esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments
## 2 Materials and Methods
### Platform Description  
The hardware architecture of our research platform is presented in Figure 2. The aerial robot is based on the standard DJI Matrice 600 platform, in which all sensors and the processing computer are embedded. The autopilot is DJI A3, for which a specific connection with the onboard computer was devised. We used a Ubiquiti Bullet radio device for communications between the aerial robot and the ground station. For data processing, an Intel NUC with an i7 processor was used.  
A 3D LiDAR sensor was used to obtain point clouds of the aerial robot environment. The 64-channel Ouster OS1 sensor was chosen, which counts with a vertical field of view of 45 degrees, and a range of around 100 m. However, due to the high computational load of point cloud processing, it was decided to decrease the number of scans and use only 16 channels, thus enabling greater computational efficiency. This LiDAR sensor was used for building the 3D map of the environment and for point cloud matching during the flights.  
Finally, the radio sensors that were used to obtain distance measurements to and from the aerial robot were the standard model of the _swarm bee ER_ family from Nanotron. One of them was integrated onboard the aerial robot, while the rest of the sensors were rigidly attached to different locations in the environment to serve as beacons. The accuracy of the distance measurements was 10 cm between 0 and 50 m, which was well suited to our application.
## 4 Cognitive Radio Combined with WBAN
### Tier-1 WBAN Model  
In the Tier-1 WBAN model, WBAN sensors communicate with the BNC, which communicates with the Tier-2 WBAN model. Due to the extremely low Effective Isotropic Radiated Power (EIRP), the UWB naturally has a noise-like structure. This increases its advantages such as invisibility during searching, inability to be jammed, and it can be applied to a simple transceiver, eliminating the implementation use of an encryption algorithm. Wearable sensors with a lower amount of power dissipation are attained through the use of an IR-UWB radio for communication in Tier-1. Three sub-bands--3232, 3960, and 4488 MHz--are chosen for Tier-1 communications. The battery charge must be drained linearly; the CR channel assignment process must not consume much power and drain the BNC battery. If the BNC is often drained, it needs to be charged frequently, affecting the communication with the sensors. Thus, for frequency allocation, if most of its power is drained, it cannot be used in an energy constraint device, thus affecting directly the communication with the sensor and reducing the life of it. Thus, CR is not suited for a resource-constrained IMD sensor communication for Tier-1 communication.
## 2 Methodology and Computational Model
### Wind Turbine Model and Coordinate Systems  
A straight-bladed VAWT was used in the present study, which was the same as one in the previous literature [33]. The turbine had a diameter (\(D\)) and a span length (\(H\)) of 1 m with 3 blades. The airfoil was NACA0015, and the chord length (\(c\)) was 0.0575 m. The turbine operated in a uniform inflow field of 7 m/s with a rotational speed of 535 r/min, corresponding to a tip-speed ratio of 4.0. The intersection points of the inclined pitch axes and the aerodynamic center lines were in the equator of the blades.  
As shown in Figure 1, three coordinate systems were built up to describe the innovative VAWT. The first was an inertial coordinate system (ICS), of which the origin was at the center of the turbine as high as the inclined pitch axes, and the \(x_{i}\), \(y_{i}\) and \(z_{i}\) axes were in the downstream, lateral and upward direction, respectively. The second was an unfolded bladecoordinate system (BCS), of which the origin was at the aerodynamic center of a blade as high as the inclined pitch axes, and the \(x_{b}\), \(y_{b}\) and \(z_{b}\) axes were in the chordwise, thickness and upward direction, respectively. The third was an inclined pitch axis coordinate system (PCS), which was the rotational transformation of the BCS around its negative \(y_{b}\) axis with an incline angle (\(\gamma\)). The \(x_{p}\) axis of the PCS was coincident with the inclined pitch axis. In this way, the blade was folded around the negative \(x_{p}\) axis of the PCS with a fold angle (\(\eta\)). It is clear that two parameters dominated the results of the present pitch regulation, namely the fold angle and the incline angle. Therefore, the present study focused on these two pitch parameters.  
The sign convention of the concerned variables is shown in Figure 2. A revolution of the blade was divided into four quartiles: the first quartile refers to \(0^{\circ}\ \leq\ \psi\ \leq\ 90^{\circ}\), the second quartile refers to \(90^{\circ}\ \leq\ \psi\ \leq\ 180^{\circ}\), the third quartile refers to \(180^{\circ}\ \leq\ \psi\ \leq\ 270^{\circ}\) and the fourth quartile refers to \(270^{\circ}\ \leq\ \psi\ \leq\ 360^{\circ}\). Besides that, the fore half refers to \(0^{\circ}\ \leq\ \psi\ \leq\ 180^{\circ}\) and the aft half refers to \(180^{\circ}\ \leq\ \psi\ \leq\ 360^{\circ}\).
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 4 Vibration Analysis of Subway Tunnel Construction
### Summary and Analysis of Results
#### 4.3.3 Vibration Frequency and Resonance Risk Analysis  
The study further analyzed the relationship between blasting-induced vibration frequencies (40-80 Hz) and the natural frequencies of structural components (50-200 Hz). The results show that the majority of the blasting vibrations fall within the safe frequency range for structures and do not pose a resonance risk with the natural frequencies of the concrete tunnel structure. This finding has significant engineering implications, suggesting that under normal construction and blasting conditions, resonance due to frequency matching is unlikely.  
In comparison, vehicle-induced low-frequency vibrations (0.5 to 25 Hz) are much lower than the frequency of the blasting vibrations, reducing the likelihood of resonance. The statistical analysis in Figure 11 shows that approximately 95% of the vibration frequency data fall within the 40-80 Hz range, aligning with the safety threshold for tunnel structures and further confirming that the blasting vibrations remain within a safe range, avoiding potential resonance issues.
# MCST Scheme for UAV Systems over LoRa Networks
## 6 Numerical Simulations  
This section describes the performance evaluation of MCST/LoRa and mMCST/LoRa by comparing them with the CSMA/LoRa [19] in the LoRa network. By considering the remote identification and tracking of UAV systems for sharing the location information as the application case study, we perform a preliminary network capacity study of the MCST scheme over the LoRa network.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 2 Design Concept
### A Wheel Tractor with Attitude Adjustment Function
#### 2.2.1. Rear Adjustment Drive Axle  
The rear adjustment drive axle is shown in Figure 2. As shown in Figure 2a, the rear axle has an axle housing, two slewing bearings, two end driving mechanisms, and two wheels. The entire rear axle is separated by the slewing bearing. The inside of the slewing bearing is mounted to the axle housing, and the outside is fixed connected to the end driving mechanism. The slewing bearing is driven by a hydraulic cylinder, thereby allowing the relative rotation motion between the inside and outside. The front view and left of the rear axle are shown in Figure 2b,c, respectively. As shown in Figure 2e,f, by actively changing the rotation angle (\(\alpha\)) of the slewing bearing, the wheel, which is connected to the ending driving mechanism, can rotate around the rotation center of the rear axle. Thereby, the height of each side can be actively adjusted.  
Figure 2. Rear axle: (**a**) Simple model; (**b**) Front view; (**c**) Left view; (**d**) Initial state; (**e**) Turn upward; (**f**) Turn downward; (**g**) Transmission system.  
The initial state in Figure 2d. As shown in Figure 2e,f, when the rear wheel rotates upward around the rotation center, the height of COG is reduced; when rotates downward, it is increased. This tractor can adjust its roll angle by changing the height of each side independently. The transmission system is shown in Figure 2g, where the power transfers from the engine to the end driving mechanism through a main reducer and a differential mechanism. Then, power is transferred to the rear wheel by a pair of gears.  
Consequently, by changing the rotation angle of the slewing bearing on both sides, the heights of COG on both sides change together. Thus, the attitude of the tractor can be actively adjusted while providing power to the rear wheels.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 4 Further Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Actual** & **Speed** & **Source of** & **Weight** & **Carrying** & & & & **Max** & \\  & **(m/s)** & **Energy** & **(g)** & **(g)** & **Capacity (g)** & **Output Force** & & & **Displacement per** & **Dimension** & **Refs.** \\ \hline SMARoll robot & SMA & 0.13 & \textless{}10 V DC & 191.2 & - & - & 50 mm & 100 \(\times\) 50 \(\times\) 8 mm\({}^{3}\) & [53] \\ \hline GoQbot & SMA & 0.5 & \textless{}10 V DC & 5-6.2 & - & 0.05 N & 25 cm & 100 mm (Length) & [82] \\ \hline Inchworm robot & Fluid & 0.0022 & Air pressure & - & 50 & 0.5 N & 2 mm & 98 \(\times\) 25 \(\times\) 17 mm\({}^{3}\) & [94] \\ \hline Tube chamber & Fluid & 0.006 & Air pressure & 98 & 1381 & 138 N & 2.4 cm & 70-80 mm & [96] \\ \hline \multirow{2}{*}{Jellyfish robot} & \multirow{2}{*}{Fluid and EAP} & \multirow{2}{*}{0.009} & Gas-liquid & & & & & & \\  & & pressure & & & & & & & \\  & & difference & & & & & & & \\ \hline Annfield robot & EAP & 0.0053 & 3 kV DC & 10.3 & - & 2 MPa & 9 mm & 170 mm (Length) & [125] \\ \hline RSR & EAP & 0.03627 & 3.2 kV DC & 0.88 & - & - & 145.09 mm & 24.83 mm & [126] \\ \hline Multi-material & Magnetic & 0.0031 & Magnetic field & 0.23 & 3 & - & 5.5 mm & 40 \(\times\) 5 \(\times\) 2 mm\({}^{3}\) & [25] \\ \hline C-Balls & Magnetic & 0.046 & Magnetic field & 30 & - & - & - & [180] \\ \hline \multirow{2}{*}{Helical robot} & \multirow{2}{*}{Magnetic} & \multirow{2}{*}{0.01} & Magnetic field & 0.04 & - & - & 30 mm (Length) & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline Inching robot & Light & 0.00025 & \begin{tabular}{c} 200 mW-cm\({}^{-2}\) \\ (Light intensity) \\ \end{tabular} & - & - & - & 2.7 mm & 17 mm (Length) & [23] \\ \hline \multirow{2}{*}{Transporter robot} & \multirow{2}{*}{Light} & \multirow{2}{*}{0.0005} & 150-250 & \multirow{2}{*}{0.02} & \multirow{2}{*}{0.005} & \multirow{2}{*}{0.05 N} & \multirow{2}{*}{4 mm} & \multirow{2}{*}{20 mm (Span)} & [133] \\  & & & (Light intensity) & & & & & \\ \hline Wormbot & Sound & 0.056 & \begin{tabular}{c} 300 mAh \\ 7.4 V (Batter) \\ \end{tabular} & - & - & - & 6 mm & \begin{tabular}{c} 160 mm (Length) \\ 27 mm (Radius) \\ \end{tabular} & [162] \\ \hline Tripedal robot & Chemical & 3.6 & Methane & - & - & 71 kPa & 30 cm & \begin{tabular}{c} 130 mm (Length) \\ 10 mm (Height) \\ \end{tabular} & [164] \\ \hline Jumping robot & Chemical & \
\begin{tabular}{c} 0.43 (Horizontal) \\ 0.86 (Vertical) \\ \end{tabular} & Butane & 510 & - & 138 kPa & 0.6 m &
\begin{tabular}{c} 150 mm (Radius) \\ 80 mm (Height) \\ \end{tabular} & [52] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the characteristics of the different soft robots.
## Abbreviations  
### Acronyms  
5G Fifth generation  
6G Sixth generation  
B5G Beyond fifth generation  
BS Base station  
\(\mathcal{D}\mathcal{K}\mathcal{M}\) Discrete-time kinodynamic motion model  
\(\mathcal{D}\mathcal{M}\) Dynamic model  
IoTs Internet of Things  
IoVs Internet of Vehicles  
IVs Intelligent vehicles  
\(\mathcal{K}\mathcal{M}\) Kinodynamic motion model  
LoS Line of sight  
MIMO Multi-input multi-output  
mmWave Millimeter wave  
MPC Model predictive control  
OF Objective function  
QoS Quality of service  
RIS Reconfigurable intelligent surface  
RIS-equipped UAV  
SLAM Simultaneous localization and mapping  
UAV Unmanned aerial vehicle  
UAV BS UAV as BS  
VTOL Vertical take-off and landing  
V2V Vehicle-to-vehicle  
V2X Vehicle-to-everything  
WCNs Wireless communication networks
## 3 Results and Discussion
### Modeling Study
#### 3.2.4 Steady-State, Porous Media Capture Model
Therefore, the cumulative reduced SS load, \(X_{\sigma}\), was calculated according to the specific SS deposit (\(\sigma\)) (Equation (15)), and the results are presented in Figure 6C. Here, \(X_{\sigma}\) is seen to decrease almost linearly from 10.35 to \(-\)3.27 m\({}^{-1}\), from 6.97 to \(-\)4.05 m\({}^{-1}\), and from 6.23 to \(-\)4.97 m\({}^{-1}\), at filtration velocities of 20, 30, and 40 m/h, respectively, during the period of operation. Moreover, the \(X_{\sigma}\) was well correlated with the natural logarithm of operation time (ln(\(t\))) at each filtration velocity and with the filtration velocity at each operation time. These results indicate that the SS removal in the granular media layer can successfully be described by the steady-state porous media capture model based on the specific SS deposit. The results described above indicate that the specific SS deposit (\(\sigma\)) is useful for describing the SS removal for the media layer used in the present study (Figures 5E,F and 6C). Hence, various parameters related to the specific deposit, such as \(k_{a}\), \(k_{d}\), \(\sigma_{m}\), and \(X_{\sigma}\), can be correlated with the head loss, as shown in Figure 7. As reported previously, the total head loss in the media layer increases from 3.0 to 5.5 cm, from 4.2 to 6.5 cm, and from 5.6 to 7.9 cm, at flow velocities of 20, 30, and 40 m/h, respectively [20]. In the present work, the parameters \(k_{a}\), \(k_{d}\), and the natural logarithm of \(\sigma_{m}\) exhibited good correlations with the final head loss (Figure 7A). In particular, \(k_{a}\) and the \(k_{d}\) are seen to increase, while ln (\(\sigma_{m}\)) decreases as the final head loss increases. The increased \(k_{a}\) indicates that an enhanced SS deposit in the media layer leads to a decrease in the amount of pore space due to the SS accumulation therein. By contrast, the increased \(k_{d}\) indicates an increased possibility of re-dispersion with increased SS deposit (\(\sigma\)) in the media layer [24; 37]. This conclusion is supported by the observed decrease in \(\sigma_{m}\) with increasing head loss (i.e., with an increasing amount of SS deposit). In addition, the results in Figure 7B indicate that the final head loss decreases with increasing \(X_{\sigma}\). As reported previously, both a higher head loss and a lower \(X_{\sigma}\) indicate a decreased SS permittivity and a more significant level of blockage [16]. Moreover, the previous studies report a linear correlation between the head loss and \(X_{\sigma}\) for \(X_{\sigma}\) values of <3, with fluctuations in the head loss at \(X_{\sigma}>3\), probably due to the re-dispersion of the deposited SS [38]. The present and previous results thus support the conclusion that an increased level of SS deposition is accompanied by an increased probability of re-dispersion. Figure 7: The correlations between the head loss and (**A**) the parameters \(k_{a}\), \(k_{d}\), and ln (\(\sigma_{m}\)); and (**B**) \(X_{\sigma}\).
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 5 The PK Program Verification with MAVRIC
### Isotropic Gamma Sphere with a Slab Shield  
The PK calculations of the uranium (\"black\") sphere 25 cm in radius and point detector located 100 cm from the sphere center are presented next. The concrete slab shield (2.35 g/cm\({}^{3}\)) of variable thickness (1, 5, 10, 20, 30, 40, and 50 cm) is located symmetrically between the sphere and point detector. The spherical surface source has an intensity of 1 phot/s and is emitting photons of 1 MeV isotropically. The results obtained were again verified using the MAVRIC shielding sequence of SCALE6.1.3 with the same simulation parameters as before. Figure 15 shows a good agreement of PK and MC total fluxes with larger differences of \(-\)16% and \(-\)27% for slab thicknesses of 40 cm and 50 cm, respectively.  
The exceptionally good agreement of PK and MC total gamma flux can be noticed. The slab shield is large with uniform material while point detector is distant relative to the source sphere radius. In this case the source behaves as an isotropic point source concentrated at the sphere center. These conditions strongly fulfill PK \"theoretical\" assumptions in \(B\)-factors, so a high similarity of PK to MC solution was obtained.  
The uncollided parts of the flux are practically the same and their ratio to buildup part has a similar behavior. The \(B\)-factors are shown in Figure 16 where the Taylor form shows more conservatism over the Berger form.  
The relative error of uncollided and buildup flux (PK to MC) is shown in Figure 17 with different trends compared to the gamma disc and slab shield solution. The fundamental shape function is preserved in both cases and decreases with shield thickness.  
The MAVRIC photon flux distribution after passing through 50 cm concrete slab is shown in Figure 18 using the CADIS VR technique for optimizing point detector flux.  
Figure 14: MC total flux solution for 0.5 MeV photons using CADIS technique: (**a**) mesh tally total gamma flux; (**b**) relative errors of mesh tally flux.  
Figure 16: Buildup factors for 1 MeV gamma-ray sphere in concrete shield.  
Figure 17: PK to MC relative error for 1 MeV gamma rays in a concrete shield (spherical source).  
Figure 15: Total gamma flux (PK and MC) from the 1 MeV gamma-ray sphere.
### Related Works  
Signal processing for SDR has been intensively studied from multiple perspectives, including MATLAB-based approaches [4; 5] and general guides [6; 7]. An extensive survey on SDR NOMA implementation is presented in [8]. In Ref. [9], performance of multiuser quadrature phase shift keying (QPSK) NOMA was analyzed for two and for three users both numerically and through practical SDR implementation. In [10], the performance of NOMA implementation on a field-programmable gate array (FPGA) platform was presented; Ref. [11] presented system- and level-link simulators for MATLAB; and in [12], link-level simulation using GNU Radio was presented.  
In the context of symbol synchronization, various methods have been proposed to address the challenges posed by the superimposed nature of NOMA signals. Symbol synchronization involves aligning receiver's timing with the symbol timing of the received waveform, which is particularly challenging in NOMA due to the overlapping of signals from multiple transmitters with different power levels. A recent study [13] introduced a novel synchronization algorithm that leverages the unique structure of NOMA signals to achieve accurate symbol alignment. This method improves the synchronization performance by reducing the inter-symbol interference, which is critical for decoding accuracy.  
Timing alignment in NOMA systems is equally crucial, as it ensures that the signals from different users arrive at the receiver within the same time frame. Misalignment can lead to significant performance degradation. In Ref. [14], a robust timing alignment technique that utilizes adaptive filters to dynamically adjust the timing offsets was proposed. This approach was shown to significantly enhance the system's resilience to timing errors, thereby improving the overall reliability of NOMA transmissions. Triggering mechanisms in NOMA systems refer to the initiation processes for various transmission and reception tasks, such as channel estimation and data decoding. Efficient triggering mechanismsare vital for minimizing latency and maximizing throughput. Recent research [15] has been focused on utilizing deep-learning methods for overcoming challenges in NOMA processing. Overall, performance improvement in symbol synchronization, timing alignment, and triggering mechanisms is essential for the practical implementation of NOMA. These techniques collectively address the inherent challenges of NOMA and pave the way for its deployment in future wireless communication systems. Among the performance metrics, the error vector magnitude (EVM) for downlink NOMA was previously investigated, e.g., [16]. In Ref. [17], it was shown that when timing is aligned and link-level synchronization is achieved, NOMA can result in significantly higher rates compared to orthogonal transmission. We note that to the best of our knowledge, symbolic language models were not applied in generating NOMA link-level simulations, and NOMA signal processing design did not include the complete details of the parameter settings.
## 3 Description of Adsorption Isotherms and Modeling Investigation
### Fitting Results and Discussion  
The experimental data depicted in Figure 1 were adjusted using three analytical models. In general, numerical results show that the parameters of monolayer and double-layer models are not theoretically consistent enough to supply a correct understanding of the dexamethasone and hydrocortisone adsorption processes in dual-template MNIPs and MMIPs. As an example, the adsorption capacity values determined using numerical simulations for the dexamethasone and hydrocortisone solutions were very elevated and inconsistent with the experimental results. These models are not appropriate for the study of the adsorption mechanisms of dexamethasone and hydrocortisone solutions. The parameters of the multilayer model were more accurate with the correlation coefficient \(R^{2}\) near unity, and the residual root mean square error (RMSE) values were close to zero (Table 1).  
The choice of the multilayer model can be further explained by the following reasons. Firstly, both MMIPs and MNIPs may exhibit a certain degree of surface roughness or nanoscale irregularities. These irregularities can create additional binding sites for dexamethasone and hydrocortisone molecules, thereby promoting multilayer adsorption [29]. Additionally, molecularly imprinted polymers (MIPs) are synthetic multifunctional materials characterized by a high affinity and specificity for target molecules [30; 31; 32], enabling the adsorption of multiple layers of these molecules onto the polymer's surface. These high-affinity interactions can lead to the formation of multiple layers as dexamethasone and hydrocortisone molecules compete for the available binding sites.  
Therefore, the multilayer model is used in this article to describe dexamethasone and hydrocortisone adsorption on MNIPs and MMIPs. The adjusted parameters of the chosen model are summarized in Table 2. Figure 1 presents the fit of this model to all the experimental adsorption data and shows an excellent accordance.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**adsorbate**} & \multirow{2}{*}{**adsorbent**} & \multicolumn{3}{c}{**Coefficient of Determination \(\mathbf{R^{2}}\)**} & \multicolumn{3}{c}{**RMSE**} \\ \cline{3-8}  & & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** \\ \hline \multirow{2}{*}{**Hydrocortisone**} & **MNIPs** & 0.869 & 0.977 & 0.999 & 0.716 & 0.107 & 0.03 \\ \cline{2-8}  & **MMIPs** & 0.816 & 0.961 & 0.997 & 0.745 & 0.134 & 0.07 \\ \hline \multirow{2}{*}{**Dexamethasone**} & **MNIPs** & 0.726 & 0982 & 0.999 & 0.830 & 0.207 & 0.06 \\ \cline{2-8}  & **MMIPs** & 0.803 & 0.911 & 0.998 & 0.862 & 0.311 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the adjustment coefficient \(\mathbf{R^{2}}\) and the residual root mean square error (RMSE) for the three proposed fitting models.  
Figure 4: Schematic illustration of the distribution of the molecules adsorbed onto a solid according to the multilayer model with saturation.