esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Future Generation Internet (FGI): Clean Slate to Overcome Internet Weaknesses
### FGI, a Clean-State Revolutionary Approach  
The basic idea of the clean-state approach comes from the question: How would we build the internet today? Internet has been evolving for more than 30 years and one of its major achievements is its long backward compatibility. The IP protocol was standardized in RFC 791 [5] in 1981. From that time, only minor updates have generally been accepted. The internet architecture became stable and resistant to any evolution. Even IPv6 proposed as standard in 1995 has not fully been able to take off until now.  
In this situation, researches oriented into \"out of the box\" research, which allows exploration of radically new ideas and approaches. The whole network architecture along with protocols could be re-designed from scratch. This can lead to new and more efficient approaches in communication technologies. One of the important goals of the clean slate approach is to provide widely accepted testing facilities and to show that new concepts are good enough to be deployed.  
A clean slate approach does not necessarily mean that all existing protocols should be replaced. The achievements can be in new higher-level architectures for service delivery. However, backward compatibility would not be essential in a new clean-state FGI architecture. Several respective approaches exist for future internet architecture design and projects start around globe (in the USA e.g., NSF Future Internet architecture projects [14], in Europe, e.g., Future Internet Assembly [8]). There are several technologies identified as potential technologies involved in future internet designlike machine to machine communication, clouds and virtualization technologies, APIs and cross-layer interfaces, context awareness networking, _etc._
# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results  
### The Experimental Settings  
#### 5.1.1 Datasets  
Most existing works evaluate their methods on DBP15K, which can only be used for the cross-lingual KG alignment task and is not suitable for our research. DomainEA is proposed for aligning entities of different sources to improve the quality of domain knowledge graphs, so we conducted experiments on two heterogeneous datasets, e.g., ZH-MSJ and ZH-ZHYSW, from Chinese recipe websites. Notably, the dataset has been released on GitHub [[https://github.com/Zhonglinjun/DomainEA](https://github.com/Zhonglinjun/DomainEA)]([https://github.com/Zhonglinjun/DomainEA](https://github.com/Zhonglinjun/DomainEA)) (accessed on 31 December 2022) to facilitate access for scholars who may wish to employ it for research purposes. ZH-MSJ consists of 8156 entities, 18 attributes, and 95,145 attribute triples. ZH-ZHYSW includes 6083 entities, 11 attributes, and 46,652 attribute triples, as shown in Table 2. The original knowledge graphs are constructed according to ZH-MSJ and ZH-ZHYSW.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 3 Test Case Modeling
### Test Case PV System Model
\[R_{SH}(G)=R_{SH,ref}\cdot\frac{G_{ref}}{G} \tag{10}\]  
Variation of the ideality factor of the cell and the series resistance with irradiance and cell temperature are neglected. The MPPT controller regulates the duty cycle according to the MPPT algorithm. As the MPPT controller always operates with the average PV voltage and current measured in the steady-state, the dynamics of the DC/DC converter, namely the voltage and current high frequency ripples, can be neglected. Moreover, MPPT controller regulates DC/DC converter input voltage and current regardless of its internal losses. In this context, the boost DC-DC converter is represented by its low-frequency ideal model, consisting of an ideal transformer, whose transformation ratio is:  
\[\frac{V_{out}(t)}{V_{PV}(t)}=\frac{1}{1-d(t)} \tag{11}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electrical Data** & **STC [1]** & **NOCT [2]** \\ \hline Rated Power & \(P_{MPP}\) (\(W\)) & 285 & 208 \\ Rated Voltage & \(V_{MPP}\) (\(V\)) & 31.3 & 28.4 \\ Rated Current & \(I_{MPP}\) (\(A\)) & 9.10 & 7.33 \\ Open-Circuit Voltage & \(V_{OC}\) (\(V\)) & 39.2 & 36.1 \\ Short-Circuit Current & \(I_{SC}\) (\(A\)) & 9.73 & 7.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PV module electrical data. Figure 5: Diagram of the simulated off grid PV system. where \(d(t)\) is the duty cycle, \(V_{out}(t)\) is the output voltage applied to the load resistor, and \(V_{pv}(t)\) is the input voltage applied to the PV module. According to the MPPT test facility available in the SolarTechLab, the resistance of the load resistor, \(R_{load}\), is 37.5 \(\Omega\). At the PV module terminals, the load and the DC-DC converter can be replaced by an equivalent time-varying resistor, \(R_{eq}(t)\). \[R_{eq}(t)=\left(1-d(t)\right)^{2}\cdot R_{load} \tag{12}\]  
The solution of the electrical model is the intersection between the I-V curve representing the actual electrical behavior of the PV module and the load line representing the resistor.
# A Joint Training Model for Face Sketch Synthesis
## 3 Joint Training Model for Face Sketch Synthesis
### Face Sketch Synthesis  
Assuming there are M pairs of joint training photos and sketches that are geometrically aligned, they are divided into patches of fixed size (\(p\times p\times 2\)). Each joint patch is reshaped to a \(2q\)-dimensional column vector. For each joint test patch location, we extend the search region around the patch with \(c\) pixels. Thus, there are \((2c+1)^{2}\) patches in the search region for one patch location, and there are \((2c+1)^{2}M\) joint training photo/sketch patch-pairs. Among these patch-pairs, \(K\) joint training photo patches \(\mathbf{U}^{(i,j)}\in\mathbb{R}^{2p^{2}\times K}\) and joint training sketch patches \(\mathbf{V}^{(i,j)}\in\mathbb{R}^{2p^{2}\times K}\) are randomly and simultaneously selected.  
Figure 1: Illustration of the proposed joint model for face sketch synthesis.  
For each joint test patch \(\mathbf{t}_{1}^{(i,j)}\), the reconstruction weight is calculated as follow:  
\[\underset{\mathbf{w}^{(i,j)}}{\text{min}}\|\mathbf{t}_{1}^{(i,j)}-\mathbf{U}^{( i,j)}\mathbf{w}^{(i,j)}\|_{2}^{2}+\lambda\|\mathbf{d}_{1}^{(i,j)}\odot\mathbf{w}^{( i,j)}\|+\mu\|\mathbf{d}_{2}^{(i,j)}\odot\mathbf{w}^{(i,j)}\|,\ \ s.t.\,1^{T}\mathbf{w}^{(i,j)}=1, \tag{7}\]  
where \(\mathbf{w}^{(i,j)}\in\mathbb{R}^{K\times 1}\) is the weight coefficient for the joint test patch \(\mathbf{t}_{1}^{(i,j)}\). \(\mathbf{d}_{1}^{(i,j)}\in\mathbb{R}^{K\times 1}\) is the Euclidean distance vector between the joint test patch \(\mathbf{t}_{1}^{(i,j)}\) and sampled joint training photo patches \(\mathbf{U}^{(i,j)}\), and \(\mathbf{d}_{2}^{(i,j)}\in\mathbb{R}^{K\times 1}\) is the Euclidean distance vector between the LoG test patch image \(\mathbf{h}^{(i,j)}\) and \(K\) sampled LoG training sketch patches \(\mathbf{Z}^{(i,j)}\).  
Equation (7) has the closed-form solution:  
\[\mathbf{a}\mathbf{w}^{(i,j)}=(\mathbf{C}^{i,j}+\lambda\text{diag}(\mathbf{d}_ {1}^{(i,j)})+\mu\text{diag}(\mathbf{d}_{2}^{(i,j)}))\backslash 1, \tag{8}\]  
\[\mathbf{w}^{(i,j)}=\mathbf{w}^{(i,j)}/1^{T}\mathbf{w}^{(i,j)}, \tag{9}\]  
where \(\mathbf{1}\) is a column vector in which all elements are 1. \(\mathbf{C}^{i,j}=(\mathbf{U}^{(i,j)}-1\mathbf{t}_{1}^{(i,j)^{T}})(\mathbf{U}^{ (i,j)}-1\mathbf{t}_{1}^{(i,j)^{T}})^{T}\) denotes the data covariance matrix, and \(\text{diag}(\cdot)\) extends the vector into a diagonal matrix.  
The target joint sketch patch \(\mathbf{s}^{(i,j)}\) can be synthesized by linearly combining the sampled joint training sketches with the reconstruction weight coefficients \(\mathbf{w}^{(i,j)}\).  
\[\mathbf{s}^{(i,j)}=\mathbf{V}^{(i,j)}\mathbf{w}^{(i,j)}. \tag{10}\]  
The obtained joint sketch patch is a \(2q\)-dimensional vector. Thus, we extract the first half and reshape it to a \(p\times p\) patch, which is the target sketch patch. After obtaining all target sketch patches, the final target sketch can be achieved with an averaged overlapping area.
## 5 Conclusions  
In summary, a measurement method for waveplate parameters based on differential frequency modulation with dual PEMs is presented. The principle of the method is established. A measurement system is built, and experiments are carried out. The measurement results reveal that the standard deviations of the fast-axis azimuth and retardance are 0.02\({}^{\circ}\) and 0.03 nm, respectively, and the maximum relative deviations of fast-axis azimuth and retardance is 0.6% and 0.06%, respectively. The two parameters of fast-axis azimuth and retardance are simultaneously measured, and the single-point data measurement time is only 200 ms. The proposed method cooperates with a two-dimensional motorized scanning  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Standard Deviation** & **Standard Deviation of Fast-Axis** & **Standard Deviation of** & **Simultaneous Measurements** & **Measurement** & **Surface Distribution** & \multirow{2}{*}{**Ref.**} \\  & **Azimuth \(\sigma_{\theta}\)(\(\cdot\))** & **Retardance \(\sigma_{\theta}\)(nm)** & **(Yes/No)** & **Time** & **Measurement.** & **(Yes/No)** & \\ \hline \hline \multicolumn{6}{l}{Senarmort compensation method} & 0.1\({}^{\circ}\) & \(\pm\)5 nm & No & 1 min & No & [7] \\ \multicolumn{6}{l}{Interferometry Laser feedback} & 0.22\({}^{\circ}\) & 0.37\({}^{\circ}\) (0.65 [EMAIL_ADDRESS] nm) & Yes & 1 s & No & [8] \\ \multicolumn{6}{l}{Single photoelastic modulation Differential frequency modulation with dual Photodastic modulation} & 0.2\({}^{\circ}\) & 0.03 nm & Yes & 200 ms & Yes & Present work \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with reported waveplate measurement methods.  
stage, which can realize the parameter measurements in the entire waveplate light transmission range, and the maximum area can be 150 \(\times\) 150 mm in this system. The proposed method demonstrates high-precision, high-speed, highly stable and repeatable, and highly sensitive waveplate parameter measurements.  
Methodology, K.L. and S.W.; software, K.L.; formal analysis, K.L. and Z.W.; validation, K.L.; investigation, K.L. and Z.W.; resources, Z.W.; data curation, K.L.; writing--original draft preparation, K.L. and S.W.; writing--review and editing, K.L.; supervision, S.W. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62205309); the National Natural Science Foundation of China (62205310).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
**(b)** Simulated filter transmission spectrum for a 6.5–6 mirror pair Bragg cavity with \(n\)(SiO\({}_{y}\) = 1.447) and \(n\)(silicon-rich SiN\({}_{x}\)) = 2.389 with an air gap of 1 \(\upmu\)m. The wavelength of the transmission notch can be tuned by the gap size \(L_{\mathrm{cnr}}\). High quality films are frequently deposited with plasma-enhanced chemical vapour deposition (PECVD) that allows to tune the chemical composition as well as the stress of thin films. Low temperature PECVD is essential for deposition of dielectric materials with high thickness precision over temperature-sensitive materials. PECVD deposited SiN\({}_{\mathrm{x}}\) had been studied in terms of refractive index showing results in the telecommunication range from 2 to 2.6 at a chamber temperature of 80 \({}^{\circ}\)C and even up to 3.08 for higher temperatures [2; 3; 4]. Thin films of this material have also been characterised in terms of stress gradient, from compressive to tensile values and their Young modulus (\(E\)) through various techniques [13; 14; 15]. Both parameters are essential for designing the MEMS-tuned cavities where the material stress in the MEMS Bragg reflector determines its radius of curvature and the optical parameters its reflectivity and absorptive loss. For cavities with high quality factors as well as for implementation as tuning elements in VCSELs [16] that feature much less gain per round trip as in-plane DFB or DBR lasers, the loss tangent of the material must be as low as possible. Besides application in lasers, tunable MEMS find their applications in spectroscopy, optical communication or optical imaging [17; 18; 19], amongst others. The MEMS design developed in this study and the desired characteristics are meant as tunable transmission notch filter within a large gain bandwidth laser's cavity for their application in swept source optical coherence tomography (SS-OCT). For this reason, our goal is to design a device with low MEMS mass for fast tuning, wide block band and small cavity length for large free spectral range and low absorption in the 1550 nm range as these parameters will determine the characteristics of the tomographic image [20]. In this study, we first investigate the refractive index, extinction coefficient, the stress gradient, and roughness of single silicon-rich SiN\({}_{\mathrm{x}}\) film applying different recipes. Then, selecting the most suitable recipe for our application we deposit and characterise a silicon-rich SiN\({}_{\mathrm{x}}\)/SiO\({}_{\mathrm{y}}\) DBR. As final step, we develop a movable membrane with different architectures, selecting one of the designs, we studied its electro-thermal tuning behaviour and through simulations we estimate the Young's Modulus of silicon-rich SiN\({}_{\mathrm{x}}\) of that specific recipe.
## 1 Introduction
However, we do not know for sure what is the state of it and to what extent it can work with TCP and UDP applications. Therefore, to obtain a complete picture of the state of the art of DTN for terrestrial applications, especially related to TCP and UDP, we performed an SLR. This SLR aims to offer researchers an overview of this technology and whether it can evolve shortly for terrestrial applications or if, on the other hand, it is meant to die. We have defined three research questions that we believe can answer this interest and offer enough details to obtain a complete panorama of DTN about terrestrial applications. We want to know if DTN can be considered a feasible support for TCP and UDP applications in rural areas where the telecommunication infrastructure is considered extreme, as it is not always available. This research work has the intention of collecting, classifying, organizing, and studying works related to the DTN implementations of terrestrial and TCP/IP applications through an SLR. For that, we have defined the following three research questions. _RQ1: What are the trends in publications on terrestrial DTN?_  
_RQ2: What are the main issues studied in the literature regarding implementing DTN in terrestrial applications?_  
_RQ3: What are the trends in terrestrial applications of DTN?_
## 2 Calculation Methods  
### The Seismic Fragility Analysis Method  
Structural seismic fragility is the probability that the structural damage will exceed a specified value when the ground motion intensity reaches a certain value [27; 28; 29]. The seismic fragility of bridges can be expressed as:  
\[F_{R}(a)=P[EDP\geq LS|IM=a]=\int_{V}^{\infty}f_{R|IM}[r|a]dr \tag{1}\]  
where, \(F_{R}(a)\) is seismic fragility, \(P\) is the probability of the structure failure exceeding a specified value, \(EDP\) is the engineering demand parameter, \(LS\) is the state limit of structure, \(IM\) is the intensity coefficient of ground motion; \(f_{R\,IM}\left[r\,a\right]\) means that, when the ground motion intensity is \(IM\), the structure (component) of a certain engineering demand parameter (\(EDP\)) has reached or exceeded the conditional probability density of a specified damage state.  
Assuming that the relationship between \(EDP\) and \(IM\) follows the log normal distribution, the mean value of the structure (or component) is:  
\[E\overset{\Lambda}{D}P=b(IM)^{c} \tag{2}\]  
where, \(c\) and \(b\) are correlation coefficients.  
The seismic fragility of the structural limit state can be calculated. The smooth \"seismic fragility curve\" is obtained by statistical curve fitting:  
\[P=[EDP\geq LS|IM]=1-\Phi(\frac{\ln(LS_{m}-\ln(bIM^{\text{C}})}{\sqrt{\beta_{LS }^{2}+\beta_{EDP}^{2}}}) \tag{3}\]  
where, \(\Phi\) is the cumulative density function of standard normal distribution, \(LS_{m}\) is log normal distribution under structural damage states, \(\beta_{LS}\) is log standard deviation of structural capacity, \(\beta_{EDP}\) is log standard deviation of structural requirements.
## 3 Materials and Methods
### Design of Experiments
The quality of the fitted model can be evaluated by applying the analysis of variance (ANOVA), which is used to compare the variations due to the treatment with the variations due to random errors inherent to the measurements of the generated responses. Variations that occur in the response can be attributed to the model and are not due to random errors if the F-probability is less than 0.05 (for 95% confidence level). The relevant variables and interactions can be identified by the Student's \(t\)-test. If \(t\)-probability is smaller than 0.05, the parameter or interaction is considered significant. When several responses need to be evaluated (in this case, the main goal was to maximize the response areas for all compounds), a desirability function for each individual response is created, transforming each response into a dimensionless individual desirability (\(d_{i}\)) scale, that ranges from \(d_{i}\) = 0 for inacceptable value to \(d_{i}\) = 1 for the single response maximum. The response is considered unacceptable if it is outside the desirable limit [20] (Equation (3)):  
\[d_{i}^{max}=\left\{ \begin{array}{cc}0&if\ f(x)<A\\ \left(\frac{f(x)-A}{B-A}\right)^{w}&if\ A\leq f(x)\leq B\\ 1&if\ f(x)>B\end{array} \right. \tag{3}\]where \(d_{i}\) is the individual desirability values, \(f(x)\) the response value, \(A\) is the minimum response value, \(B\) the maximum response value, and \(w\) is the weight used to determine the importance (in this work, equal weights were chosen for all responses). It is then possible to calculate the overall desirability (\(D\)), also ranging from 0 to 1 [21] (Equation (4)):  
\[D=\sqrt[n]{d_{1}d_{2}\ldots d_{m}} \tag{4}\]  
where \(m\) is number of responses studied in the optimization process. The optimized conditions indicate the values of input variables for which the overall desirability is maximal. In order to generate the experimental matrix for the design of experiments, to evaluate the effects of several parameters and to perform the data analysis, the JMP 14.3 (SAS Institute Inc., Cary, NC, USA) statistical software was used. However, this could be substituted by other similar statistical software.
# Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations
## 8 Conclusions  
In this paper, we reexamined the structure of GRBFNN in order to make it tractable for problems with high-dimensional input. By using the separable property of Gaussian radial-basis functions, we proposed a new feedforward network called separable-Gaussian-neural-network (SGNN). Different from the traditional MLPs, SGNN splits the input data into multiple columns by dimensions and feeds them into the corresponding layers in sequence. As opposed to GRBFNN, SGNN significantly reduces the number of neurons, trainable variables, and the computational load of forward and backward propagation, leading to the exponential improvement of training efficiency. SGNN can also preserve the dominant subspace of the Hessian matrix of GRBFNN in gradient descent and, therefore, offer comparable minimal loss. Extensive numerical experiments have been carried out, demonstrating that SGNN has superior computational performance over GRBFNN while maintaining a similar level of accuracy. In addition, SGNN is superior to MLPs with ReLU and Sigmoid units when approximating complex functions. However, whether SGNNs can form a dense set of GRBFNN is unclear and is up to further study. Further investigation could also focus on the universal approximability of SGNN and its applications to physics-informed neural networks (PINNs) and reinforcement learning.  
S.X.: Conceptualization, methodology, investigation, writing--original draft preparation. J.-Q.S.: subspace gradient descent, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
Siyuan Xing was funded by Keysight Technologies, Inc. (grant number 47118) and the Donald E. Bently center for Engineering Innovation. Jianqiao Sun was also partially supported by a National Natural Science Foundation of China grant 11972070.  
Figure 7: Approximation of the four-dimensional \(f_{5}\) using SGNN and ReLU-NNs. The plots are generated by projecting the surface to the \(x_{1}-x_{2}\) plane with other coordinates all zero. (**a**) Ground truth; (**b**) SGNN; (**c–f**) ReLu-NNs with different network configurations. The layers and neurons per layer of the NNs are listed in Table 8.
###### Abstract
In typical MEMS accelerometers, there are at least two other sources of noise, which are the mechanical vibrations of the microstructure and interference introduced by the output circuits of the amplifier and low-pass filter. The causes of noise in MEMS gyroscopes are analogous [36; 37]. Stochastic processes caused by these phenomena, among others, are discussed in detail in the following sections. Figure 3: The idea of measuring acceleration using a MEMS capacitive accelerometer, based on [38]. To obtain high precision of measurements, accelerometers and gyroscopes should be calibrated. Calibration errors resulting from inaccuracies in the manufacturing and assembly process of the device are minimized and temperature compensation is performed. A simplified block diagram of a three-axis accelerometer integrated with a gyroscope and magnetometer is shown in Figure 4.  
This paper will analyze in detail the measurement properties of 3D accelerometer and 3D gyroscope assemblies produced using MEMS technology. The main purpose of the presented analysis is to demonstrate the validity of the use of selected IMUs (inertial measurement units) on board small aircraft and UAVs (unmanned aerial vehicles). In miniature UAVs, high sampling frequencies of angular rates and accelerations are desired. This is important for attitude and heading calculation in AHRS, but such high sampling frequencies are especially important in flight recorders used for research purposes. The registered data can, for example, be used for the identification of aircraft characteristic frequencies. The stochastic analysis of MEMS inertial sensors in advanced IMUs in this article presents reliable information about the IMU properties not only for this particular system but can also be helpful in the synthesis of complementary filter parameters used in different AHRSs. The comparative analysis in this paper was performed with the use of three methods for all six sensors; temperature change was also considered. The presented approach to IMU research, which is an element of a miniature flight parameter recording system, has an original character, intended to lead to non-obvious subsequent research tasks.