esa-sceva/satcom-chunk-collection huggingface dataset:
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 4 Experimental Results  
This section presents the experiment and simulation results. A high-precision ring laser gyro inertial navigation system is used to conduct flight experiments. The ring laser gyro bias stability is less than 0.01\({}^{\circ}\)/h. The accelerometer bias stability is less than 20 ug. The GNSS positioning error is less than 10 m, which is used as the position reference. The update frequency of the gyro and accelerometer is 200 Hz, while the update frequency of GNSS is 1 Hz.
# On the Use of Data Envelopment Analysis for Multi-Criteria Decision Analysis
## 3 Results
### Using the Waste Management Data  
The estimated efficiency scores (i.e., cost-effectiveness measures) and their relative rankings are given in Table 6. Both the radial and slacks-based approach estimated that more alternatives were efficient when all data were compared than when the hierarchical structure of the information was taken into consideration. For example, the number of efficient (i.e., TE = 1) alternatives using all data was found to be 12 and 11 for the radial and slacks-based model respectively, decreasing to 5 and 4 alternatives when the hierarchical structure was imposed.  
The relative rankings also changed substantially when the hierarchical structure was imposed. For example, alternative 3 was found to be efficient (TE = 1; rank = 1) when all the data were used, although it fell to 18 th and 20 th place, respectively, when the hierarchical structure was imposed (Table 6).  
The efficiency scores also differed substantially for several of the alternatives when estimated using the different models. For example, the efficiency scores for alternative 2 were substantially lower when estimated using the slacks-based model than the radial model, although the impact of this on their rank was less pronounced.  
The correlations between the ranks from the different measures are presented in Table 7. [PERSON] and [PERSON] (2009) provided the relative rankings of each of the option using the ELECTRE III method, and these are also included in the correlation matrix in Table 7. The correlation between ELECTRE III and the ranks derived using DEA were  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alternative**} & \multicolumn{4}{c}{**Efficiency Scores**} & \multicolumn{4}{c}{**Rank of Efficiency Scores**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Radial**} & \multicolumn{2}{c}{**Slacks-Based**} & \multicolumn{2}{c}{**Radial**} & \multicolumn{2}{c}{**Slacks-Based**} \\ \cline{2-9}  & **All** & **Hierarchy** & **All** & **Hierarchy** & **All** & **Hierarchy** & **All** & **Hierarchy** \\ \hline
1 & 0.984 & 0.934 & 0.268 & 0.242 & 16 & 9 & 20 & 19 \\
2 & 0.931 & 0.797 & 0.025 & 0.023 & 20 & 18 & 21 & 21 \\
3 & 1.  
generally low, but were higher for the slacks-based model in general and for the hierarchical slacks-based model in particular. The ELECTRE III method incorporates preference weights for the different criteria, although unfortunately these weights were not presented by the authors [40].
## 2 Coupled Thermo-Mechanical Phase-Field Modeling
### Damage  
In the absence of body force, the linear momentum balance equation can be expressed as follows.  
\[\
abla\cdot\boldsymbol{\sigma}=0 \tag{9}\]where \(\
abla\cdot\) denotes the divergence operator. The total strain \(\varepsilon\) can be divided into the elastic strain \(\varepsilon_{e}\) and the thermal strain \(\varepsilon_{t}\).  
\[\varepsilon=\varepsilon_{e}+\varepsilon_{t}=\
abla_{s}u:=\frac{1}{2}\Big{(} \
abla u+\
abla^{T}u\Big{)} \tag{10}\]  
The thermal strain \(\varepsilon_{t}\) should satisfy the linear expansion law.  
\[\varepsilon_{t}=\alpha_{t}(T-T_{0})I \tag{11}\]  
where \(\alpha_{t}\) and \(T_{0}\) represent the coefficient of thermal expansion and initial temperature, respectively. The total elastic free energy density of the undamaged solid is shown below.  
\[\psi_{e}=\frac{1}{2}\varepsilon_{e}:\mathbb{C}^{e}:\varepsilon_{e}=\frac{ \lambda}{2}(\text{tr}\varepsilon_{e})^{2}+\mu\text{tr}(\varepsilon_{e}^{2}) \tag{12}\]  
The elastic strain energy density additively decomposes into a positive (tensile) part \(\psi_{e}^{+}\) and a negative (compressive) part \(\psi_{e}^{-}\) when only the tensile part is affected by the degradation function:  
\[\psi_{e}=\psi_{e}^{-}+f(d)\psi_{e}^{+} \tag{13}\]  
where \(f(d)\) represents the energy degeneracy function with the following conditions.  
\[f(d)\in[0,1],f(0)=1,f(1)=0;f^{\prime}(d)<0,f^{\prime}(1)=0 \tag{14}\]  
Only the tensile part contributes to the fracture in this method. Thus, the stress tensor can be further written as follows.  
\[\mathbf{\sigma}=\frac{\partial\psi_{e}}{\partial\varepsilon}=f(d)\frac{\partial \psi_{e}^{+}}{\partial\varepsilon}+\frac{\partial\psi_{e}^{-}}{\partial \varepsilon}=f(d)\mathbf{\sigma}^{+}+\mathbf{\sigma}^{-} \tag{15}\]  
The elastic energy degradation function \(f(d)\) proposed by [PERSON] and [PERSON] [42] is used to make the phase-field model insensitive to crack length scale parameters.  
\[f(d)=\frac{(1-d)^{2}}{(1-d)^{2}+a_{1}d(1+a_{2}d+a_{3}d^{2})} \tag{16}\]  
In the equation, \(a_{1}=\frac{4l_{cb}}{\pi t_{0}}\), \(a_{2}=-\frac{1}{2}\), and \(a_{3}=0\) represent the cohesive nature of the fracture in the process zone. Moreover, \(l_{cb}=\frac{EG_{e}}{\sigma_{h}^{2}}\) represents Irwin's length, which is used to measure the size of the fracture process zone. The behavior of the material becomes increasingly brittle as the length scale decreases. Parameters \(a_{2}\) and \(a_{3}\) are adopted to adjust different softening curves [29].  
We need to apply an irreversible condition, the history strain field \(\mathcal{H}\)[27], to stop the crack from healing:  
\[\left\{\begin{array}{l}\psi_{e}^{+}-\mathcal{H}\leq 0,\ \dot{\mathcal{H}}\geq 0,\ \dot{\mathcal{H}}(\psi_{e}^{+}-\mathcal{H})=0\\ \mathcal{H}=\max\Big{\{}\max_{t\in[0,\tau]}\psi_{e}^{+}(\varepsilon_{e},t), \psi_{th}\Big{\}}\end{array}\right. \tag{17}\]  
where \(\psi_{th}\) is the damage threshold.  
\[\psi_{th}=\frac{\sigma_{u}^{2}}{2E} \tag{18}\]  
In this equation,\(\sigma_{u}\) and \(E\) represent the strength and modulus of elasticity of the material, respectively.
# The Frequency Response Characteristics of Ge-on-Si Photodetectors Under High Incident Power
## 1 Introduction  
Silicon photonics has been applied to optical communication and interconnection, and its applications in optical computing, optical sensing, and lidar have attracted extensive research interest [1, 2, 3]. As germanium photodetectors are the key devices in the receiver, their performance plays a key role in the entire optical transceiver system. High responsivity and bandwidth have been needed for the development of photodetectors [4, 5, 6].  
For applications such as coherent optical communication, lidar, and microwave photonics, photodetectors must possess both high optical signal power and high operating frequency [7, 8]. Currently, there are several papers on germanium photodetectors that exhibit excellent frequency characteristics under high incident light power [9, 10, 11].  
The bandwidth of germanium photodetectors can decrease under high incident light power, which limits their application [12, 13, 14]. The main reason for the decrease in bandwidth is the carrier-shielding effect described in previous papers [15, 16, 17]. To reveal the reasons for the bandwidth decrease under high incident light power, this paper conducted simulations and experiments on the carrier-shielding effect. To the best of our knowledge, this is the first time that the impact of the decrease in carrier saturation drift velocity,which is caused by a high concentration of photogenerated carriers, on the bandwidth of germanium photodetectors has been studied. This research provides theoretical support for the design of germanium photodetectors for high-power applications.
# Teleseismic Tomography for Imaging the Upper Mantle Beneth Northeast China
## 3 Northeast China
### Inversion Resolution and Reliability Evaluation
#### 3.3.2 Checkerboard Test  
The test board test (template/chesboard test) is a classic process used to evaluate the reliability of solutions, as shown in Figure 7. This method can add regular positive and negative errors to the initial model to affect the velocity distribution at the nodes and get the synthetic data model. The ray path and travel time residuals are calculated by adding the source information and station location, and then the initial model is inverted by using travel time residuals. Finally, the rationality of the initial model grid setting can be known by comparing the resulting model with the synthetic data model.  
In the test of the test board, the horizontal grids are all selected with a distribution interval of \(0.8^{\circ}\times 0.8^{\circ}\). The model with a depth of 50 km is tested, and the input models are respectively added with a velocity disturbance of \(\pm 4\%\) and a random error of 0.1%.  
In terms of the effect of the test results as shown in Figure 8, the resolution of this study is relatively high for the upper mantle and mantle transition zone, especially for Changbai Mountain and Songliao basin, the key area of this study, which have better test results at different depths. From the results, the reliability of imaging results is high. Therefore, in this paper we select the \(0.8^{\circ}\times 0.8^{\circ}\times 50\) km grid as parameters, the horizontal resolution is about \(1.6^{\circ}\times 1.6^{\circ}\), and the vertical resolution is about 100 km.  
Figure 7: Initial model with a \(0.8^{\circ}\times 0.8^{\circ}\times 50\) km grid.
# Application of Fly Ash as an Adsorbent for Removal of Air and Water Pollutants
## 2 Global Overview of Fly Ash
### India  
With the development of economy and the increase of the population, the energy consumption in India is also increasing year by year. U.S. Energy Information Administration Department reported that the energy consumption growth in India will far exceed that of China, the United States, and Russia until the end of 2035 [44]. In terms of generating capacity, Indian power plants rely mainly on fossil fuels. In general, 76.4% of the power generation is coal-based power plants, while renewable energy sources only generate 6.62% [44]. Annual production of FA in India is therefore also increasing rapidly. Production of FA during 2010 and 2011 increased to about 130 million tons, which was about 85.7% higher than during 1996 to 1997. The FA utilization rate also increased from 9.63% in 1996 to 1997 to 54.53% in 2010 to 2011, an increase of 466% over a 15 year period [45]. This shows that people are aware of the value of FA utilization with technological advances and increased incomes. Figure 4 shows uses of FA in India: 44.76% for cement, 16.72% for reclamation, 9.1% for mine filling, 6.89% for ash dyke raising, 6.86% for bricks and tiles, 6.51% for road and embankments, 0.74% for concrete, and 7.38% for other uses [15]. The Ministry of Environment, Forests and Climate Change (MoEFCC) expanded the standard of using FA to make it more used in agriculture. Moreover, to improve the utilization of FA and reduce the cost, the Ministry of Railways has also made relevant regulation policies, such as providing FA free of charge to the surrounding users, and establishing the cement industry near coal power plant [46]. [PERSON] et al. [47] and other researchers [48; 49; 50; 51; 52] found that FA of India can improve soil structure and be used to reclaim dumps and degraded soils by making soil more fertile and increasing the yield of agricultural products. Given the advantages of FA and the policies in place in India, it is expected that the utilization rate of FA in India will continue to increase in the near future.  
Figure 4: Utilization of fly ash in India (reproduced with permission from [15], Elsevier, 2018).  
Figure 3: Production and utilization of fly ash from 2001 to 2015 (reproduced with permission from [41], Elsevier, 2018) **(a)**; and comprehensive utilization rate of fly ash in Chinese cities in 2010 (reproduced with permission from [37], Elsevier, 2018) **(b)**.
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Fluid-Injection Scheme
#### 4.4.3 Influence of Injected Flow Rate Average Value  
Under other constant parameters, the impact of varying injected flow rate mean values on the simulation results was examined. The mean values of injected flow rate were set at 2 m\({}^{3}\)/min, 3 m\({}^{3}\)/min, 4 m\({}^{3}\)/min, and 6 m\({}^{3}\)/min. The simulation results from Figure 16 reveal that the downhole pressure increases with the increase in the mean value of the injected flow rate. As the mean value of injected flow rate increases, the velocity of fracturing fluid in the pipe column also increases, resulting in increased pressure losses along the pipeline. Consequently, although the downhole pressure increases with the injected flow rate mean value, the fluctuation in downhole pressure decreases.  
A further analysis was conducted on the average injection flow rate, with the average injection flow rate as the variable, simulating the amplitude of downhole pressure fluctuations at depths of 1000 m and 3000 m, respectively. The results indicate that a higher average flow rate is not always better, but is related to the well depth, and there exists an optimal solution. In Figure 17a,b, taking wells at depths of 1000 m and 3000 m as examples, it is evident that in the 1000 m deep well, with 10 m\({}^{3}\)/min as the threshold, the fluctuation amplitude decreases as the average injection flow rate increases. Therefore, we can conclude that the optimal average injection flow rate for a 1000 m deep well is approximately 10 m\({}^{3}\)/min; similarly, in the 3000 m deep well, this optimal solution occurs earlier, at 3 m\({}^{3}\)/min.
## 2 Materials and Methods
### Fiber Structure Analysis and Generating Mechanical Properties Method
#### 2.4.3 Simulation Process  
Figure 9 illustrates the simulation workflow, spanning from injection molding process simulation to structural simulation. The injection molding simulation was conducted using SIGMASoft, employing a material card for PA66/GF50 (wt%) provided by DatapointLabs (Ithaca, NY, USA).  
The mesh model generated for injection molding analysis cannot typically be directly used for structural simulation due to differences in modeling requirements. Therefore, residual stress results were generated for each element of the mesh model during the injection molding simulation. These results were then mapped onto the corresponding elements of the structural analysis mesh model, which was created using Abaqus. The mapping process was performed using the mapping module provided by SIGMASoft.  
The fiber orientation tensor results were further utilized in the structural simulation to define orthotropic material properties. Specifically, the orientation tensor values were used to assign directional elastic moduli (E\({}_{1}\), E\({}_{2}\), E\({}_{3}\)) for each element in the structural mesh model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Fiber Orientation Ratio** \\ **(Based on the X-Axis)** \\ \end{tabular} } & \multicolumn{3}{c}{**Fiber Orientation Tensor Value**} \\ \cline{2-4}  & **X-Axis** & **Y-Axis** & **Z-Axis** \\ \hline
90\% & 0.9 & 0.05 & 0.05 \\ \hline
80\% & 0.8 & 0.1 & 0.1 \\ \hline
70\% & 0.7 & 0.15 & 0.15 \\ \hline
60\% & 0.6 & 0.2 & 0.2 \\ \hline
50\% & 0.5 & 0.25 & 0.25 \\ \hline
40\% & 0.4 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fiber orientation tensor values for RVE modeling.  
The injection molding analysis results, along with information such as fiber orientation and residual stress, were utilized in structural analysis. In structural analysis, interpretations were conducted for tension specimens at 0 degrees, 45 degrees and 90 degrees with respect to the flow direction, based on flat specimens. Additionally, analyses were performed for the hammering test on the motor mount bracket product.
## 2 Instrumental Data
The 3-channel acceleration record of the 11 September 2016 magnitude M\({}_{\rm L}\)5.3 earthquake at the IZIIS location is shown on Figure 5. The maximum acceleration was observed on the E-W component and had a zero-to-peak value of 0.192 g, on the Z-component the zero-to-peak PGA was 0.123 g, and on the N-S component the zero-to-peak PGA was 0.098 g.  
The differences in the amplitude values of the strong motion records can be primarily attributed to the types of surface and place where the instruments were installed--bedrock  
Figure 3: The acceleration record of the 11 September 2016 magnitude 5.3 earthquake at station SKO. versus the sediments. Shaking can increase or decrease depending on the geometry and structure of the layers, Moreover, the shape of the waveforms might change because of their relative site position in respect to the fault activated during the main earthquake. Figure 4: The acceleration record of the 11 September 2016 magnitude 5.3 earthquake at the FCE station (Faculty of Civil Engineering-Gradezen Fakultet). Figure 5: The acceleration record of the 11 September 2016 magnitude 5.3 earthquake at the IZIIS site. The records of Zagreb March 2020 earthquake from the two closest stations coded QUHS and QARH, were selected for our analysis. Both were equipped with Guralp instruments with a general set of response curves to convert the measurements from counts into units of acceleration, as described in the user manuals. Figure 6 displays about half-minute sections of the recorded Zagreb M\({}_{\rm L}\)5.5 March 2020 earthquake on stations QUHS and QARH. Each plot is automatically scaled and shown as time in seconds vs. acceleration in g. The vertical component Z of the P-waves had the highest accelerations, though the horizontals carried most of the energy in their respective S-waves. That is expected with close earthquakes, given the geology, the source mechanism, and the fault orientation. Figure 6: The 3-component sections of the Zagreb M\({}_{\rm L}\)5.5 March 2020 earthquake records on the two closest stations QUHS (top) and QARH (bottom). The maximum acceleration and the hypocentral distance for the selected five stations are summarized in Table 2.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 2 Signal Model
### Signal Model Based on Array Antennas  
The distance of the GNSS signal to the array antenna is much larger than the array aperture, and the far-field condition is satisfied. The wavefront of a GNSS signal can be approximated as a plane wave. Suppose that one line-of-sight signal component and \(M-1\)  
Figure 1: Multipath reflection model with one antenna.  
Figure 2: The peak of direct signal and multipath signal correlation.  
multipath signal components impinge on a uniform linear array consisting of \(N\) elements with element spacing \(d\). The baseband satellite navigation signal received by the array antenna can be represented as follows [5]:  
\[\mathbf{x}(t)=\sum_{m=1}^{M}\mathbf{a}(\theta_{m})s_{m}(t)+\mathbf{n}(t) \tag{4}\]  
where \(\mathbf{x}(t)=\left[x_{1}(t),x_{2}(t),\cdots,x_{N}(t)\right]^{T}\) stands for the array data vector. The output of the \(n\)th element at time \(t\) is represented by \(x_{n}(t)\), while \(\mathbf{n}(t)=\left[n_{1}(t),n_{2}(t),\cdots,n_{N}(t)\right]^{T}\) denotes the additive white Gaussian noise vector. It is commonly assumed that the observation noise on different array elements and at different time instants is mutually independent. \(s_{m}(t)\) represents the \(m\)th signal component.  
\[s_{m}(t)=A\alpha_{m}c(t-\tau_{m})e^{i2\pi f_{d}t+\dot{\varphi}_{m}} \tag{5}\]  
where \(\mathbf{a}(\theta_{m})\) represents the array guide vector corresponding to the \(m\)th navigation signal, which can be expressed as  
\[\mathbf{a}(\theta_{k})=\left[1,e^{-i2\pi d\sin(\theta_{k})/\lambda},\cdots,e^ {-j2\pi d(N-1)\sin(\theta_{k})/\lambda}\right]^{T} \tag{6}\]  
where \(\theta\) is the signal incident angle, \(\lambda\) is the wavelength, and \(d=\lambda/2\) is the array spacing. A schematic diagram of the uniform linear array is shown in Figure 3.  
The cumulative correlation value vector obtained by correlating the baseband array signals with the local replica signal can be represented as follows:  
\[\begin{array}{l}\mathbf{r}(\varepsilon)=\frac{1}{T_{coh}}\int_{0}^{T_{coh}} \mathbf{x}(t)\cdot s_{L}(t)dt\\ =A\sum_{m=0}^{M}\alpha_{m}\mathbf{a}(\theta_{m})R_{c}(\Delta\tau_{m})e^{i2 \phi_{m}}\end{array} \tag{7}\]  
where \(T_{coh}\) indicates the accumulated time.
# Polarization-Discriminated RSOA-EAM for Colorless Transmitter in WDM-PON
## 3 Simulation Results and Discussion  
The proposed device is simulated with a well-established traveling wave model [36, 37], which has incorporated the propagation of both the signal and the broadband noise, the propagation of the TE and TM modes, and the evolution of the carrier and photon density along the cavity. For more details  
Figure 1: (**a**) Schematic three-dimensional view of the proposed polarization-discriminated (PD) reflective semiconductor optical amplifier electro-absorption modifier (RSOA–EAM), and (**b**) Schematic diagram for the device operation. (\(\theta_{DS}\): polarization angle of the input downstream signal; \(\theta_{US}\): polarization angle of the input upstream signal; \(\theta_{FR}\): the round-trip Faraday rotation angle; HR: high reflective; AR: antireflective; DS: downstream; US: upstream).  
of the model and its numerical implementation, please see the Supplementary Material. With the material and structural parameters listed in Table 1, the separated SOA and EAM sections are first characterized by the simulation model, respectively. The gain and the saturation input power [38] of the separated SOA section at different polarizations are plotted in Supplementary Figure S2a, and the EAM single-pass transmission curve is plotted in Supplementary Figure S2b. As shown in Supplementary Figure S2a, the gain discrimination between the TE and TM modes is around 40 dB for a bias current of 100 mA, and is enhanced with an increased SOA bias. The saturation input power of the SOA is raised with an increased polarization angle from 0\({}^{\circ}\) to 90\({}^{\circ}\). Since the linear and nonlinear response regimes depend on the saturation input power [38], and considering a nonlinear response is required to erase the downstream signal while a linear response is required by the upstream signal, the down- and upstream polarizations should therefore be aligned at 0\({}^{\circ}\) (maximize the nonlinear regime) and close to 90\({}^{\circ}\) (maximize the linear regime), respectively. The single-pass insertion loss in the EAM section is about 1.5 dB and doubles for a round-trip. The static single/double-pass extinction ratio is around 10/20 dB with an EAM bias voltage of -1 V plus a peak-peak voltage (\(V_{pp}\)) of 2 V (see Supplementary Figure S2b).