esa-sceva/satcom-chunk-collection huggingface dataset:
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 5 Numerical Examples  
The effectiveness of the presented guidelines are now validated through numerical examples, the following sinusoidal signal is provided as the input of AG-PSMF:  
\[u=\begin{cases}\sin(20)+0.1\varepsilon(t)&\text{if }t\leq 1.5\;\text{s},\\ 2\sin(20t)+0.1\varepsilon(t)&\text{if }1.5\;\text{s}<t\leq 3\;\text{s},\\ \sin(40t)+0.1\varepsilon(t)&\text{otherwise}.\end{cases} \tag{9}\]  
According to the guidelines, the values of parameters are set as \(H=5\), \(N_{\max}=50\), and \(R=0.0001\). Besides that, \(F=1000\) and \(T=0.0001\) s are applied.  
Figure 13 shows the output of AG-PSMF under the parameter values recommended by the guidelines (hereafter denoted as the AG-PSMF Guideline). For comparison, the results of AG-PSMF with four different sets of parameter values are also included. The initial states of all conditions are set zeros at \(t=0\) s. The results clearly show the advantage of using \(H>1\). That is, the AG-PSMF Guideline is less prone to overshooting than in the case of \(H=1\). In addition, compared with the cases of \(N_{\max}=25\) and \(R=0.001\), the output amplitude of the AG-PSMF Guideline is closer to that of signal component of the input. Furthermore, the phase lag of the AG-PSMF-Guideline is the smallest among the four cases. As a whole, one can conclude that AG-PSMF performs the best under the recommended parameter values.  
Figure 13: Outputs of AG-PSMF under different values of parameters for a sinusoidal input that is corrupted by white Gaussian noise.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 2 Overview of Ant Colony Algorithm and Improvements
### Improvements on ACA
#### 2.2.2 Improved Pheromone Update Model  
Pheromone distribution follows the rule of even distribution, that is, the pheromone on each segment of the shortest route is evenly distributed. In the foraging process, however, the probability that an ant chooses each segment differs. Figure 2 shows three different routes followed by three ants from grid 1 to grid 16, which are 1-6-11-16, 1-5-6-11-16, and 1-2-3-7-11-16, respectively. Obviously, the route 11-16 has the largest weight in the ants' decision-making. Therefore, it is necessary to improve the original pheromone update model to distribute more pheromone to routes with a higher probability of being chosen.  
The pheromone increment calculation equation is improved to achieve a better convergence performance, to strengthen the role of frequent routes in ants' decision-making, and to preclude the problem of the local optimum, as shown in Equation (7):  
\[\Delta\tau_{i,j}^{k}\ =\ \begin{cases}\mu_{ij}\frac{Q}{k},\text{when ant }k\text{ passes through route }i-j\\ 0,\text{else}\end{cases} \tag{7}\]  
where \(\mu_{ij}\) is the weight coefficient for the route from grid \(i\) to grid \(j\).  
Figure 1: Diagrams of moving directions. (**a**) Seven transferable directions. (**b**) Excellent transferable direction when only expectation heuristic information is considered. (**c**) Excellent transferable direction when only passing heuristic information is considered. (**d**) Excellent transferable direction when heuristic information is comprehensively considered.  
The Softplus activation function is used to define the value of \(\mu_{i,j}\), which is expressed as \(f(x)=\log_{2}[1+e^{x}]\); the value of \(x\) is in the range [0, 1]; and the value of the function is within the range [1, \(\log_{2}(1+e)\)], which monotonously increases in the domain of definition. In this function, \(x\) is defined as follows:  
\[x\ =\ \frac{M_{i,j}-M_{\min}}{M_{\max}-M_{\min}} \tag{8}\]  
where \(M_{i,j}\) is the number of times that the ant chooses the route \(I-j\) during iterations, \(M_{\max}\) represents the maximum number of times that the ant chooses each route during iterations, and \(M_{\min}\) represents the minimum number of times that the ant chooses each route during iterations.
# Efficient and Secure Federated Learning for Financial Applications
## 5 Experiments
### Communication Costs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MNINST**} & \multicolumn{2}{c}{**Fashion-MNIST**} & \multicolumn{2}{c}{**CIFAR-10**} \\ \cline{2-7}  & **MLP** & **CNN** & **MLP** & **CNN** & **MLP** & **VGG16** \\ \hline FedAvg & 840 M & 1332 M & 432 M & 4.68 G & 94.5 G & 156 G \\  & \(\times\)13.6 & \(\times\)6.11 & \(\times\)7 & \(\times\)19.8 & \(\times\)34 & \(\times\)24.6 \\ FedProx & 444 M & 1154 M & 552 M & 5.78 G & 77.5 G & 128 G \\  & \(\times\)7 & \(\times\)5.3 & \(\times\)9 & \(\times\)24.5 & \(\times\)28 & \(\times\)20.2 \\ Ours & 61.8 M & 218 M & 61 M & 242 M & 2.77 G & 6.33 G \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Under non-IID distribution, the upload communication costs required to reach 95% accuracy when the final average convergence is achieved.
## 2 Principles and Methods
### Selection of Paramater Indicators
#### 2.3.1 Normalized Cross-Correlation (NCC)  
The NCC definitions are as follows:  
\[NCC=\frac{\sum\limits_{i=1}^{n}f(i)g(i)}{\sqrt{\left(\sum\limits_{i=1}^{n}f^{2 }(i)\right)\left(\sum\limits_{i=1}^{n}g^{2}(i)\right)}} \tag{7}\]where \(f(i)\) and \(g(i)\) represent the original and reconstructed signals, respectively. \(NCC\in~{}[-1,1]\), where \(-1\) indicates that the two curves are in opposite phases, 0 indicates that they are orthogonal, and 1 indicates that the two curves are identical. The more similar the curves are, the closer the NCC is to 1.
## 2 Numerical Methods
### Damping Calculation Method  
The blade deviates from its equilibrium position when the impulse acts on its surface, and the vibration continues until the energy decays to zero. Damping is calculated using the logarithmic de-cay rates extracted from the time history of the blade displacement response. In the vacuum, the blade vibration always decays with time due to the mechanical damping. In the fluid, the coupled fluid-structure method is used to achieve a blade displacement response, which will diverge due to the summation of the unsteady aerodynamic work on the blade being larger than that of the mechanical dissipation. Otherwise, the blade displacement tends to converge. The logarithmic decrement of the amplitude of the blade's vibration resulting from the damped vibration is shown in Figure 2; see [20] for further detail.  
In this study, the behaviors of the different natural blade modes are determined by the artificial excitation force, which is distributed over the blade nodes and applied for a short time. The blade structural equations are defined in Equation (12), which is applicable when the excitation force is loaded onto the blade:  
\[\mathbf{M}\ddot{\mathbf{x}}+\mathbf{C}\dot{\mathbf{x}}+\mathbf{K}\mathbf{x}  
Figure 6 shows the computational fluid dynamics (CFD) model used for the Navier-Stokes computations. Based on the CFD and FE models, the aerodynamic damping prediction method with an IBPA of \(0^{\circ}\) for different natural modes is detailed. The double passage models are employed to elaborate on the aerodynamic damping prediction method for an IBPA of \(180^{\circ}\).  
The convergence criteria for the fluid analysis inside a stagger iteration in CFX require that the root mean square of the residuals of the mass and momentum is within \(2\times 10^{-5}\). The convergence criteria for the transient structure analysis inside a stagger iteration in ANSYS require that the L2 norm of force is within \(0.5\%\). And the convergence criteria for the stagger iteration require that residuals of all the data that exchanged on the interface are within \(0.01\). The timestep of the analysis is set to \(1.0695\times 10^{-5}\) s.
### Purpose of the Study  
Most of the recent academic literature on technology acceptance stems from a misaligned design. For example, researchers studying technology acceptance in a business setting used a consumer model, and vice versa. In many of the studies that purported to focus on older adults' technology acceptance, this age group made up the smallest portion of the sample. Few studies on older adults' acceptance of technology explicitly targeted older adults, and even fewer attempted to recruit participants who did not have Internet in their homes. This study explicitly investigated older adults' acceptance of IoT in the consumer context using the UTAUT2, correcting these previous research design flaws and gaps in the literature. The purpose of this nonexperimental correlation research design was to explain the extent to which performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit affect northern Virginians' (age 60 and up) intention to use emerging IoT technologies in their homes. The study did not require participants to have experience with, or knowledge of, IoT technologies. The researcher recruited the target population via Internet surveys through SurveyMonkeyTM. The study used the UTAUT2 model to measure the perspectives of 316 adults age 60 and older in the northern Virginia area through SurveyMonkeyTM. Multiple regression analysis revealed that habit, performance expectancy, facilitating conditions, hedonic motivation, social influence, and price value were statistically significant in predicting behavioral intent to use IoT technologies to age in place. These dimensions of the UTAUT2 are essential to the information technology field because the implementation of IoT-enabled technologies in private homes depends on older adults' participation and adoption [30].
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 3 Results
Compared to the simulated phase noise performance in Figure 7, the measured loop bandwidths shrink 50-100 kHz for Loop1 and Loop2, possibly due to some parasitic capacitance in the loop, and the measured phase noise degrades 2-5 dB for in-band and out-of-band noise, which possibly results from the 1/f noise model for the transistor provided by the process. Compared to the Loop1, there are more spurs from Output2 of the synthesizer at 1-10 MHz offset, which is probably due to the noise coupling from the cap-bank biasing and power supply network. Figure 10: Measured results for the dual-loop dual-output frequency synthesizer: (a) at 12.96 GHz for Output1; (b) at 32.535 GHz for Output2. Figure 11a shows the measured output spectrum for the frequency synthesizer at 12.96 GHz with a span of 200 MHz, where the reference spur is below \(-\)69.2 dBc at 67.5 MHz offset. For the full span from \(\sim\)DC to 50 GHz, Figure 11b shows the measured output spectrum at Output1, where the LO1 signal is with the highest power level of \(-\)4.5 dBm, and the second peak is the coupled LO2 (2 \(\times\) fvc02) signal, with \(-\)17.3 dBc around \(\sim\)32 GHz. Although the signal at fvc02 is close to fvc01, the power level is \(-\)39.8 dBc below. Figure 11c shows the measured output spectrum at Output2, where the LO2 (2 \(\times\) fvc02) signal has the highest power level of +2 dBm, and the second peak is \(-\)19.2 dBc at fundamental frequency, fvc02. If spurious products in the mixing process are concerned, an extra bandpass filter can be used to further suppress the fvc02 signal. Table 1 compares the details of the state-of-the-art frequency synthesizers for high-speed communications. The proposed dual-loop dual-output synthesizer shows good phase noise, tuning range, and reference spurs performances. The DC power consumption is higher compared with other works in the table, mainly due to the power hungry HBT transistors in the SiGe process. Figure 11: Measured output spectrum for the dual-loop dual-output synthesizer: (**a**) reference spur level of \(-\)69.2 dBc at Output1; (**b**) full-span spectrum of Output1 with fvc01 as the main output frequency and 2 fvc02 as the second peak; (**c**) full-span spectrum of Output2 with 2 fvc02 as the main output frequency and fvc02 as the second peak.
# Dirac Equation-Based Formulation for the Quantum Conductivity in 2D-Nanomaterials
## 6 Results
### Graphene Conductivity under a Uniform Bias Potential
#### 6.1.2 Conductivity in Function of the Scattering Time  
Again, the comparison shows and excellent agreement. In particular, we observe, from Figure 5, that if the scattering time increases, the conductivity linearly increases accordingly. All the calculations have been performed by setting \(\mu_{0}=2E_{F}=0.34\) eV, a typical values in the experimental environments.  
Figure 4: Conductivity of graphene calculated: (i) by the Dirac formulation (DF) of present method (red lines), and (ii) by the Kubo–Drude formulation (blue lines), for different values of the intrinsic Fermi level \(\mu_{0}=2E_{F}\) (eV), and for \(V_{0}\in[-3,3]\) V.
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 2 Data and Methods
### Methods
#### 2.3.4 GSE Change Error Correction  
When estimating the GMB in High Asia, the penetration of snow by the C-band had to be considered. As the DEM data used in this paper has different bands, differential estimation using the SRTM C/X band DEM in glacial areas were used to correct the penetration depth of C-band radar [65]. The elevation reference of the X-band DEM was WGS84, and, to achieve unification with the C-band DEM, EGM96 model of the Geoid  
Figure 4: **(a)** Ridgeline generated based on NASA DEM in 2000; **(b)** ridgeline generated based on ALOS DEM in 2009; **(c)** ridgeline generated based on InSAR DEM in 2022; **(d)** fitted in 2000, the ridgelines in 2009 and 2022 are fitted with ridgelines.  
height function was used to convert it to the EGM96 elevation reference. The elevation conversion steps are as follows:  
1. The SRTM-X band DEM raster data of the study area were converted to point features in ArcGIS 10.5 with corresponding longitude and latitude attributes added;
2. Based on the point feature's longitude and latitude co-ordinates, the Geoid height function was used to calculate the difference in elevation between the EGM96 geodetic datum and the WGS84 ellipsoid datum;
3. The elevation values of the point feature were then calculated for the EGM96 elevation reference;
4. The point features were converted into raster data based on the elevation reference generated on the EGM96 at a resolution of 20 m.  
The average penetration depth of the C-band DEM in the Tailan River basin was found to be 2.3 \(\pm\) 0.5 m. This value is processed as a systematic error when calculating GSE change. Elevation errors caused by differences in spatial resolution can be corrected by using elevation differences in non-glacier areas [65; 66]. After co-registration and penetration correction, the root mean square error and standard deviation of elevation residuals between different DEM data in non-glacier areas can be preliminarily used to estimate the uncertainty of elevation change.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### Soundfield Reproduction
#### 3.3.2 Listening Room Compensation  
In an acoustic reverberant environment, the multi-path propagation effect introduces echoes and spectral distortions into the generated soundfield. Room equalization has been studied in theory and applied in practice to cancel this effect for sound reproduction in cinema halls, home theaters and teleconferencing applications [122]. An efficient method for correcting room reverberation is by using active room compensation, that is, by applying compensation signals to the loudspeaker input to make a reverberant room problem look like an anechoic room problem. This method, however, requires the knowledge of the underlying acoustic system, i.e., the RIR (room impulse response) or the RTF (room transfer function).  
Compensation schemes based on RIR/RTF modelling are theoretically capable of good performance; however, imperfections in the modelling process generally lead to a reduction of dereverberation performance in real environments [123]. This is further exacerbated by non-static room conditions, e.g., time-variant room responses because of a temperature variation or source/receiver position variations [124; 125]. For soundfield reproduction in time-varying environments, a multiple-loudspeaker-multiple-microphone setup is employed and the RIR/RTF of this acoustic system must be determined online in an adaptive manner. In addition, as the purpose here is to reproduce sound over a region of interest, room compensation should be achieved essentially within the entire region as well.  
In massive multichannel soundfield reproduction systems, for which the number of loudspeakers and microphones are large, active room compensation can be solved computationally and efficiently by using a wave-domain approach [103; 126]. The principle of wave-domain signal representation is to use fundamental solutions of the Helmholtz wave-equation as basis functions to express a wave field over a spatial region as shown in Label (1). Processing directly on the decomposition coefficients therefore controls sound within the region.  
The wave-domain adaptive filtering (WDAF) approach transforms the signals at the microphones and the loudspeaker signals into the wave domain, and then adaptively calculates the loudspeakercompensations signals (Figure 4). Especially, in the wave domain, the compensation filter is forced to be diagonal, and each diagonal entry can be determined from the decoupled adaptive filters [127; 128]. This technique results in parallel implementation and significantly reduces the complexity of the adaption process.  
More complex approaches for room compensation are by using fixed or variable directivity higher-order loudspeakers to minimize the acoustic energy directed towards the walls of a room [129; 130], or by exploiting room reflections to reproduce a desired soundfield [131; 132; 133].
# Femtosecond-Laser-Assisted Fabrication of Radiation-Resistant Fiber Bragg Grating Sensors
###### Abstract  
This paper demonstrates the fabrication of radiation-resistant fiber Bragg grating (FBG) sensors using infrared femtosecond laser irradiation. FBG sensors were written inside acrylate-coated fluorine-doped single-mode specialty optical fibers. We detected the Bragg resonance at 1542 nm. By controlling the irradiation conditions, we improved the signal strength coming out from the FBG sensors. A significant reduction in the Bragg wavelength shift was detected in the fabricated FBG sensors for a radiation dose up to \(10^{5}\) gray, indicating excellent radiation resistance capabilities. We also characterized the temperature sensitivity of the radiation-resistant FBG sensors and detected outstanding performance.  
fiber Bragg grating sensor; radiation resistant; femtosecond laser +
Footnote †: journal: Applied sciences
## 2 Study Area  
The Southwestern corner of the Sinai Peninsula contains the El-Qaa Plain, located between latitudes \(28^{\circ}30^{\prime}\) and \(28^{\circ}40^{\prime}\) North and longitudes \(33^{\circ}17^{\prime}\) and \(33^{\circ}37^{\prime}\) East and neighboring the Gulf of Suez [47] (Figure 1). Its area is roughly 6070 km\({}^{2}\) with a maximum length of 150 km and a maximum width of 20 km [50]. The El-Qaa Plain was defined by the Precambrian eastern mountain region that borders the study area to the East and North and features a maximum elevation of 2624 m and a minimum elevation of 300 m [47]. This section of the Sinai Peninsula comprises several varieties of igneous rock, such as diorite, granite, meta-gabbro, and volcanic rocks [51; 26]. Its sedimentary section comprisesGabal Qabaliat in the northwestern region of the study site and features an elevation of 250 m and a moderate slope toward the El-Qaa Plain, separating the Gulf of Suez from the plain. The central plain is composed mainly of Quaternary deposits, which are not perfectly flat and are dissected by several wadies, alluvial fans, palaya, and terraces [52]. Sherief [26] distinguished between old alluvial deposits and wadi deposits.  
The study area was separated into two sub-areas on the basis of the topographical elevation above mean sea level: (a) the Lowland sub-area, ranging in elevation from 0 to 300 m, includes the Ras-Sudr (29.59\({}^{\circ}\)N, 32.71\({}^{\circ}\)E, 12 m) and Abu-Rudeis (28.89\({}^{\circ}\)N, 33.18\({}^{\circ}\)E, 13 m) stations in the northern part of the area, the El-Tor (28.24\({}^{\circ}\)N, 33.62\({}^{\circ}\)E, 13 m) station in the middle, and the Sharm El-Sheikh (27.93\({}^{\circ}\)N, 34.32\({}^{\circ}\)E, 38 m) station in the South. (b) The highland sub-area, ranging in elevation from 300 to 2000 m, is represented by the Saint-Catherine (28.55\({}^{\circ}\)N, 33.98\({}^{\circ}\)E, 1562 m) station in the middle of the area. Generally, highland receives more of rainfall than lowland.
## Materials and Methods
### Finite Element Models  
Computational modeling provides extensive information under a highly-controlled environment, making it possible to screen numerous device design iterations prior to costly prototyping. The ABAQUS/Standard finite element solver (Dassault Systemes Simulia Corp., Providence, RI, USA) was used with its super-elastic user-defined material subroutine UMAT. This subroutine is able to capture the unique stress-strain behavior with the pronounced hysteresis loop of the nitinol material during simulations [18; 19]. Finite element models were developed to evaluate the device's integrity when subjected to various loading conditions, including multiple device expansions and their subsequent heat treatments during manufacturing, crimping of the device inside a catheter, and releasing it into a blood vessel. These numerical procedures were simulated with the following steps:  
1. Expand the device's diameter to 2.0 mm, 3.0 mm, 4.0 mm, and 5.2 mm. Anneal the device after each expansion to remove residual stresses.
2. Crimp the device inside a 1.6 mm cylindrical catheter for delivery.
3. Release the device into a 5.2 mm artery model.  
A 1.5 mm inner sphere, a 6.0 mm outer sphere, and a 10.0 mm outer cylinder were created in the FEA models to simulate the expansion and crimping procedures. In each expansion and its  
Figure 1: Initial cylindrical configuration of spherical occlusion device.  
subsequent heat treatment, the 1.5 mm sphere, placed inside the device, was expanded sequentially and continuously to open the device to the next target size. The stress and strain in each element were reset to zero to simulate the removal of residual stresses. The device deformed geometry was extracted for the next expansion until the final device diameter of 5.2 mm was reached. The 6.0 mm outer sphere was then placed outside the device to help shape the device into a perfect sphere from the outside. After the shaping step, the 10.0 mm outer cylinder was used to crimp the device to the catheter size of 1.6 mm. Finally, the deployment of the device was simulated by releasing the outer cylinder to 5.2 mm, allowing the device to self-expand to its final dimension. The element type C3D8I (incompatible mode eight-node brick element) was used to simulate the spherical occlusion device, while the element type SFM3D4R (reduced integration four-node quadrilateral surface element) was used for both spheres and the cylinder.