esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[13] investigated the behaviour of sandwich panels with aluminium alloy honeycomb cores under blast loading, observing that there is compromise between load transfer through the core and the increase in energy absorption. [PERSON] et al. [14] demonstrated how the permanent deflection of the skins is strongly affected by the core material properties. All of these works show the strong influence the sandwich core has, not only on the energy absorption, but also on the permanent deflection of the skins. Some authors have explored optimising geometrical parameters of the sandwich panel in order to increase the energy absorption capability without significantly compromising the overall weight of a structure. However, these studies have almost exclusively focused on quasi-static loading. [PERSON] et al. [15] for example, modified the topology of the core in an attempt to increase the bending stiffness and strength of the panel under uniaxial and shear buckling. Under these conditions, those authors achieved a 15% reduction of the weight while simultaneously increasing the strength by 25%. [PERSON] et al. [16] introduced wavy foam cores as a liner of fibre reinforced plastics (FRP) in order to increase the energy absorption capability under uniaxial tensile loading. Using a similar approach, [PERSON] et al. [17] demonstrated the capability of wavy liners under drop weight impacts, with promising results in terms of weight and energy absorption. The work here presented extends this into the dynamic regimes, by exploring and analysing the blast response of sandwich panels with aluminium skins and agglomerated cork cores. Numerical simulations of blast are performed and validated while using the experimental data available from a previous work of the authors [9; 10]. The proposed models are shown to be crucial in the understanding of the kinematic response of sandwich plates to blast loads. Different numerical approaches to model the blast are used and benchmarked for computational efficiency. The main objective is to present a novel contribution for the energy absorption of sandwich plates, proposing and validating an optimised geometry for the core.
## 3 Calculation Results and Analysis
### The Influence of Different Plate Theories on Frequencies  
Figures 11 and 12 are based on different deformation theories to study the influence of the thickness-to-radius ratio of the inner and outer ring clamped annular plates on the fundamental frequencies and to compare the numerical results under different theories to compare the differences. Take the outer radius \(r_{2}=50\) nm; it can be seen from Figure 11 that when the thickness-to-radius ratio is greater than 0.05 and less than 0.15 and the value of rotational velocity is greater than 10 and less than 15, the numerical results based on the Kirchhoff plate theory are significantly greater than the numerical results obtained by the Mindlin plate theory and the Reddy plate theory. In this case, Kirchhoff plate theory is not suitable for the analysis of the mechanical behaviors of the annular plates; it is more reasonable to apply [PERSON] plate theory or Reddy plate theory. When the thickness-to-radius ratio is greater than 0.15 and the value of rotational velocity is greater than 15, it can be seen that the numerical results under the [PERSON] plate theory and the Reddy plate theory are very different, and the Reddy plate theory gradually shows its superiority. It can be seen from Figure 12 that when the thickness-to-radius ratio is less than 0.05, the results obtained based on the three deformation theories are relatively close, and they are all suitable for analyzing the vibration behaviors of the nano-annular plates. When the thickness-to-radius ratio is greater than 0.05, the numerical results based on the [PERSON] plate theory are quite different from the numerical results obtained from the [PERSON] plate theory and the [PERSON] plate theory. In this case, the [PERSON] plate theory is not suitable for the vibration analysis of the nano-annular plates, the [PERSON] plate theory and [PERSON] plate  
Figure 10: The relationship between dimensionless natural frequencies and the temperature change under two different boundary conditions ([PERSON] plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.  
Figure 9: The relationship between dimensionless natural frequencies and the temperature change under two different boundary conditions (Mindlin plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.  
theory can be used at this time. It can be seen that when the ratio of thickness to radius is greater than 0.15, the differences between the results obtained based on the [PERSON] plate theory and the [PERSON] plate theory are smaller, but the results obtained by the [PERSON] plate theory are more accurate.
## 2 Materials and Methods
### Adaptive Co-Pilot--Shared Controller
#### 2.1.2 The Optimization Problem  
The optimization function encompasses multiple minimization objectives \(\left(J_{x}+J_{u}\right)\) and system constraints \(\left(x_{min/max},\,\mathbf{u}_{min/max},\,\mathbf{\Delta}\mathbf{u}_{min/max}\right)\) which are separated into three categories: (1) the tracking performance, to minimize the deviation from the reference trajectory (minimize \(\left(X,Y,\Psi\right)\)), (2) the driving comfort, looking for low drifting and smooth steering wheel corrections (minimize \(\left(\psi,w\right)\)), (3) the reduction of driver effort, which minimizes the control command and its rate of change (\(T_{mpc},\,\Delta T_{mpc}\)). Moreover, safety considerations are added by applying a constraint on the yaw rate (\(\psi\)) to avoid unsafe drifting of vehicle (tested experimentally and congruent with a related work [37]), and also limiting the maximum allowed lateral deviation (defined approximately as half of the lane width, to avoid vehicle departing from the lane). Additional constraints are added to limit the steering wheel behavior (\(\theta,\,w,\,T_{mpc},\,\Delta T_{mpc}\)) based on subjective feeling of drivers in the simulator.
## 4 Experimental Results and Analysis
### Discussion
#### 4.5.5 Performance under Different Noise Conditions  
To verify the reliability and superiority of the method, the effect of different noise intensities on the performance of the diagnostic model is discussed in a comparison with four models from SVM, gcForest, LeNet-5, and Resnet. Gaussian white noise with different signal-to-noise ratios (SNRs) is added to the original signal to construct different composite noise signals and simulate different noise environments. The SNR is defined as follows:  
\[\mathrm{SNR}(\mathrm{dB})=10\log_{10}\left(\frac{\mathrm{P}_{\mathrm{signal} }}{\mathrm{P}_{\mathrm{noise}}}\right) \tag{28}\]  
where \(\mathrm{P}_{\mathrm{signal}}\) denotes the signal power and \(\mathrm{P}_{\mathrm{noise}}\) denotes the noise power.  
The training and test samples in the dataset under a 0 HP load were selected. Gaussian white noise with SNRs of \(-6\), \(0\), and \(6\) dB were added to the test samples. The training strategy was the same for all 4 models, and each model is trained 10 times (50 epochs each) under the original sample to obtain 10 training models. Ten training models of each model were tested with noise test samples of different SNRs. The final results were averaged, and the experimental results are shown in Figure 23. When the SNR varied from \(-6\) to \(6\) dB, only the IGVMD-1 DCNN model consistently maintained more than 99% accuracy, which was significantly better than the other four models. In addition, the SNR = \(-6\) dB indicates  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Serial Number** & **Model** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline
1 & SVM & 83.50 & 88.80 & 88.70 & 88.89 \\
2 & gcForest & 96.70 & 96.60 & 96.47 & 96.44 \\
3 & LeNet-5 & 98.48 & 98.43 & 98.41 & 98.22 \\
4 & Resnet & 99.58 & 99.58 & 99.58 & 99.56 \\
5 & IGVMD- & 99.98 & 99.98 & 99.98 & 99.98 \\  & 1 DCNN & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison results with the different models using the CWRU dataset.  
Figure 22: The cross-validation results of different models with CWRU.  
that the noise power is much greater than the original signal power, indicating that the model is effective in detecting very weak fault signals. The IGVMD-1 DCNN model has good performance in strong noise scenes, which indicates that the model has good noise robustness performance.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 2 Literature Review
### Computational Geometry  
Computational geometry has been used to construct and deconflict airspace geofence volumes and to detect/prevent airspace boundary violations (onboard). A scaling algorithm was developed for two-dimensional keep-in/keep-out concave polygon geofences in [6]. This paper uses vehicle performance constraints and steady wind conditions to generate scaled \"warning\" and \"override\" geofence boundaries. Once a UAS crosses one of these boundaries, onboard GNC can trigger a corrective response [25] or flight termination. In [26, 27, 28], algorithms for polygon set operations (i.e., polygon intersections and unions), polygon clipping, convex hull, and point-in-polygon were developed. We use these algorithms to detect and resolve geofence boundary conflicts and generate new geofence volumes by merging conflicting boundaries. A UAS geofence violation detection method was defined in [7] using Ray Casting [29]. A Triangle Weight Characterization with Adjacency (TWCA) algorithm was developed as a faster real-time geofence violation detection method in [8]. TWCA divides geofence into a finite number of triangles and then finds UAS location in a pre-generated adjacency graph. In [9], a 3-D dynamic geofence (\"moving geofence\") was constructed using maximum cruise time, speed, and range of the UAS as a pre-departure flight planning algorithm. This paper also proposes a convex hull approach to find conflicts between current and newly submitted flight plans.
## 5 Conclusions
### Applications  
The presented manipulator, based on the actuator reduction method, and by employing only three actuators, may be used in a variety of applications, which would be performed through a larger version of the manipulator, employing an elongated arm. An example of applications include the inspection of tall structures, such as chimneys, silos, and wells. The rotating base of the manipulator and its arm's bending ability can facilitate the inspection of the full interior of a structure. Another application example is the inspection and recovery of underwater objects by placing a hook or gripper at the arm's tip to pick or grab the object, which can then be recovered by pulling up the arm's tip until it reaches the water surface. Moreover, based on the arm's thin profile and light weight, a suitably updated version of the manipulator with a longer arm can be used on space vehicles for inspection and recovery tasks related to objects in their vicinity. This includes the recovery of satellites or the collection of space junk.
# Magnetic Self-Healing Composites: Synthesis and Applications
## 4 Applications
### Slippery Surfaces  
Droplet splashing and manipulation onto surfaces is a key phenomenon for applications such as microfluidic systems, microreactors and electronic refrigerators [141; 142]. Tailoring the chemistry and topography can lead to the design of non-wetting surfaces with substantially diminished contact when exposed to liquids performing as, for example, excellent lubricants [143]. However, an inherent weakness of such types of complex surfaces is their vulnerability to too-aggressive forces, such as mechanical friction or high shear flow of viscous liquids. Thus, worn-out surfaces that possess healable properties to recover the non-wettability corresponding to the undamaged material state are highly desired. Magnetic self-healing slippery surfaces have been reported in the literature as an interesting route to recover both hydrophobicity and mechanical properties when exposed to aggressive damage. [PERSON] et al. prepared slippery liquid-infused porous surfaces with mag  
Figure 9: (**a**) A schematic representation of the interface reconstruction process of a magnetic self-healing supercapacitor induced by NIR irradiation [130]. (**b**) The healing of a yarn-based self-healing supercapacitor with excellent reconnectivity promoted by magnetic interactions. Adapted with permission from [131]. Copyright 2015, American Chemical Society. (**c**) An artificial skin based on a triboelectric nanogenerator, showing excellent conductive property recovery when the material is submitted to catastrophic damage. Adapted with permission from [133]. Copyright 2017, Elsevier.  
netic and self-healing properties [144]. The proposed system possesses a dual-crosslinked polymer-particle network containing strong irreversible epoxy-amine crosslinks, together with reversible Fe-catechol sacrificial reversible crosslinks. The strong irreversible covalent crosslinks provide great material toughness, while the reversible bonds confer great stretchability and self-healing capacity. Finally, swelling silicone oil lubricant within the matrix provides excellent surface hydrophobicity and great slipperiness to water droplets, which are mostly recovered together with the mechanical properties upon several healing cycles when the material is damaged. [PERSON] et al. reported, for the first time, a thermally triggered magnetic slippery surface with healing capacity based on a thin film of ferrofluid/oil supported on a ferrite magnetic tape substrate [145]. Droplets can be manipulated at the liquid-liquid interface under Marangoni convection generated by temperature gradients on the surface without any viscosity restrictions. The simplicity of the designed system overcomes the crucial drawbacks of other state-of-the-art slippery surfaces such as custom micro/nanofabrication or complex surface treatments. Table 3 offers a summary of the variety of applications in the literature reporting magnetic self-healing composites, together with the fabrication methods, self-healing mechanisms and magnetic fillers incorporated.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design
### PCB Implementation  
Naturally, a compact PCB design is paramount to obtain a significant size reduction when implementing custom hardware instead of using a development board or software radio. To this end, a lot of consideration went into a compact 4-layered PCB layout for this sensor node. The final result is depicted in Figure 5 and measures 70 mm by 32 mm.
# Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems
## 5 Conclusions  
In this paper, we present the AKSD algorithm, which reduces the computational complexity of the KSD for large MIMO systems. By employing the adaptive threshold, the proposed AKSD algorithm significantly reduces the number of visited nodes while preserving nearly the same BER performance as the conventional KSD. The adaptive threshold depends on the SNR, layer index, and ratio between the minimum and sub-minimum path metrics in each layer of the tree. In the AKSD algorithm, only the nodes whose path metrics are within the threshold from the \(K\)th smallest path metric are retained. The simulation results show that the proposed scheme provides an improved trade-off between the complexity and BER performance compared to the conventional KSD and adaptive/fixed threshold-aided IKSD algorithms. For future work, the lattice reduction-aided adaptive KSD algorithm can be considered to further reduce the complexity. Furthermore, the deep learning-aided optimization of parameters such as \(\alpha\) for the proposed scheme can be an interesting research direction for future work.  
Data curation, U.U.; Investigation, U.U.; Methodology, U.U. and K.L.; Supervision, K.L.; Writing--original draft, U.U.; Writing--review and editing, U.U. and K.L.  
This study was supported by the Advanced Research Project funded by the SeoulTech (Seoul National University of Science and Technology).  
The authors declare no conflict of interest.
## 5 Discussion
In the case of a C-PCL transmitter with a wideband sounding signal, e.g., Linear Frequency Modulation, when a kind of frequency-dependent array is used in the PCL, another additional issue arises. This is a possible change of the PCL's responding beam angle during the C-PCL frequency sweep. For the quasi-constant position of the C-PCL (during the sweep time) its receiver receives an amplitude and phase modulation of the received signal during the sweep time. This modulation is periodical and synchronized with the frequency sweep. Then, one of the possible solutions to overcome this is a kind of receiving channel equalization. It is based on the synchronic modulation of the receiver gain and additional phase shifter in order to obtain a constant received power and minimalized phase changes. A real design of a C-PCL system should be started from the tactical point of view. The subsequent analysis steps should cover the following issues, assuming a military mission in an unknown area:  
* Size of area dedicated to be searched through, and the terrain profile;
* Location of known radar stations--based on intelligence data or direction finding devices;
* Prediction of possible location of PCL radars, assuming that they may serve as so called \"gap fillers\"--additional radars used to maintain full coverage of surveillance area;
* Location of DVB-T/FM/GSM transmitters and their power. The information above allows the prediction of the background signals power to be performed. These data are very important in estimating the C-PCL performance. In order to merge and use the information above, one may develop a computer simulation engine which would allow the various scenarios to be analyzed. This kind of simulator would help in the process of C-PCL design. The authors plan further research in this direction. The practical implementation of this method of PCL detection may rely on the use of Unmanned Aerial Vehicles (drones) with an adaptively defined search trajectory. One may further analyze and optimize the configuration of a distributed C-PCL system with the use of several UAVs. The sounding signal transmitters may be placed on two drones separately. Moreover, the IMD receiver may be placed on another separate drone or even multiplied and placed on several drones. The methodology of scanning the given area may be optimized according to various criteria, e.g., power consumption, investigation time of an area, accuracy of localization of the PCL system, or maximization of the detection distance. The distributed \"smart\" C-PCL system based on a number of drones with sounding transmitters and IMD receivers may overcome issues related to optimal interception of IMD signals emitted by the circular antenna array of PCL and, moreover, allows a fast PCL localization to be done.
## 5 Simulation Analysis
### Analysis of AREG  
As mentioned in Section 4.1, AREG detects the NS gap using the ratio of the NS gap to the NN gap. In order to verify the performance of AREG, the eigenvalues, the gaps of eigenvalues, and the values of AREG are numerically analyzed. The parameters are set to \(M=7\), \(\zeta=\eta/2\), \(D=3\), \(\theta_{1}=-30^{\circ}\), \(\theta_{2}=45^{\circ}\), \(\theta_{3}=60^{\circ}\), AWGN with \(\sigma_{\text{N}}^{2}=1\), \(\text{SNR}=0\) dB, and \(L=1000\). Under these settings, \(\hat{\mathbf{R}}_{xx}\) is generated, and the eigenvalues are calculated.  
Figure 2 shows the results of numerical analysis of AREG. Panel (a) shows the eigenvalues, where \(\hat{\lambda}_{1}\) to \(\hat{\lambda}_{4}\) denote the noise-subspace eigenvalues and \(\hat{\lambda}_{5}\) to \(\hat{\lambda}_{7}\) denote the signal-subspace eigenvalues. Because of \(\sigma_{\text{N}}^{2}=1\), the values of noise-subspace eigenvalues are close to 1, while the values of signal-subspace eigenvalues are comparatively greater than 1.  
Panel (b) shows the gaps of eigenvalues, where \(\Delta\hat{\lambda}_{1}\) to \(\Delta\hat{\lambda}_{3}\) denote NN gaps, \(\Delta\hat{\lambda}_{4}\) denotes the NS gap, and \(\Delta\hat{\lambda}_{5}\) and \(\Delta\hat{\lambda}_{6}\) denote SS gaps. This result shows that the NN gaps are comparatively smaller than the NS gap is; however, the greatest value is \(\Delta\hat{\lambda}_{6}\), which is the SS gap. This is why the ratio of the NS gap to the NN gap is used in AREG to avoid wrong estimation which can be caused by using the greatest gap of eigenvalues for source enumeration.  
Panel (c) shows the means of the accumulated gaps of eigenvalues, i.e., \(\frac{1}{\gamma}\sum_{k=1}^{i}\Delta\lambda_{k}\) from (14). The means of the NN gaps (when \(i\) is 1, 2 and 3) are relatively small, while the mean of the NN gaps and the NS gap (when \(i\) is 4) and the mean of the NN gaps, the NSgap, and the SS gap (when \(i\) is 5) are comparatively greater than the means of the NN gaps are; this reduces the value of AREG even if the greatest gap is the SS gap because the denominator of AREG is increased when the NS gap or SS gap are included. As a result, the wrong estimation of the NS gap can be prevented.  
Panel (d) shows the values of AREG. The result shows that AREG(3) is significantly greater than the other values of AREG--about eight times greater than AREG(4). According to (17), the estimated number of sources \(\hat{D}\) is 3; this result shows that AREG can estimate the right number of sources in an 0 dB SNR condition. In addition, under the same condition, 10,000 cases of AWGN are randomly generated, and the performances of AREG and AIC are compared. The result is that AREG estimates the number of sources in 100% accuracy while AIC has 90.03% accuracy.
# Wind Turbine Enhancement via Active Flow Control Implementation
## 4 Results
### Airfoil Section Chosen for the AFC Implementation  
From the two sections analyzed, only Section 54 presents flow separation; this section is therefore chosen for AFC implementation via a parametric analysis. AFC is capable of reducing drag and increasing lift forces; its implementation shall generate a reduction in aerodynamic loads on the blade's downstream direction while increasing the torque generated.  
The placement of the AFC groove is based on previous research works [35; 36; 37], where it was concluded that the optimum location for the groove was in the vicinity of the boundary layer separation point. Which for the chosen section occurs at \(x=2.566\) m, \(x/C=0.423\) (approximately 42 % of the airfoil's chord). As defined in Equation (22), the groove width is directly related to the jet momentum coefficient. For the present study, a width of \(h=0.5\) % of the airfoil chord was selected, a dimension widely accepted in the literature [23; 24; 50]. In the present parametric study, jet position and jet width were kept constant across the entire set of CFD simulations; therefore, the remaining three AFC parameters, momentum coefficient, jet inclination angle, and jet pulsating frequency, are the ones to be optimized. The parametric optimization employed consists of maintaining constant the jet inclination angle and jet pulsating frequency while changing the jet momentum coefficient until obtaining the maximum efficiency of the airfoil. The optimum value of the momentum coefficient, as well as the previous value of the jet frequency, will be kept constant on the second step of the process; different values of the jet inclination angle are evaluated until obtaining the one generating the maximum airfoil efficiency. On the final stage of the process, the changing variable will be the jet pulsating frequency.  
To perform the parametric study, it is needed to modify the existing baseline mesh of Section 54, implementing a slot in the upper surface of the airfoil upwind of the separation point (\(x/c=0.4\)), as depicted in Figure 5a. A zoomed view of the mesh implemented in the vicinity of the jet groove has been previously introduced in Figure 5b.