esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary of Efficient FFT Algorithms  
The pruning algorithms proposed in [58; 59; 60; 61; 62; 63], work for specific zero-input/output distributions. A more generic algorithm that works for any zero valued input distribution was proposed in [64]. The conditional statements were used for deciding whether a node in the signal flow graph has to be computed or not. The time taken to execute the conditional statements reduces the efficiency of the FFT. Thus, modified pruning algorithm was proposed to avoid using conditional statements [65]. The configuration matrix used in [65] requires high data storage capabilities for large size FFTs. The configuration matrix size used for defining the nodes to be executed is reduced by introducing butterfly identifier and using partial FFT pruning in [67]. Thus, there was a trade-off between complexity reduction and memory requirement.  
Split Radix FFT pruning algorithm [74] is able to reduce the computational complexity by applying pruning to split radix FFT instead of radix-2 FFT. It was shown in [74] that, the SRFFT pruning technique has the least computational complexity for any arbitrary distributions of the zero inputs/outputs. However, all these FFT pruning algorithms require some preprocessing operational overhead. In Traced FFT Pruning algorithm [68], the preprocessing operation is made simple and recursive. In the application of OFDM based cognitive radio systems, the hardware implementation of FFT pruning algorithms become complex due to arbitrary distributions of the zero inputs/outputs. Hence, Transform decomposition method was proposed in [69; 70], which is more efficient and flexible than FFT pruning algorithms in the view point of hardware implementation.  
Algorithms proposed in [76; 77], are used for WHT-spread OFDM systems. These algorithms combine WHT and DFT computations into a single transform. While FWFT is applicable for computing transforms of length in power of four, T-transform can be applied for the computation of transforms of any length. Sparse FFT algorithm proposed in [78; 79; 80] is suitable for any signal that is sparse in frequency domain and hence, the algorithm is much suitable for compressive spectrum sensing.
## 3 Materials and Methods
### Architecture Element Boundary Identification Methodology
#### 3.3.4 Differences in Oil and Gas Content  
Generally, sand bodies from the same lobe, after excluding structural differences, will have similar oil and gas content. During development, the characteristics of contemporaneous lobes should also be similar. If two nearby wells reveal significant differences in oil and gas content within their channel sand bodies, it can be concluded that they do not belong to the same lobe sedimentation period. For example, the sand body in well K212 is interpreted as an oil and gas reservoir, while the sand body in well K312 is interpreted as a dry layer, and the sand body in well K8 is interpreted as a poor oil layer. Therefore, a boundary between lobes must exist between these wells, and they are interpreted as different lobes (Figure 4d).  
While the four boundary identification markers were established with quantitative criteria and supported by field examples (Figure 4), practical application faces two key constraints. First, subjectivity in ambiguous cases, approximately 12% of lobe boundaries  
Figure 4: Identification markers of lobe boundaries. (**a**) Facies transition; (**b**) Elevation difference at sand body base; (**c**) Thickness variation trend at sand body center; (**d**) Hydrocarbon-bearing property contrast.  
simultaneously satisfy multiple criteria, requiring contextual prioritization. Second, the absence of high-resolution seismic profiles (due to Gobi terrain acquisition challenges) limits lateral continuity validation between wells > 500 m apart, potentially affecting lobe connectivity interpretations. In practice, a comprehensive analysis integrating geological context, dynamic production data, and probabilistic modeling is recommended when encountering complex boundary scenarios.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 2 LPWAN and LoRa
### Security  
Since LoRa devices operate for as long as 10 years after deployment, security must be future-proof [11]. The LoRaWAN security design adheres to principles such as standards, well-vetted algorithms, and end-to-end security, and it provides mutual authentication, integrity, and confidentiality. Mutual authentication is verified between a LoRa node and a LoRa gateway through Over-the-Air Activation (OTAA). This proof is made by calculating the AES-CMAC using AppKey at the device's join request and receiver.  
LoRa MAC and application messages are provided with origin authentication, integrity protection, replay protection, and encryption. This protection, combined with mutual authentication, allows network traffic to remain unchanged, comes from legitimate devices, is unintelligible to an eavesdropper, and prevents malicious actors from capturing and replaying. LoRaWAN security implements end-to-end encryption for application payloads exchanged between the end devices and application servers. All LoRaWAN traffic is protected using two session keys. Each payload is  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **PDR [\%]** & **Throughput [bps]** \\ \hline Number of channels & increase & increase \\ Number of nodes & decrease & increase \\ Payload length & decrease & increase \\ Data period & increase & decrease \\ \hline \hline \end{tabular}
\end{table}
Table 3: LoRa MAC Parameters.  
encrypted by AES-CTR and transmits a Message Integrity Code (MIC), which is filled with a frame counter and AES-CMAC, to prevent packet tampering and packet replay.
## 4 Model Stability Analysis
### Analysis of System  
In the evolutionary game of multiple groups, the strict Nash equilibrium is the stable solution of the evolutionary game [53]. In the asymmetric evolutionary game constructed in this article, the strict Nash equilibrium is a pure strategy. According to \(F(x)=0\), \(F(y)=0\), \(F(z)=0\), the system equilibrium points are \(\rm E_{1}(0,0,0)\), \(\rm E_{2}(1,0,0)\), \(\rm E_{3}(0,1,0)\), \(\rm E_{4}(0,0,1)\), \(\rm E_{5}(1,1,0)\), \(\rm E_{6}(1,0,1)\), \(\rm E_{7}(0,1,1)\), \(\rm E_{8}(1,1,1)\), \(\rm E_{9}(0,\left(F_{\rm c}+F_{\rm p}+T_{\rm g}-C_{\rm g}\right)/(M_{\rm p}+F_ {\rm p}+T_{\rm g})\), \((C_{\rm cfr}-C_{\rm p})/(F_{\rm p}+M_{\rm p})\), \(E_{10}(\left(F_{\rm c}+F_{\rm p}+T_{\rm g}-C_{\rm g}\right)/(F_{\rm c}+T_{\rm g })\), \(0,\left(C_{\rm co}-C_{\rm cf}-C_{\rm cfs}-C_{\rm cfr}\right)/F_{\rm c}\)), \(\rm E_{11}(\left(F_{\rm c}-M_{\rm p}-C_{\rm g}\right)/F_{\rm c}\), \(1,\left(C_{\rm co}-C_{\rm cf}-C_{\rm cfs}-R_{\rm c}\right)/F_{\rm c})\), \(\rm E_{12}(\left(C_{\rm cfr}-C_{\rm p}\right)/C_{\rm cfr}\left(C_{\rm co}-C_{ \rm cf}-C_{\rm cfr}\right)/(F_{\rm c}-C_{\rm cfr})/(F_{\rm c}-C_{\rm cfr})\), \(0,\) and \(\rm E_{13}(\left(C_{\rm cfr}-M_{\rm p}-F_{\rm p}-C_{\rm p}\right)/C_{\rm cfr} \left(C_{\rm co}-C_{\rm cf}-C_{\rm cfr}-C_{\rm cfs}-C_{\rm cfr}-F_{\rm c}\right) /(R_{\rm c}-C_{\rm cfr})\), \(1).\) Because \(\left(C_{\rm co}-C_{\rm cf}-C_{\rm cfs}-R_{\rm c}\right)<0\), \(\rm E_{11}\) is meaningless. According to the replication dynamic equation of the system, the Jacobian matrix and determinant are constructed as follows:  
\[J=\left[ \begin{array}{cc}(1-2x)(R_{\rm c}-C_{\rm cfr})y+x_{\rm c}+C_{\rm sfr}+ C_{\rm sfr}+C_{\rm sfr}-C_{\rm co}&x(1-x)(R_{\rm c}-C_{\rm sfr})\\ y(1-y)C_{\rm cfr}\\ z(1-z)(\left(y-1\right)T_{\rm g}-F_{\rm c})&z(1-z)(\left(y-1\right)T_{\rm g}-F _{\rm c})&z(1-z)(\left(y-1\right)T_{\rm g}-F_{\rm c})\end{array} \right] \tag{22}\]  
Bring the above strategy equilibrium points into matrix \(J\) and obtain the corresponding eigenvalues of each point, as shown in Table 4.  
According to Lyapunov Stability theory, the eigenvalues of the Jacobian matrix and determinant can determine the asymptotic stability of the equilibrium point [54]. The equilibrium is evolutionarily stable when the eigenvalues of the matrix are all negative and unstable when at least one of the matrix eigenvalues is positive. According to Table 4, there are \(\lambda_{3}>0\) for \(\rm E_{1}\), \(\lambda_{2}>0\) for \(\rm E_{2}\), \(\lambda_{1}>0\) for \(\rm E_{3}\), \(\lambda_{2}>0\) for \(\rm E_{6}\), \(\lambda_{1}>0\) for \(\rm E_{7}\), and \(\lambda_{3}>0\) for \(\rm E_{8}\), so that \(\rm E_{1}\), \(\rm E_{2}\), \(\rm E_{3}\), \(\rm E_{6}\), \(\rm E_{7}\), and \(\rm E_{8}\) are the unstable points.
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 6 Device Authentication
### Body Portable Devices  
This category pertains to any other WBAN devices that should be worn on any part of the body except the hands or head. Most of these studies used the Accelerometer Sensors and Fingerprint for WBAN Authentication [24; 25; 46]. These techniques were used several times and are often considered as the first and the oldest phase of the WBAN authentication. A paper identified the user using a portable device that records their accelerometer signals from gait signals. The device should be worn on a belt, similar to carrying a mobile phone attached to the waist [24]. The device consists of three dimensional accelerometer, two perpendicularly positioned Analog Devices ADXL202 JQ accelerometers (Norwood, MA, USA). It will record the accelerometer signals of gait signal 256-Hz sampling frequency on a laptop with National Instruments Lab 1200 DAQ card (National Instruments, Austin, TX, USA). The user should walk 20 Meters in normal, slow, and fast walking speeds in order to detect the signals. A typical gait pattern is shown in Figure 5 It requires several other testing sessions to identify the users from their gait.  
Personal devices, such as smartphones, typically include motion sensors, and another study utilized Google Nexus 5 and Samsung Galaxy S5 smartphones for biometric authentication [25]. The phones were placed in the subject's pockets and both the accelerometer and gyroscope sensors were used for authentication. Eighteen activities, including walking (gait), were evaluated as biometric signatures. This study also evaluated biometric performance when the smartphone sensors were combined with the sensors on a commercial smart watch.  
Other studies discussed the Fingerprint authentication system to give access to the person who will assist the patient [46]. This type of authentication access can be implemented by using fingerprint authentication systems which have become cheap and available in the market. The biometric fingerprint recognition system is used in the new generations of smart mobile phones such as Samsung and Apple. Other access devices are using electronic fingerprint recognition systems to grant access to doors or computer devices. Adding the fingerprint recognition system to the WBANS Body Portable Devices can be an advanced solution which should be considered in the near future.
###### Abstract  
The optimal management strategy of patients with concomitant anterior communicating artery aneurysm (ACoAA) and internal carotid artery (ICA) stenosis is unclear. This study aims to evaluate the impacts of unilateral ICA revascularization on hemodynamics factors associated with rupture in an ACoAA. In the present study, a multiscale computational model of ACoAA was developed by coupling zero-dimensional (0D) models of the cerebral vascular system with a three-dimensional (3D) patient-specific ACoAA model. Distributions of flow patterns, wall shear stress (WSS), relative residence time (RRT) and oscillating shear index (OSI) in the ACoAA under left ICA revascularization procedure were quantitatively assessed by using transient computational fluid dynamics (CFD) simulations. Our results showed that the revascularization procedures significantly changed the hemodynamic environments in the ACoAA. The flow disturbance in the ACoAA was enhanced by the resumed flow from the affected side. In addition, higher OSI (0.057 vs. 0.02), prolonged RRT (1.14 vs. 0.39) and larger low WSS area (66 vs. 50 mm\({}^{2}\)) in ACoAA were found in the non-stenoic case. These acute changes in hemodynamics after revascularization may elevate the rupture risk of ACoAA. The preliminary results validated the feasibility of predicting aneurismal hemodynamics characteristics in revascularization procedures by using multiscale CFD simulations, which would benefit the management of this group of patients.  
Keywords:intracranial aneurysm; revascularization; computational fluid dynamics; multiscale; hemodynamics +
Footnote †: journal: Applied Soft. 2019, 9, 4143; doi:10.3390/appr9194143
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm  
### Problem  
According to the discussion presented in Section 2.1, the objective of this study is to identify the optimal joint delivery path for drones and trucks. This optimization process  
Figure 3: Flowchart of the road vulnerability assessment algorithm.  
considers various factors, including delivery time, delivery costs, and the vulnerability of the road network, while utilizing the known road network and identified failure nodes. This scenario clearly represents a multi-objective optimization problem. The evolutionary algorithm (EA) is a category of heuristic optimization methods that emulate the process of biological evolution. It is characterized by significant features such as robust global search capabilities and a high adaptability [28]. In comparison to traditional deterministic algorithms, the evolutionary algorithm offers distinct advantages in addressing nonlinear, multi-constraint, and multi-objective optimization problems [29]. Consequently, we opt to enhance the evolutionary algorithm to determine the optimal delivery path.
## 2 Materials and Methods  
IL was performed using a continuous-wave Nd:YVO4 laser (Verdi V6, Coherent, Santa Clara, CA, USA) operating at 532 nm. As shown in Figure 1A with a schematic image, the 532 nm beam was split into three equal-energy beams that were combined at the substrate to crosslink the patterning layer, and the angle between the beams and the substrate was 23\({}^{\circ}\). Figure 1B shows the schematic image of interference at the substrate for fabricating PhC structures. The three beams which came from the same source coincided at the patterning layer on the substrate and the exposed area and crosslinked to make PhC structures. After exposure to the three beams for fabricating PhC structures, the sample was exposed to 2 PL setup immediately without post process, such as post-baking and development, as shown in Figure 2. The 2 PL setup used the 800 nm mode-locked Ti:sapphire laser (Mira 900, Coherent, Santa Clara, CA, USA) pumped by solid state laser (Verdi-V10, Coherent, Santa Clara, USA) with 130 fs pulses and a 76 MHz repetition rate. By moving the scanning stage, 2D or 3D structures can be patterned without photomask. The sample for patterning was prepared using a slide glass as the substrate. On top of the glass, we spin-coated 2.5% EPON resin SU-8 (Miller-Stephen- son Chemical Co., Danbury, CT, USA) with a photoinitiator (PC-2506, Polyset Co., Mechanicville, NY, USA) to improve the adhesion with a thickness of 5 nm between the patterning layer and the glass substrate. Subsequently, we spin-coated a 35% SU-8 layer of 2 \(\upmu\)m thickness as the patterning layer.  
Figure 1: A schematic image of the IL setup (**A**) and interference at the substrate for fabricating PhC structures (**B**).  
After spin coating, the sample was prebaked at 65 \({}^{\circ}\)C for 30 min to evaporate the solvent and anneal out the stress caused by the spin coating. The 532 nm laser which has the size of 1 cm focused spot was used to irradiate with a cumulative dose of 0.6 W\(\cdot\)cm\({}^{-2}\) for 3 to 8 s onto the sample to form a PhC pattern of area 1 cm\({}^{2}\). Then without post-baking, the 2 PL system with an 800 nm femtosecond pulsed laser was employed using a microscope with an air objective lens (60\(\times\), NA 0.85 and 1.0, focused spot size < 1 \(\upmu\)m) and computer-controlled piezoelectric stage to make a controlled defect to the PhC pattern. After exposure to the femtosecond pulsed laser, the sample was baked at 95 \({}^{\circ}\)C for 15 min to accelerate the acid-catalyzed reaction, which changes the solubility of the polymer and drives the diffusion of the photoactive compound [21]. The exposed sample was cooled sufficiently and then developed to complete the fabrication process.
## 2 Methods
### RNG k-\(\epsilon\), LES, and IDDES Theory
In a non-stationary turbulent field, large-size vortices dominate the evolution of the flow field, while small-size vortices mainly cause the diffusion of turbulent momentum and are significantly isotropic. The LES method filters the real velocity field \(u_{i}(x_{i},t)\) through a filtering procedure, which resolves only the large-scale turbulence structure, and then selects an appropriate model to solve for the small-scale turbulence. LES methods are able to capture more of the turbulent large-scale motion than RNG \(k\)-\(\varepsilon\) methods. The filtering process is defined as Equation (7), and then linear eddy viscosity models are employed to link the sublattice stresses to the resolved strain rate tensor (Equation (8)). \[\overline{U}_{i}(x_{i},t)=\int G(r,x_{i})u_{i}(x_{i}-r,t)dr \tag{7}\]  
\[\tau_{ij}^{R}-\frac{1}{3}\tau_{kk}^{R}=-2\
u_{SGS}\overline{S}_{ij}=-\
u_{SGS} \Bigg{(}\frac{\partial\overline{U}_{i}}{\partial x_{j}}+\frac{\partial \overline{U}_{j}}{\partial x_{i}}\Bigg{)} \tag{8}\]  
where \(G(r,x_{i})\) and \(\tau_{ij}^{R}\) are the filter function and the sublattice scale stress tensor, respectively; scale factor \(\
u_{SGS}\) indicates the vortex viscosity of the sublattice motion; and \(\overline{U}_{i}\) and \(\overline{U}_{j}\) denote velocities obtained from the filtered decomposition. In the detached eddy simulation (DES), the zones near the sedan's surface are calculated using the RNG \(k\)-\(\varepsilon\) method, while the fully developed zone of turbulence away from the sedan's surface is calculated using the LES method. Consequently, the DES method can notably reduce the number of meshes surrounding the sedan, thus reducing the computational costs while ensuring computational effectiveness. When using the DES model to predict the wind field, [PERSON] et al. found that the measured results on the leeward side of hilly terrain differed from those predicted by the DES model [35]. They attributed this to the improper definition of the boundary layer mesh length, which resulted in the problem of unphysical separation and logarithmic mismatch between the boundary layers. Over the years, researchers have continuously improved and optimized the DES model. The Delayed Separation Eddy Simulation (DDES) method can avoid the separation phenomenon by controlling the parameters of the boundary layer [36]. In addition, the IDDES method has been developed based on DDES to deal with the logarithmic mismatch between the layers. Therefore, the IDDES method based on SST in the RANS model has been identified in this study. In the IDDES method, the scale of length is evaluated by Equation (9):  
\[l=f_{d}(1+f_{e})l_{RANS}+(1-f_{d})l_{LES} \tag{9}\]  
where \(f_{e}\) and \(f_{d}\) denote the lifting and hybrid functions, respectively; and \(l_{RANS}\) and \(l_{LES}\) denote the length scale of the RANS and LES models, respectively.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 2 Time-Cooperative Reentry Problem for Multi-Hypersonic Vehicles  
### Reentry Motion Equation  
The reentry motion equations of multi-hypersonic glide vehicles (MHGVs) are as follows:  
\[\left\{\begin{array}{l}\dot{r}_{i}=V_{i}\sin\gamma_{i}\\ \dot{\theta}_{i}=\frac{V_{i}\cos\gamma_{i}\sin\psi_{i}}{\tau_{i}\cos\phi_{i}} \\ \dot{\phi}_{i}=\frac{V_{i}\cos\gamma_{i}\cos\psi_{i}}{\tau_{i}}\\ \dot{V}_{i}=-\frac{D_{i}}{\eta_{i}}-g_{i}\sin\gamma_{i}\\ \dot{\gamma}_{i}=\frac{L_{i}\cos\sigma_{i}}{m_{i}V_{i}}-(\frac{S_{i}}{V_{i}}- \frac{V_{i}}{r_{i}})\cos\gamma_{i}\\ \dot{\psi}_{i}=\frac{L_{i}\sin\sigma_{i}}{m_{i}V_{i}\cos\gamma_{i}}+\frac{V_{ i}}{r_{i}}\cos\gamma_{i}\sin\psi_{i}\tan\phi_{i}\end{array}\right.. \tag{1}\]  
where, \(r_{i}\) represents the distance from the center of mass of the \(i\)th aircraft to Earth's center, \(\theta_{i}\) and \(\phi_{i}\) are the longitude and latitude of the aircraft, respectively, \(V_{i}\) is the aircraft velocity, \(\gamma_{i}\) and \(\psi_{i}\) are the corresponding flight path angle and heading angle, respectively, \(L_{i}\) and \(D_{i}\) are the resistance and lift, respectively, \(\sigma_{i}\) is the bank angle, which forms the control quantity together with the AOA \(\alpha_{i}\), \(m_{i}\) is the aircraft mass, and \(g_{i}\) is the corresponding gravitational acceleration.  
The calculations of resistance and lift are as follows:  
\[\left\{\begin{array}{l}L_{i}=0.5\rho_{i}V_{i}^{2}C_{Li}S_{ri}\\ D_{i}=0.5\rho_{i}V_{i}^{2}C_{Di}S_{ri}\\ \end{array}\right.. \tag{2}\]  
where \(\rho_{i}\) is the atmospheric density, \(h_{i}=r_{i}-R_{e}\), and \(R_{e}\) is the average radius of the Earth, \(h_{s}=7100\), \(S_{ri}\) is the reference area for the aircraft, \(C_{Li}\) and \(C_{Di}\) are the lift coefficient and drag coefficient of the aircraft, respectively. This paper adopts the form of the lift coefficient and drag coefficient given in Reference [27].
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods
### Band Analysis and Model Evaluation Approaches
In order to determine the impact of uncertainty on GPR model development, this band analysis procedure was repeated both with and without the observational uncertainty. Our best performing GPR models for estimating LAI, LCC and CCC from the Sentinel-2 spectral data, with the propagated observational uncertainty, were further made available for the remote sensing community in a format that can be imported into the ARTMO MLRA toolbox (see the Supplementary Material). In addition to the band analysis using the three-fold cross-validation statistics, we performed an independent validation of the GPR model performance by re-training the model with only half of the observations (i.e., with remaining observations used for validation). We further compared this independent model evaluation to a multivariate linear model, using ordinary least squares regression, which was developed using the most explanatory MAIA/Sentinel-2 bands and calibrated and validated using the same observations as the GPR model. We also quantified the spectral responses of these individual bands to the variables that were measured directly (i.e. LAI and LCC) and, in doing so, determined the extent to which these variables can be retrieved from the single bands. We thus compared the performance of the GPR approach to simple parametric models.