esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### Convergence Study  
From Section 2.2, it can be seen that the disk has a definite number of units, so the convergence analysis mainly focuses on the number of units of the shaft section (taking the number of unit nodes of the shaft section as 3) and the number of unit nodes of the ring plate. The convergence analysis is carried out by taking the rotor made of fiber woven composite with round holes as an example, and the structure of the rotor is as follows: the length of the shaft \(L=0.2\) m, the radius \(r_{1}=r_{2}=0.02\) m; the thickness of the disk \(h_{\rm D}=0.008\) m, the position \(L_{d}=0.1\) m, the inner diameter \(R_{i}=0.02\) m, the outer diameter \(R_{o}=0.1\) m, the number of round holes \(N=4\), the radius of the round holes \(r_{h}=0.025\) m, and the eccentricity of the round holes \(R_{h}=0.06\) m. The number of shaft segment units not used as variables is 5, and the number of ring plate unit nodes is 10. The boundaries of the rotor are set at the two ends of the rotor shaft, both of which are simply supported boundaries and are denoted as S-S.  
Unless otherwise stated, the shaft in the following calculations is made of steel with [PERSON]'s modulus \(E=210\) GPa, [PERSON]'s ratio \(\mu=0.3\), and density \(\rho=7800\) kg/m\({}^{3}\), and the disk material is an orthotropic plain-wo carbon fiber-reinforced resin matrix composite when the total fiber volume fraction is equal to 0.28. The mechanical properties of the materials with different total fiber volume fractions are all given in Figure 3.  
The first six orders of the natural frequencies of the fiber woven composite rotor are given in Tables 1 and 2 for different numbers of shaft segment units and different numbers of ring plate nodes, respectively. From the tables, it can be found that the natural frequency of the rotor converges rapidly with the increase in the number of shaft segment units and the number of ring plate nodes, and stabilizes when the number of shaft units is greater than or equal to 3 and the number of ring plate nodes is greater than or equal to 10. Therefore, the number of shaft segment units is set to 3 and the number of ring plate nodes is set to 10 in the following examples.
## 2 Materials and Methods
### Land Cover Classification
#### Vegetation Indices  
The fused multispectral images were used to generate vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), Structural Index (SI), and Normalized Difference Vegetation Red Edge (NDRE). NIR wavelengths are used to calculate vegetation indices like the NDVI and SI, which are known to be useful for land cover classification [22; 61; 62]; studies also suggest that including Sentinel-2's Red Edge band in a vegetation index, such as the NDRE, can yield more accurate results for the classification of agricultural areas [16; 63].
## 4 New Propagation Model  
The simulation results are categorized into two distinct components: the first pertains to the LoS probability on the street; the FSPL model is applicable for calculating the average propagation loss and the second is additional path loss due to the shadowing effect of propagation buildings for NLoS conditions. Our previous investigation [34] shows that an elevation angle of 60 to 80 degrees provides the most realistic coverage area. However, it is also important to consider other elevation angles, as they may be significant for studying interference and other related factors.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 3 Experimental Section  
### Impedance Testing  
The interface impedance between each bioelectrode and the subject's skin was measured with an impedance analyzer (WK6500, Wayne Kerr Electronics, Bognor Regis, UK) to evaluate the electrode's ability to capture physiological electrical signals and thus ensure high-quality recordings. The measurement protocol was as follows:  
1. Skin preparation: The skin on the volunteer's forearm was cleaned with ethanol to remove surface oils and contaminants.
2. Electrode placement: Two bioelectrodes, each having the same contact area, were affixed to the skin at a fixed inter-electrode distance.
3. Circuit configuration: Metallic leads were attached to each electrode and connected to the two terminals of the impedance analyzer, thereby forming an electrode-skin-electrode measurement circuit.
4. Frequency sweep: The analyzer was programmed to perform a frequency sweep from 20 Hz to 100 kHz, and the impedance spectrum was subsequently recorded.
## 3 Experimental Results
### Rin  
To investigate the RIN for the laser, the RIN spectra including all modes and individual longitudinal modes are measured in the frequency range from 10 MHz to 20 GHz using the Keysight N4371A RIN measurement system; examples are shown in Figure 3a. The RIN values from the entire spectral emission are all below -160 dB/Hz over the 20 GHz frequency range, and an integrated average RIN as low as \(-164.4\) dB/Hz is achieved. The RIN spectra of individual longitudinal modes are assessed by selecting each mode using a tunable optical narrow bandwidth filter. Figure 3a shows a comparison of the RIN spectra of three modes at low (1546.04 nm), center (1550.58 nm), and long (1554.46 nm) wavelength. They all demonstrate similar behaviors and the RIN values measured at high frequencies (>4 GHz) drop down to below \(-140\) dB/Hz for all three modes. Figure 3b shows the integrated average RIN values and the difference of the average RIN from the average value (\(-133.54\) dB/Hz) for 19 selected modes across the wavelength range from 1543 nm to 1556 nm (which contains a total of 56 modes). It is observed that the average RIN is below \(-132.2\) dB/Hz, and the difference is <1.3 dB for the modes between 1543 and 1555 nm. At the long wavelength range close to 1556 nm, the average RIN and the difference are increased to \(-128.7\) dB/Hz and 4.8 dB. However, the measured RIN values of the all-longitudinal modes are below the IEEE standard (\(-128\) dB/Hz) for RIN with Terabit/s communications as demonstrated in [17].
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 4 Crystal Structure
### U-Fe Assessment  
The calculated phase diagram resulting from the present CALPHAD assessment is depicted in Figure 3. Overall improvements including the solubility of iron into the uranium lattice (\(\beta\)-U and \(\gamma\)-U) is provided and is indicated in the invariant reactions described in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method/Refs.** & **Pu\({}_{6}\)Fe** & **U\({}_{6}\)Fe** & **PuFe\({}_{2}\)** & **UFe\({}_{2}\)** \\ \hline CALPHAD (This work) & \(-1.2\) & \(-5.8\) & \(-9.8\) & \(-15\) \\ Experiment & & & & \\
[64]/CALPHAD [4] & N/A & \(-6.8\)/\(-7.3\) & N/A & \(-16.3\)/\(-15.8\) \\ CALPHAD [3] & \(-4.4\) & \(-7.8\) & \(-5.1\) & \(-5.2\) \\ Experiment [47] & N/A & \(-2.33\) & \(-13.1\) & \(-11.30\) \\ Experiment [93] & N/A & \(-2.33\pm 0.72\) & N/A & \(-10.74\) \\ Experiment [49] & \(-1.97\pm 0.65\) & N/A & \(-9.06\pm 0.56\) & N/A \\ Experiment [48] & N/A & N/A & \(-16.6\) & N/A \\ SR-EMTO (This work) & \(-0.50\) & \(-6.17\) & \(-16.25\) & \(-16.0\) \\ FPLMTO (This work) & \(0\) & \(-2.5\) & \(-11.6\) & \(-15.5\) \\ \hline \end{tabular}
\end{table}
Table 6: \(\Delta_{\mathrm{H}}\)(kJ/mol) of Pu-Fe and U-Fe compounds compared to DFT calculations and experiment.  
Figure 2: Calculated Pu-rich side of the Pu-Fe phase diagram based on the present CALPHAD assessment.  
The improved assessment includes better agreement for the congruent melting of UFe\({}_{2}\) compared to [PERSON] [3] and [PERSON] [4], as well as some adjustments to the eutectic temperatures. Similarly, the eutectoid reaction [\(\gamma\)-U \(\leftrightarrow\)\(\beta\)-U + U\({}_{6}\)Fe] was improved with respect to U-composition. The peritectic reaction (reaction 20, Table 5) temperature was updated to include the most recent measurements by [64], which was also considered in the works by [PERSON] et al. [4], but neglected in the most recent assessment by [PERSON] [3]. This CALPHAD assessment is in good agreement for the formation enthalpies for both compounds (UFe\({}_{2}\) and U\({}_{6}\)Fe) when compared to literature and the values calculated by DFT from this work, which were used initial points for the fit.
# Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
## 3 Experiments  
### Vibration of a Cantilever  
#### 3.1.1 Modeling  
The vibrations of a cantilevered beam, shown in Figure 1a, can be described by the dynamic Euler-Bernoulli bending theory under the small amplitude assumption, which can be expressed as follows:  
\[\mu\frac{\partial^{2}\omega(x,t)}{\partial t^{2}}+B\frac{\partial^{4}\omega( x,t)}{\partial x^{4}}=q(x,t) \tag{5}\]  
Here, \(\omega(x,t)\) represents the vertical deflection of the beam at the horizontal position \(x\) and time \(t\). The horizontal position \(x\) varies from 0 to the beam length, \(L\). The parameters \(\mu\) and \(B\) are the mass per unit length and flexural rigidity, respectively. For a rectangular cross-section, the mass per unit length \(\mu\) can be expressed as \(\rho_{s}hl\), where \(\rho_{s}\) is the density of the beam, \(h\) is the thickness, and \(l\) is the width. The flexural rigidity is the product of Young's modulus, \(E\), and the area moment of inertia of the cross-section \(I\), which can be expressed as \(B=EI\). The area moment of inertia of the plate \(I\) can be calculated using the formula \(I=\frac{h^{3}l}{12}\).  
The cantilevered beam is subject to fixed and free end boundary conditions at \(x=0\) and \(x=L\), respectively. These conditions require that \(\omega=0\) and \(\partial\omega/\partial x=0\) at \(x=0\), and that \(\partial^{2}\omega/\partial x^{2}=0\) and \(\partial^{3}\omega/\partial x^{3}=0\) at \(x=L\), and they are denoted as BC,1 to BC,4 in the following configuration. Additionally, to simulate the free vibration of the cantilevered beam, we consider an initial condition corresponding to a static cantilevered plate with end load P. This initial condition is characterized by the initial displacement of \(\omega(x,0)=Px^{2}(3L-x)/6 EI\) and an initial velocity of \(\partial\omega(x,0)/\partial t\) of zero. At the start of the simulation (\(t=0\)), the load is released, and the cantilevered beam begins to vibrate freely, without any transverse load (\(q(x,t)=0\)). The initial displacement and velocity conditions are denoted as IC,1 and IC,2, respectively.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 2 Materials and Methods
### Noah-MP Model  
Noah-MP is enhanced from Noah LSM [21] with expanded and multi-parameterization options [4; 5]. A two-source energy balance module, short-term dynamic vegetation growth model, modified two-stream radiation transfer scheme, dynamic groundwater component, and multilayer snowpack are included in Noah-MP LSM. The multi-parameterization options make it possible for users to try combinations of parameters in different modules to optimize the model performance, such as leaf dynamics, surface-layer turbulence treatment, canopy stomatal resistance, runoff, and groundwater [5]. A crop module has also been added to Noah-MP LSM, improving the simulations of vegetation dynamics and turbulent fluxes for corn and soybean by using several agricultural management data, such as growing degree days (GDDs) and planting/harvesting dates [8; 9; 22].
## 1 Introduction
[PERSON] [30] derived a calculation formula for the seepage flux of slope-wall dams based on the assumptions that the variation of head loss underneath the blanket is linear; the drag coefficient was determined by taking the average value of the seepage flux of the foundation, but this calculation formula cannot apply to longer or more permeable blankets. Besides, the U.S.B.R. [31] recommended an analytical method to estimate the seepage through dam foundation, considering the blanket to be impermeable; [PERSON] and [PERSON] [32] approximated the shape of the concrete face as a triangle and derived a formula for calculating seepage through the dam body; [PERSON] [33] computed the dam leakage mainly based on the subsection method and [PERSON]' law. Furthermore, some scholars used the conformal transformation method to establish a mathematical model and derive a series of seepage calculation formulas for approximating the seepage flux of a rock-fill dam, though this method is complicated to apply in practice [34; 35; 36; 37]. In addition, [PERSON], et al. [38] demonstrated an analytical solution for a triangular blanket and a resultant solution for seepage discharge by introducing a modified Bessel function of the first orders, 0 and 1. [PERSON], et al. [39] proposed a simple calculation method to generalize the steady-state seepage of rock-fill dams with thin impervious elements, from which the seepage flux and apparent overall value of the permeability coefficient for impervious elements could be obtained. However, the above studies mainly focus on dams with higher groundwater levels; little attention has been paid to dams located at a higher altitude and with a lower groundwater level. In addition, the existing methods for calculating leakage are relatively complicated to assess the benefits and evaluate the safety of dams. Thus, a more simplified calculation method of leakage is required. In this study, based on [PERSON]'s law and the assumption of porous continuous media, a simplified calculation method is proposed to estimate the seepage flux of slope-wall rock-fill dams with a horizontal blanket in a steady state. The proposed method considers the influence of dams with different groundwater levels. Finally, two engineering examples are used to validate the proposed method by comparing the results of the proposed method with those of other analytical methods and a numerical method.
## 5 Case Study
### Vulnerability Functions  
Fragility functions are often transformed into vulnerability functions that can be subsequently used in loss assessment [52]. The VMTK wasalso employed in deriving these curves by calculating the probability distribution of the loss rate using the resulting fragility model and a damage-to-loss relationship model (i.e., the relationship between each damage state and the corresponding loss rate).  
The damage-to-loss model used is the one described in FEMA [53], where the expected loss rates for each damage state are as follows: slight damage (LR) = 0.05, moderate damage (LR) = 0.25, extensive damage (LR) = 0.60, and complete damage (LR) = 1.00.  
The average loss rate of the vulnerability functions at each level of intensity measurement (LR I IM) is equal to the result of multiplying the probability of each damage state (P [DS = disi | IM]) by the associated average loss rate (LRdsi), as described in Equation (2) [24].  
\[LR|IM=\sum_{i=1}^{nDS}P[DS={ds_{i}}|IM]\times LR_{{ds_{i}}} \tag{2}\]  
Following this procedure and utilizing the fragility functions and the damage-to-loss model presented earlier, the vulnerability functions were obtained, as depicted in Figures 12 and 13.  
Table 8 presents a summary of the probability of loss ratio in low-rise (RM2/L) and mid-rise (RM2/M) buildings for an expected value of 0.50 g of PGA and 0.50 g of SA (T = 0.5 s), respectively. For example, it can be observed that the loss ratio wasminimal for low-rise (RM2/L) and mid-rise (RM2/M) buildings with a moderate and high seismic design. For a low seismic design, both low-rise and mid-rise structures hada 30% and 44% probability of loss ratio, respectively.
# Extrinsic Bayesian Optimization on Manifolds
## 1 Introduction
Following that work, innovations developed in the same literature include multi-fidelity optimization [39], multi-objective optimization [40], and a study of convergence rates [41]. The observation by [PERSON] [34] that Bayesian optimization is useful for training deep neural networks sparked significant interest in machine learning. Within this trend, Bayesian optimization has also been used to choose laboratory experiments in materials and drug design [42], in the calibration of environmental models [43], and in reinforcement learning [44]. However, most studies mainly focus on the input domain in a Euclidean space. In recent years, with the surging collection of complex data, it is not common for the input domain to have such a simple form. For instance, the inputs might be restricted to a non-Euclidean manifold. [PERSON]. [45] proposed a general extrinsic framework for GP modeling on manifolds, which depends on the embedding of the manifold in the Euclidean space and the construction of extrinsic kernels for GPs on their images. [PERSON]. [46] learned a nested-manifold embedding and a representation of the objective function in the latent space using Bayesian optimization in low-dimensional latent spaces. [PERSON]. [47] exploited the geometry of non-Euclidean parameter spaces arising in robotics by using Bayesian optimization to properly measure similarities in the parameter space through the Gaussian process. Recently, [PERSON]. [48] implemented Riemannian Matern kernels on manifolds to place the Gaussian process prior and applied these kernels on the geometry-aware Bayesian optimization for a variety of robotic applications, including orientation control, manipulability optimization, and motion planning. Motivated by the success of Bayesian optimization algorithms, in this work, we propose to develop the eBO framework on manifolds. In particular, we employ an extrinsic class of Gaussian processes on manifolds as the surrogate model for the objective function and utilize the uncertainty for calculating the acquisition function. An acquisition function can also be defined from this surrogate to decide where to sample [49]. The algorithms are demonstrated and applied to both simulated and real data sets to various optimization problems over different manifolds, including spheres, positive definite matrices, and the Grassmannian. We organize our work as follows. In Section 2, we provide a streamlined introduction to Bayesian optimization on manifolds employing the extrinsic Gaussian process (eGP)on manifolds. In Section 3, we present a concrete illustration of our eBO algorithm in the extrinsic setting in terms of optimizing the acquisition function from eGP. In Section 4, we demonstrate numerical experiments on optimization problems over different manifolds, including spheres, positive definite matrices, and the Grassmann manifold, and showcase the performance of our approach.
## 2 Reviewing Background and Related Work
### Data Encryption Transmission Technology  
As the TCP/IP transport layer provides reliable network transmission, packet encryption is not performed in the standard packet state [24]. This ensures that packets can be checked quickly and easily, with the checksum only verifying whether the package is being transmitted. If damage occurs during the process, inspection operations must be carried out. Therefore, in the current network transmission process, we will use secure connection operations such as SSH (Secure Shell Protocol), SSL (Secure Socket Layer), and other associated technologies to perform packet security encryption operations at the application layer. In many network communication applications, a VPN is commonly used to provide comprehensive channel encryption [25]. This system connects the data port at both ends and uses encryption to prevent tampering and eavesdropping during the transmission of information.  
However, this method may encounter another challenge. For example, in Figure 2, when the user enters the network in response to the connection, if the communication node of the peer's target is not directly connected to the VPN system, secure communication encryption can be performed through commercially available secure connection channels. However, there is still an information security risk because the data port is the corresponding network port of the VPN. The main concern is that in the environment corresponding to the VPN connection port, the original network packets are not converted and encrypted for secure communication, and the data remain in clear text. It is still possible to extract clear text information using basic tools such as Wireshark.  
To effectively address security issues in communication, one can use the application layer system model design. This design involves the sender and receiver performing  
Figure 2: Vulnerabilities in using VPN to encrypt communications.  
encryption and decryption simultaneously in the communication channel environment. Information-hiding operations are established before the data are transmitted, and the data are obtained through software. After the agent identifies the data, they are transferred to the secure encryption operation service. This study utilizes the technical characteristics of blockchain to merge information with the blockchain's channel operation. The software agent synchronizes the block keys of cluster nodes in different regions and establishes the cluster's encrypted channel service. When the IoT sensing device sends and transmits the obtained data, the information is encrypted to complete the data conversion. Unauthorized parties cannot tamper with or distribute the data. Since the key is converted, a secure and dependable information conversion service is constructed.
## 1 Introduction
The scenarios of power allocation were modeled as a game structure with convex pricing. As the MUI cancellation problem has been investigated in detail for wireless CDMA, such schemes have also been introduced in OCDMA networks [28]. Due to the non-negativity of the optical intensity, a modified parallel interference cancellation (PIC) is proposed to suit this inherent property of the optical signals [29]. Compared to the existing balanced detection, these studies yield better performance to some extent, but some bottlenecks still require further investigation--for example, the limited user numbers, the ineffectiveness in performance improvement, and the time delay resulting from the processing of the electrical signals. In this paper, we investigate an intelligently structured receiver for noise suppression, as well as high-speed transmissions. The proposed architecture employs array waveguide grating (AWG), an optical switch, and a balanced detector to build an interference mitigation scheme known as recursive interference cancellation (RIC). There are several stages in RIC, with each of them performing interference estimations and regenerations. At each stage, the signal of an interference user is removed by subtracting the estimated interference term from the received multiplexed signals. Before detecting the data bit of the target user, the signals from interference users are gradually removed through the consecutive stages. PIIN variance is a function of user number and bit-rate and increases with these two variables. Therefore, by reducing the number of interference users, the network is allowed to transmit signals with a higher bit-rate for a given noise level. Compared to SIC and PIC, the proposed receiver scheme has several advantages. First, instead of MUI, we investigate the function of noise suppression, which has been rarely investigated in the existing interference cancelation schemes before. Second, most noise suppression methods in OCDMA are employed for BER reduction, while we investigate the applications to high-speed transmissions. Third, rather than requiring multiple decoders in PIC or SIC, the proposed RIC reduces system complexity, using a single structure to identify all interference users. Finally, as MUI cancellation is performed in the optical domain, RIC has the potential to solve the latency of signal processing in the electrical components. The remainder of this paper is organized as follows. In Section 2, the proposed mechanism of MUI cancellation in a SAC-OCDMA network is described. In Section 3, the hardware implementation of the proposed receiver is investigated, together with software simulations to verify its effectiveness. In Section 4, numerical simulations are conducted to quantify the bit-rate increment achieved by MUI elimination and noise suppression. Section 5 presents a brief conclusion.