, namely, the initial impact peak (PF1) for heel strike transient, the secondary impact peak (PF2) for impact loading, and the third impact peak (PF3) for drive-off. Based on the classification of previous studies [10; 20; 21], five periods can be clearly identified in the stance phase: (I) initial contact (from heel strike to PF1); (II) impact loading (from PF1 to PF2); (III) weight acceptance (from PF2 to peak knee joint flexion angle (PAK)); (IV) pre drive-off (from PAK to PF3); and (V) drive-off (from PF3 to lift-off from ground). In addition, the fatiguing process was divided into four stages according to the slope of the HR-time curve. Six continuous lumging motions were assessed at the end of each stage, and the kinematic and kinetic data of three lunge motions were then averaged and normalized for further statistical analysis.  
During the stance phase, the motion of lower extremity joints, particularly the knee, occurs primarily in the sagittal plane. This contributes to the major knee joint biomechanical characteristics in badminton research [10; 21]. Based on the previous literature linked to the lunge in badminton [10; 17; 18; 19; 20; 21; 27], we analyzed the impact peak; duration of five sub-stance phases; hip, knee, and ankle joint initial contact angles; durations to peak angle; ranges of motion (RoM); peak angles; moments; and power in the sagittal plane.
## 4 Experimental Evaluation
### Cut-In Driving Scenario  
#### 4.3.1 Experimental Environment  
The performance of the proposed cooperative environment perception approach was also evaluated in cut-in driving scenarios where the remote vehicle performs a lane-change maneuver in front of the host vehicle. Three different cut-in scenarios were considered in this work as described in Table 11. The host vehicle traveled at 40-45 km/h at the start of the cut-in maneuver in all three cut-in driving scenarios, while the remote vehicle originally driving in the adjacent lane cut in at a distance of 15-20 m in front of the host vehicle. The speed of the remote vehicle was set differently for each cut-in scenario in order to vary the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data Range** & \multicolumn{2}{c}{**Camera Lat. Position Error**} & \multicolumn{2}{c}{**Radar Lat. Position Error**} & \multicolumn{2}{c}{**V2X Lat. Position Error**} & \multicolumn{2}{c}{**Fusion Lat. Position Error**} \\ \cline{2-9}
**(m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** & **RMSE (m)** & **SD (m)** \\ \hline
0–10 & N/A & N/A & 0.32 & 0.22 & 0.15 & 0.09 & 0.11 & 0.09 \\
10–20 & 0.15 & 0.07 & 0.29 & 0.18 & 0.18 & 0.06 & 0.05 & 0.05 \\
20–30 & 0.16 & 0.08 & 0.28 & 0.25 & 0.31 & 0.15 & 0.09 & 0.08 \\
30–40 & 0.17 & 0.11 &level of collision threat, such that the scenario 1 presents the lowest level of threat while the highest level of threat is expected in the scenario 3.
## 4 Results and Discussion
### Comparison of Different Wavelet Transforms for MIMO DWT  
Figure 5 shows the performance results in terms of BER for different wavelet transforms. The wavelet families used were dB8, Haar and Bior 3.5, and the modulation scheme was QAM modulation. When the performance of dB8 was compared with that of Haar and Bior 3.5, the performance of Haar was found to be better in terms of SNR, with an increase of 2 dB, and in terms of BER for the MIMO DWT system in millimeter wave communication systems. The values of SNR at \(10^{-3}\) were 10 dB and 12 dB for Haar and dB8, respectively. Hence, the performance of dB8 family was much better than that of the Bior family.  
The simulation performance in terms of BER for the MIMO DWT system was analyzed with different wavelets; the parameters are listed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **MIMO DWT** \\ \hline Modulation Scheme & QAM 16 \\ DWT family & Haar, dB8, Bior 3.5 \\ Type of channel & AWGN \\ Number of antennas & \(2\times 2\) \\ SNR range & [0:20] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for different wavelets.  
Figure 5: Comparison of different wavelet transforms.
## 3 Effects of Orientation  
Due to the inherent anisotropy of the LiN material, the RF dielectric constant, optical refractive index and effective Pockel's coefficient change with the orientation of the device. Change in the RF dielectric constant and effective refractive index results in the change in RF electric field distribution and optical mode distribution within the structure, affecting the overlapping factor.  
In order to quantify the effects of orientation on the effective Pockel's coefficient of the device, the device is rotated by an angle \(\theta\) as shown in Figure 11. An x-cut LiN has axes given by x, y and z, with unit vectors denoted as \(\hat{x}\), \(\hat{y}\) and \(\hat{z}\). Under the effect of rotation the new axes are represented as x', y' and z', with unit vectors \(\hat{x^{\prime}}\), \(\hat{y^{\prime}}\) and \(\hat{z^{\prime}}\). The rotation matrix for the transformation is thus given by  
\[R_{\theta}=\begin{bmatrix}1&0&0\\ 0&cos(\theta)&sin(\theta)\\ 0&-sin(\theta)&cos(\theta)\end{bmatrix} \tag{3}\]Since dielectric (\(\epsilon\)) and perturbation (\(\Delta\epsilon\)) are tensors, under the effect of rotation new dielectric and perturbation tensors are given as  
\[[\epsilon^{\prime}]=R_{\theta}\begin{bmatrix}\epsilon_{11}&0&0\\ 0&\epsilon_{22}&0\\ 0&0&\epsilon_{33}\end{bmatrix}R_{\theta}^{-1} \tag{4a}\] \[[\Delta\epsilon^{\prime}]=R_{\theta}\begin{bmatrix}\Delta\epsilon_{11}&0&0\\ 0&\Delta\epsilon_{22}&0\\ 0&0&\Delta\epsilon_{33}\end{bmatrix}R_{\theta}^{-1}\] (4b) \[\Delta\epsilon_{11}=-e_{11}^{2}(r_{13}E_{rf})\] \[\Delta\epsilon_{22}=-e_{22}^{2}(r_{23}E_{rf})\] (4c) \[\Delta\epsilon_{33}=-e_{33}^{2}(r_{33}E_{rf})\]  
Combining the Equations in (4a) and (4b), the new perturbation is written as  
\[\Delta\epsilon_{33}=sin^{2}(\theta)\Delta\epsilon_{22}^{2}+cos^{2}(\theta) \Delta\epsilon_{33}^{2} \tag{5}\]  
Using the above relations the change in effective refractive index as a function of \(\theta\) is given by  
\[\Delta n_{eff}(\theta)=n_{eff_{a}}\left(\epsilon_{33}^{2}r_{33}cos^{3}(\theta )+\epsilon_{22}^{2}(r_{22}sin(\theta)+r_{23}cos(\theta))sin^{2}(\theta)\right) \tag{6}\]  
Similar equations are derived for the y-cut LiN platform using the same concept as described in Equations (3)-(6). The normalized change in refractive index with orientation angle while neglecting the effects of change in RF and optical field distribution are plotted in Figure 4a.  
However, due to the anisotropic behaviour of LiN, the RF dielectric constant and optical refractive index changes with the orientation of the device. This results in a change in the distribution of RF and optical field, varying the overlap factor \(\tau\). Variation in the (\(\tau\)) for an orientation of \(30^{\circ}\) is depicted in Figure 4b. The results in the figure suggest that \(\tau\) reduces by a factor of 0.91 (at the previously determined value of D) due to the change in field distribution within the structure.  
Figure 4: Change in (**a**) refractive index (neglecting the change in \(\tau\)) and (**b**) \(\tau\) for varying D.
## 2 System Modeling
### Shadow Fading  
The final step to complete the small cell deployment in our model is to consider a realistic model for shadow fading [13]. In [3] we have considered shadow fading with a simple log-normal shadowing value but here we replaced that model with the method in [13] which generates a correlated shadow fading map. In the proposed model, correlated shadow fading can be described simply with normalized correlation function:  
\[r(x)=e^{-\alpha x},\quad x\geq 0 \tag{5}\]  
where \(x\) is the distance and \(e^{-\alpha}\) is the correlation coefficient between two UE locations spaced by 1 m using the suggested value of \(\alpha=1/20\). Using this value of \(\alpha\) means that the shadow-fading correlation reduces to a value of 0.5 when the UEs are spaced by a distance \(x\) of approximately 14 m.. In our simulation we therefore assumed the shadow fading is unchanged over a distance of 10 m and therefore we construct our map with square micro-cells with length of 10 m. Figure 2 shows one realization of a Monte Carlo simulation with a shadow-fading map integrated into the simulation scenario shown in Figure 1. To generate the correlated fading map in Figure 2, we used two-dimensional space using four neighbors (each neighbor and square of 10 m) to create correlation matrices as explained in the Appendix of [13]. In Figure 2 we have a large square map around a macro-cell base station at the center of map with length and width of 6 km. Based on the interpretation of the map, the value of shadow-fading attenuation can be calculated between the macro-cell base station and each UE and then used in the simulation of the coverage analysis.  
Our Monte Carlo simulation steps can be summarized as follows:  
* A shadow-fading map is created using the algorithm in the Appendix of [13]. The BS is located at the center of the map with randomly spread UE devices around the map as can be seen in Figure 2.  
Figure 2: Applying Shadow Fading Map to our simulation analysis.  
Then the physical BS/UE locations in Figure 1 are mapped to the shadow-fading values shown in Figure 2. This process allows the simulation to identify where the UE has been located and if any walls are present in the BS-UE link that need to be accounted for in the path-loss calculation.
* According to the geometry of the UE and BS and the path-loss model, we measured the received signal power level in the location of the UE devices.
* Finally, we calculate the percentage of UE devices in outage. Then we implement the D2D communication scenario or add small cells randomly to the map. Again, we calculate the received signal level from nearby UE devices for the D2D communication scenario or the small cell assisted scenario. Using the data provided below in Section 3, the MCL can be computed for the D2D/small cell wireless links to identify the improvement of outage for UE devices.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### Adapting the A* Algorithm for Such Graphs
#### 3.5.1 The Notation  
A _path_ from the node \(n_{i}\) to the node \(n_{k}\) is an ordered set of nodes \(\{n_{i}\), \(n_{i+1}\),..., \(n_{k}\}\) that are connected by edges, and that are traversed in the order in which they appear in this list. If an edge goes from the node \(n_{i}\) to the node \(n_{k}\), then \(n_{k}\) is said to be a _successor_ of \(n_{i}\). \(n_{\text{m}}\) is said to be _accessible_ from \(n_{k}\) if there exists a path from \(n_{k}\) to \(n_{\text{m}}\). In our application, the cost \(c_{ik}\) assigned to the edge \(e_{ik}\) is simply the length of this edge. The _cost_ of a path is the sum of the costs of all of its edges. A node is said to be _expanded_ or _closed_ when all of its successors have been generated. It is said to be an _end node_ or an _open node_ when none of its successors have been generated. In our adaptation of the A* algorithm, at any given time, a node will have either all of its successors generated or none of them generated. An obstacle \(O\) is said to be _open_ when it has only some _end_ nodes. It is said to be _expanded_ when it has at least one _expanded_ node. An _evaluation function_\(f\) is used to select the next node to be expanded in the algorithm. As is often done, we define \(f\) such that its value for the node \(n\) is  
\[f(n)=g(S,n)+EDist(n,T), \tag{1}\]  
in which \(g\big{(}n_{i},n_{j}\big{)}\) is the length of the shortest path in the graph, from the node \(n_{i}\) to the node \(n_{j}\), and \(EDist\big{(}n_{i},n_{j}\big{)}\) is the Euclidean distance between \(n_{i}\) and \(n_{j}\).  
As pointed out by [PERSON], some modifications are required in the standard A* algorithm in order to deal with the fact that the obstacles are not punctual so that many nodes can exist on the same obstacle. These modifications are described in the following sections.
## 4 Conclusions  
An efficient and sustainable approach for increasing n-3 PUFA content in eggs was presented in this study. A robust sample of about 3000 hens were fed with a 3% _N. oceanica_ supplemented diet. After 1 week of the trial, a remarkable increase in DHA and EPA fatty acid was observed, allowing eggs classification as high in n-3 (CE n\({}^{\circ}\) 1924/2006). It is worth mentioning that the supplemented diet gave rise to volks with more intense orange colour. This characteristic is highly appreciated by consumers, who associate orange yolk with healthy and nutrient-rich eggs. In conclusion, _N. oceanica_ was shown to be a reliable source of n-3 PUFA for eggs enrichment, while preserving its quality and nutritional patterns during storage time.  
Conceptualization: J.L.S., S.M., M.M.G. and C.T.; methodology: M.N., S.M., M.M.G. and C.T.; software: M.N., A.F., M.A., S.M., M.M.G. and C.T.; validation: M.N., A.F., M.A., S.M., M.M.G. and C.T.; formal analysis: M.N., A.F. and M.A.; investigation: M.N., A.F., M.A., S.M., M.M.G. and C.T.; resources: M.N., A.F., M.A., S.M., M.M.G. and C.T.; routines: M.N., A.F., M.A., S.M., M.M.G. and C.T.; writing--original draft preparation: M.N., A.F., and C.T.; writing--review and editing: M.N., M.A., J.L.S., S.M., M.M.G. and C.T.; visualization: M.N., A.F., M.A., S.M., M.M.G. and C.T.; Supervision: [PERSON], S.M., M.M.G. and C.T.; Project administration: [PERSON], S.M., M.M.G. and C.T.; Funding acquisition: S.M., M.M.G. and C.T. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Principal component analysis (PCA) biplot of egg samples along the shelf-life, concerning 11 parameters: amounts of the fatty acids EPA, DHA and ALA; protein (Prt), lipids (Lips) and ash (Ash); Yolk colour (yC); Shell colour (sC); shell thickness (sT); egg weight (eW) and egg protein quality (HU). C-0; C-7; C-14; C-21 and C-28—Eggs from hens fed with standard diet, after zero (0), 7, 14; 21 and 28 days of storage. W4-0; W4-7; W4-14; W4-21 and W4-28—eggs from hens fed with _N. oceanica_ supplemented diet, after 0, 7, 14; 21 and 28 days of storage. PC-1 and PC-2 explain, respectively, 37.9% and 23.2% of data variability.  
**Funding:** This research was funded by Portuguese Foundation for Science and Technology (FCT) to the research units of MARE (UIDB/04292/2020) and by the Integrated Programme of SR&TD \"Smart Valorization of Endogenous Marine Biological Resources Under a Changing Climate\" (reference Centro-01-0145-FEDER-000018), co-funded by Centro 2020 program, Portugal 2020, European Union, through the European Regional Development Fund.  
**Acknowledgments:** The authors are grateful to Allmicroalgae--Natural Products S.A., Portugal for supplying _Nannochloropsis oceanica_ and to Alimave, S.A, Portugal and Gameirovo, Caldas da Rainha, Portugal, for creating the conditions to carry out the assays.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Wireless Communication Modules and Configurations  
The measured water quality data were transmitted from a wireless communication chip (XBeeTM S2C; Digi International Inc., United States) to other XBeeTM S2C chips. The XBeeTM platform was selected for its capabilities of mesh-type networking [17] and continuous wireless communication with the water quality sensing modules within the range of the controllable distance of the drones on water (500 m [18]). The primary modulation method was the Offset-Quadrature Phase Shift Keying (O-QPSK). The spectrum spreading method was the Direct Sequence Spread Spectrum (DSSS). These XBeeTM S2C chips have routing functions in the Application Programming Interface (API) mode, which provide a multiple drone system with wireless communication. All the XBeeTM chips were configured using software (XCTU ver. 6.5.6; Digi International Inc., Eden Prairie, MN, USA) and an XBeeTM Universal Serial Bus (USB) dongle (SparkFun Electronics Inc., Niwot, CO, USA). Each of the XBeeTM S2C chips with a U.FL connector was connected to a half-wave dipole antenna (A24-HABUF-PSI, 2.1 dBi (1.62) gain and 10 cm overall height; Digi International Inc., United States). These antennas were used vertically. All the XBeeTM S2C chips were operated at the maximum transmission power (6.3 mW, localized for Japanese regulations), \(-102\) dBm receiver sensitivity and 9600 baud rate. Each XBeeTM S2C chip for the drone system was configured to work in the router mode, whereas the chip for the receiver was used in the coordinator mode. The default values for other parameters in the XCTU were retained. To avoid interference with signals from the drone controller (2.4 GHz Wi-Fi transceiver for Japanese regulations), the channel for the wireless communication between the XBeeTM S2C chips was not fixed. The scan duration of the selectable channels (15, channel 26 was not used) was set as approximately 2.4 s (the SD value in the XCTU was set at 3). The corresponding central frequency range was 2.405-2.475 GHz. The bandwidth for each channel was 0.002 GHz. The node joining time was set as infinite. The node discovery backoff time was set as 6.0 s. The interval of water quality measurement and data transmission was set as approximately 7.0 s by adjusting the delay time in the ArduinoTM sketches (programs), but the interval was extended later (experiments in 2023) to 10 s for stable communication. Each of the measured water quality data points (multiplied to make a 16-bit integer value) was divided into two 8-bit payloads by bit-shifting for the communication with XBeeTM S2C. A unique identifier number for each sensor (no duplication throughout all nodes) was also sent before each data transmission.  
Figure 2: Schematic representation of amphibious drone-mounted water quality monitoring system with wireless data relay.
# Perturbation-Based Explainable AI for ECG Sensor Data
## 5 Model Explanations
### Designed Method
```
Procedure:XAI_FOR_INVIDUAL_LEAD (_Ecg_Instance, NN_Model, Syntax_Of_Interest_) Input:Instance of ECG data \(\mathit{ECG}\)_Instance, Neural network model NN_Model, Syndrom_Of_Interest_ Output:Lead importance \(\mathit{Load}\)_Importance
1:Prediction \(\leftarrow\) NN_Model_predict(Ecg_Instance);
2:initialize Load_Importance
3:for\(i=0,...,12\)do
4:\(\mathit{New}\_Ecg\leftarrow\mathit{Perturb}(\mathit{ECG}_Instance[i])\) Prediction\(\mathit{for}\_Perturbed\_instance\leftarrow\)NN_Model.predict(\(\mathit{New}\_Ecg\))
5:\(\mathit{Load}\_Importance[i]\leftarrow\mathit{Prediction}[\mathit{ Syndrom_Of_Interest}]\) Prediction\(\mathit{for}\_Perturbed\_instance[\mathit{ Syndrom_Of_Interest}]\)
7:endfor
8:return Global_predictions;
```  
**Algorithm 2** Most important lead  
Figure 4: An example of an ECG graph that presents explanations in form of heatmap coloring. The output of the function MOST_IMPORTANT_LEAD is a list of values. These values describe the degree of reduction in the prediction of the specified syndrome when changing the individual leads. The greater the reduction due to the resulting lead perturbation, the more important it is to the prediction. The magnitudes of changes vary for individual syndromes. Therefore, we added normalized importance and a horizontal bar graph for clearer quantification of the importance. An example of the output of the MOST_IMPORTANT_LEAD function is shown in Figure 5, along with representation of the most important leads for the prediction of long QT syndrome.