esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Developed HSCN for Estimating Channel  
A new channel estimation model is implemented for the MIMO system by using a cascaded deep network with a hybrid optimization algorithm. Various traditional methods are used for estimating the channel coefficient, but they produce large computation complexity and are highly time-consuming. So, a serial cascaded multi-scale autoencoder with attention to LSTM is used to eliminate the BER and reduce the time needed for the computation process of channel coefficients. The main drawback of using an autoencoder system is the overfitting problem and the need for additional training. Due to this, the MIMO system becomes overloaded, and errors are prone to occur on the receiver side. The system complexity and the overfitting problem are reduced using the hybrid serial cascaded multi-scale network. Here, the autoencoder is constructed with three components--encoder, code, and decoder--and then trained to select the input automatically. The selected input is applied to the encoder. The encoder compresses the input data using code and then again reconstructs the input data using a decoder and stores them in the memory of the receiver antenna. LSTM is used to focus on a particular feature at a time by avoiding the remaining features. The LSTM is used because of its ability to minimize the vanishing gradient problem. Here, the HSCN network is constructed to identify the channel in MIMO, where the autoencoder and LSTM are serially integrated to build a HSCN. The hidden neuron count and epochs from the autoencoder and LSTM network are optimally tuned to increase the channel assessment performance. The efficiency of the system is improved by optimizing the variables using the proposed RP-WHEVO algorithm by considering maximum iteration as 50, population counts as 10, and length of chromosome as 4. Thus, the RP-WHEVO-HSCN system effectively reduces the MSE, RMSE, and BER and generates high performance in channel estimation tasks. Channel estimation using the HSCN system is diagrammatically illustrated in Figure 2.  
Figure 2: Diagrammatical illustration of channel estimation using HSCN.  
The value of the matrix \(Y\) is derived by using the variables \(\Theta^{y}(w)\) and \(V^{w}\), from the above equations. The matrix \(Y\) is determined using Equation (23).  
\[Y=\begin{bmatrix}I_{diag}^{1}(0)\widetilde{H}(0)&\cdots&f_{diag}^{dy}(0) \widetilde{H}(0)\\ \vdots&\cdots&\vdots\\ f_{diag}^{y}(k_{1}-1)\widetilde{H}(k-1)&\cdots&f_{diag}^{dy}(k_{1}-1)\widetilde{ H}(k_{1}-1)\end{bmatrix} \tag{23}\]  
The diagonal matrix has a non-numeric value and it is denoted by \(I_{diag}^{1}\).
Generation of an Adjustable Optical Cage through Focusing an Aperture Bessel-Gaussian Correlated Schell-Model Beam  
[PERSON]  
1 School of Optoelectronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China; [EMAIL_ADDRESS] (L.G.); [EMAIL_ADDRESS] (L.C.)1 School of Optoelectronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China; [EMAIL_ADDRESS] (L.G.); [EMAIL_ADDRESS] (L.C.)1 School of Optoelectronic Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China; [EMAIL_ADDRESS] School of Physics and Material Science, Anhui University, Hefei 230039, China; [EMAIL_ADDRESS] School of Physical Science and Technology  
applications, such as beam shaping, microscopy imaging, optical trapping, and free-space optical communications [35; 36; 37; 38]. As two typical partially coherent beams with NCFs, the Laguerre-Gaussian correlated Schell-model (LGCSM) beam and the Bessel-Gaussian correlated Schell-model (BGCSM) beam were introduced in theory [27], and later generated in experiment with the aid of a spatial light modulator or with a hologram and a diffuser [21; 39; 40]. Their correlation functions are expressed in the form of Laguerre-Gaussian and Bessel-Gaussian functions, respectively. Both LGCSM and BGCSM beams display ring-shaped beam profiles in the far field in free space. It was also demonstrated that BGCSM and LGCSM beams have advantages over a partially coherent beam with a conventional correlation function (i.e., Gaussian Schell-model beam) to reduce turbulence-induced degeneration upon propagation in turbulent atmosphere [41; 42], which will be beneficial in free-space optical communications. Furthermore, [PERSON] et al. demonstrated both theoretically and experimentally that by focusing an LGCSM beam with a thin lens and tailoring the source correlation function, a controllable partially coherent optical cage can be generated near the focal area [43]. Thus, one may ask: can an adjustable optical cage be generated from focusing a BGCSM beam? In this paper, we demonstrate that a partially coherent optical cage can be generated by focusing an apertured BGCSM beam. We also show that the size and depth of the generated optical cage is adjustable by modulating the source spatial correlation function, the coherence width, and the aperture radius. The effective beam width and spectral degree of coherence (SDOC) of the focused BGCSM beam in the focal region are also discussed. Our results may find uses in optical trapping and material thermal processing.
# Off-Resonance Control and All-Optical Switching: Expanded Dimensions in Nonlinear Optics
## 1 Introduction
It is important to note that these differ from standard nonlinear optics, in that the laser-modified processes involve a resonant mode along with the off-resonant beam. Section 5 delivers an overview of optically controlled resonance energy transfer, which may be interpreted as optical catalysis of energy transfer. It is shown that this effect has a fundamental connection to the well-established phenomenon of optical binding, which also involves an off-resonant input beam. The penultimate Section introduces the potential for all-optical switching based on each mechanism and entertains the possibility of an optical transistor in a related scheme. The concluding section provides a discussion of the context for deploying these and other schemes for all-optical switching.
###### Abstract  
Nowadays, the use of freeform surfaces in various functional applications has become more widespread. Multi-sensor coordinate measuring machines (CMMs) are becoming popular and are produced by many CMM manufacturers since their measurement ability can be significantly improved with the help of different kinds of sensors. Moreover, the measurement accuracy after data fusion for multiple sensors can be improved. However, the improvement is affected by many issues in practice, especially when the measurement results have bias and there exists uncertainty regarding the data modelling method. This paper proposes a generic data modelling and data fusion method for the measurement of freeform surfaces using multi-sensor CMMs and attempts to study the factors which affect the fusion result. Based on the data modelling method for the original measurement datasets and the statistical Bayesian inference data fusion method, this paper presents a Gaussian process data modelling and maximum likelihood data fusion method for supporting multi-sensor CMM measurement of freeform surfaces. The datasets from different sensors are firstly modelled with the Gaussian process to obtain the mean surfaces and covariance surfaces, which represent the underlying surfaces and associated measurement uncertainties. Hence, the mean surfaces and the covariance surfaces are fused together with the maximum likelihood principle so as to obtain the statistically best estimated underlying surface and associated measurement uncertainty. With this fusion method, the overall measurement uncertainty after fusion is smaller than each of the single-sensor measurements. The capability of the proposed method is demonstrated through a series of simulations and real measurements of freeform surfaces on a multi-sensor CMM. The accuracy of the Gaussian process data modelling and the influence of the form error and measurement noise are also discussed and demonstrated in a series of experiments. The limitations and some special cases are also discussed, which should be carefully considered in practice.  
C 2016 6 409; doi:10.3390/appf6120409
## 3 Beamforming Optimization with AN
### Beamforming Optimization with AN in the MISO SWIPT CR-NOMA Network
To define the computational complexity to solve the (12)-SDR problem, we define \(\operatorname{T}=L+M+1\) as the number of matrix variables of size \(N\times N\), and \(\operatorname{Y}=3L+M+K+1\) is the linear constraint. Then, we can denote the computational complexity to solve problem (12)-SDR as \(\mathcal{O}\left(\sqrt{\operatorname{TY}}\left(\operatorname{T}^{3}\! \operatorname{Y}\!^{6}+\operatorname{T}^{2}\!\operatorname{Y}\!^{2}\right)\log \binom{1}{\!/\!\chi}\right)\) with a solution accuracy of \(\chi>0\). The solution to problem (12)-SDR is denoted as \(\left\{\mathbf{V}_{l}^{*},\mathbf{W}_{m}^{*},\theta_{l}^{*}\right\},\mathbf{F}^ {*}\), which is unlikely to satisfy the following constraints: \(\operatorname{rank}\left(\mathbf{V}_{l}\right)=1,\operatorname{rank}\left( \mathbf{W}_{m}\right)=1,\operatorname{rank}\left(\mathbf{F}\right)=1,\, \forall l,\,\forall m\). Therefore, we propose a Gaussian randomization technique to obtain the close-to-optimal solutions for problem (11). In the Gaussian randomization technique [13; 14; 39], the first step is to perform eigen decomposition of the precoder matrices obtained from problem (12)-SDR, i.e., we evaluate \(\mathbf{V}_{l}=\mathbf{Q}_{\text{SU},l}\Lambda_{\text{SU},l}\mathbf{Q}_{\text{ SU},l}^{H}\), \(\mathbf{W}_{m}=\mathbf{Q}_{\text{EH},m}\mathbf{\Lambda}_{\text{EH},m}\mathbf{Q}_{ \text{EH},m}^{H}\), and \(\mathbf{F}=\mathbf{Q}_{f}\Lambda_{\text{f}}\mathbf{Q}_{f}^{H}\). Next, we generate candidate solutions based on vectors \(\kappa_{\text{SU},l}\), \(\kappa_{\text{EH},m}\), and \(\kappa_{f}\), where the elements follows a complex, circularly symmetric Gaussian distribution, \(\mathcal{CN}\left(0,1\right)\), i.e., \(\mathbf{v}_{l}=\mathbf{Q}_{\text{SU},l}\Lambda_{\text{SU},l}^{1/2}\kappa_{ \text{SU},l}\), \(\mathbf{w}_{m}=\mathbf{Q}_{\text{EH},m}\Lambda_{\text{EH},m}^{1/2}\kappa_{ \text{EH},m}\) and \(\mathbf{f}=\mathbf{Q}_{f}\Lambda_{f}^{1/2}\kappa_{f}\). However, there exists the possibility that the obtained candidate precoder vectors are not feasible for problem (11). Then, we can adjust the value of the candidate precoded vectors based on scalar factors, \(\left\{\tau_{\text{SU},l},\tau_{\text{EH},m}\right\}\tau_{f}\), which can be obtained through the solution to the following optimization problem:\[\frac{\theta_{l}\big{|}\textbf{g}_{l}^{H}\textbf{v}_{l}\big{|}^{ 2}}{\theta_{l}\left(\sum_{l=1}^{i-1}\big{|}\textbf{g}_{l}^{H}\textbf{v}_{l^{ \prime}}\big{|}^{2}+\sum_{m=1}^{M}\big{|}\textbf{g}_{l}^{H}\textbf{w}_{m} \big{|}^{2}+\big{|}\textbf{g}_{l}^{H}\textbf{f}\big{|}^{2}+\sigma_{l}^{2} \right)+\sigma_{cn,l}^{2}}\geq\gamma_{i},\,\forall l,\,i\in\mathbb{Q},\,l\leq i \tag{11b}\]9 (13a) s.t.
## 4 Results and Discussion  
### RQ1 and RQ2: Quality of the Translations  
The descriptive results of the models trained using the word embeddings are presented in Figure 1 and in Table 10.  
As observed, all translators provide translations that meet a sufficiently high quality threshold (\(\geq\)0.8), with some even surpassing the score achieved by the reference translation from INCIBE. Therefore, it can be concluded that no translator should be dismissed a priori. Additionally, this finding affirms that the reference translation itself demonstrates a sufficiently high level of quality. However, a recurring trend emerges, indicating that the pretrained models consistently yield superior results, whereas those utilizing the corpus tend to perform less favorably. This prompts a reconsideration of the corpus quality used in the translation process.  
Figure 2 illustrates the total number of victories across the three word embedding models for each translator and the INCIBE translations. As shown, Bing achieves a significantly lower number of victories compared to the other systems. Conversely, the translations from INCIBE, ChatGPT, and Copilot attain a similar and relatively higher number of victories, suggesting that these systems exhibit greater stability in translation quality.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Value** & **INCIBE** & **ChatGPT** & **Bing** & **DeepL** & **Goog.Trans.** & **Copilot** \\ \hline \multirow{3}{*}{DB (INCIBE)} & Maximum & 0.9748 & 0.9748 & 0.9752 & 0.9739 & 0.9753 & 0.9748 \\  & Minimum & 0.7315 & 0.7125 & 0.6992 & 0.7231 & 0.7010 & 0.6957 \\  & Victories & 1058 & 1497 & 480 & 1528 & 1208 & 1743 \\ \hline \multirow{3}{*}{Corpus (INCIBE)} & Maximum & 0.9729 & 0.9735 & 0.9737 & 0.9702 & 0.9727 & 0.9732 \\  & Minimum & 0.5891 & 0.5768 & 0.5869 & 0.5635 & 0.5768 & 0.5628 \\  & Victories & 1821 & 1297 & 558 & 1031 & 916 & 1891 \\ \hline \multirow{3}{*}{META} & Maximum & 0.9607 & 0.9600 & 0.9588 & 0.9597 & 0.9602 & 0.9626 \\  & Minimum & 0.7653 & 0.7797 & 0.4900 & 0.4862 & 0.7837 & 0.4111 \\ \cline{1-1}  & Victories & 1909 & 2064 & 834 & 525 & 913 & 1269 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Maximum, minimum, and number of victories in the word embedding analysis.  
Figure 1: Average and standard deviation of value in word embedding of each translation. Axis X: translators. Axis Y: average.
## 2 Materials and Methods
### Data Analysis  
First, one SUP stroke movement was separated into four phases: catch, pull, exit, and recovery. \"Catch\" refers to the process of placing the paddle blade in water. The \"pull\" phase is the process, in which the blade is completely immersed in water and is swung backward to generate the forward power [17]. In the \"exit\" and \"recovery\" phases, the blade is pulled out of water and returned to the starting position before starting the next catch phase. In this study, accelerometer data were used to segment the four phases (exit, recovery, catch, and pull) during one stroke. For example, we analyzed stroke data for right-handed participants when they paddled on the right side. At this moment, they placed their left hands (grip hands) on top of the paddle and held a shaft with their right hand to provide propulsion force. Data were treated as a cycle from the beginning of the exit period to the end of the next pull period. The time for one paddle cycle was normalized to 0%-100%. The EMG sensors received raw EMG data and first removed frequencies less than 3 Hz and greater than 1000 Hz. After signal rectification, a 20-450 Hz bandpass filter and a 60 Hz notch filter were used. Finally, a root mean square (RMS) envelope with a window of 0.125 seconds was used. The average and maximum EMG activation values (\(\mu\)V) for each muscle were then calculated. The time series analysis of each muscle activation was based on 50% of the peak activation value. Here, the same peak value was used for kneeling and standing. The period greater than 50% was defined as the main activation period, and that less than 50% was the nonactivation period. The starting and termination of activation for each muscle group were defined using the starting and ending times of the main activation period, respectively, and all muscle activation and termination times were presented as a percentage of the paddle cycle (% paddle cycle).
## 5 Experiments and Results  
### Experiments Setting and Descriptions  
To obtain the effectiveness of the proposed method, a standard DN test system is chosen to test based on [21]. The single-line diagram of the DN test system is shown in Figure 6. There are 14 nodes in this system with three feeders. The reactive power devices are one ULTC, one substation capacitor and three feeder capacitors in the system, whose configuration information is shown in Table 2.  
Figure 5: Flow chart of control variable fine adjustment method.  
Figure 6: Test case of standard 14-node system.  
In the test system, the nodes are separated into three types, residential load type, commercial load type and industrial load type (Table 3). The historical load data used in the test are from practical hourly load data collected by Nantucket Electric Company. Based on the load data model presented in Section 2, the simulation load data are established with the practical load for nine years from 2006 to 2014 according to Equation (10). The data of the years from 2006 to 2013 are treated as historical load. Suppose load forecasting has been accurately completed and ignore the load forecasting deviation. The data of 2014 can be used to test the method. An improved multi-population genetic algorithm (MGA) is chosen to obtain the historical RPO dispatching scheme based on the simulation load. Then, the history data including the sequence of tap setting actions and capacitor capacities are available.
# A Numerical Simulation of the Underwater Supersonic Gas Jet Evolution and Its Induced Noise
## 2 Numerical Methods  
### Mathematical Method  
The numerical simulation of an underwater supersonic jet and its noise involves calculating compressible flow, two-phase interaction, turbulence, and jet noise [25]. A mixture multiphase model with a hybrid sharp/dispersed interface modeling method was used to simulate multiphase flow with solid coupling, allowing all phases to move at the same speed [26]. This model is particularly appropriate for small-scale multiphase flow with slip velocities between the phases and locally isotropic assumptions. This paper focuses on two-phase flow (\(n=2\)) in which the primary phase is compressible air, assuming an ideal gas, and the secondary phase is incompressible water, the effects of gravity, and evaporation-condensation being neglected [27]. The continuity, momentum, energy, and volume fraction equations for the mixture can be expressed as follows:  
\[\frac{\partial}{\partial t}(\rho_{m})+\
abla\cdot\left(\rho_{m}\overset{\to}{v} _{m}\right)=0 \tag{1}\]  
\[\frac{\partial}{\partial t}\left(\rho_{m}\overset{\to}{v}_{m}\right)+\
abla \cdot\left(\rho_{m}\overset{\to}{v}_{m}\overset{\to}{v}_{m}\right)=-\
abla p +\
abla\cdot\left[\mu_{m}\left(\
abla\overset{\to}{v}_{m}+\
abla\overset{ \to}{v}_{m}^{T}\right)\right], \tag{2}\]  
\[\frac{\partial}{\partial t}\sum_{k}(\alpha_{k}\rho_{k}E_{k})+\
abla\cdot\sum_{ k}\left(\alpha_{k}\overset{\to}{v}(\rho_{k}E_{k}+p)\right)=\
abla\cdot\left(k_{ eff}\
abla T+\left(\overline{\tau}_{eff}\overset{\to}{v}\right) \right), \tag{3}\]  
\[\frac{\partial}{\partial t}\left(\alpha_{p}\rho_{p}\right)+\
abla\cdot\left( \alpha_{p}\rho_{p}\overset{\to}{v}_{m}\right)=0 \tag{4}\]  
where the subscript p denotes the secondary phase, \(\overset{\to}{v}_{m}\) denotes the mass-averaged velocity, \(\rho_{m}\) denotes the density of the mixture, \(\mu_{m}\) denotes the mixture dynamical viscosity coefficient, and \(k_{eff}\) denotes the effective thermal conductivity; the corresponding expressions are as follows:  
\[\overrightarrow{v}_{m}=\frac{\sum_{k=1}^{n}\alpha_{k}\rho_{k}}{\rho_{m}} \overrightarrow{v},\ \rho_{m}=\sum_{k=1}^{n}\alpha_{k}\rho_{k},\ \mu_{m}=\sum_{k=1}^{n}\alpha_{k}\mu_{k},\ k_{eff}=\sum \alpha_{k}(k_{k}+k_{t}), \tag{5}\]  
where \(\alpha_{k}\) denotes the volume fraction of the \(k\)th component, \(k_{t}\) denotes the turbulent thermal conductivity coefficient, and \(\overline{\overline{\overline{r}}}_{eff}\) denotes the effective viscosity stress tensor.
###### Abstract
The subsidence monitoring of infrastructure in Shanghai has always been a research hotspot of scholars at home and abroad. [PERSON] et al. [25] monitored Shanghai's reclamation area (Lingang New Area) based on time series InSAR by using various radar data and showed that the ground deformation after reclamation in Shanghai experienced three obvious stages: primary consolidation, slight rebound, and platform stage. [PERSON] et al. [26] processed 49 TerraSAR-X images with TSInSAR technique, obtained the subsidence time series of viaudet in Shanghai, and analyzed it in combination with construction time, load, and other factors. [PERSON] et al. [27] extracted the average subsidence rate of coherent targets in line of sight by using the ALOSPALSAR data that covered the Shanghai area from 2007 to 2010 with the small baseline time series analysis method and verified the feasibility of SBAS technique in Shanghai by comparing with the leveling data. The obvious subsidence funnel in Shanghai area has been detected. [PERSON] et al. [28] used SBASInSAR technique to study the temporal evolution characteristics of surface deformation in Shanghai land reclamation area from 2007 to 2016. Combined with C-band and X-band data, the average cumulative deformation of Shanghai land reclamation from 2007 to 2016 reached 25 cm. [PERSON] et al. [29] obtained the deformation situation of Shanghai reclamation area in 2016 by using COSMS-SkyMed and Sentinel-1A data and obtained that the subsidence rate of Shanghai reclamation area was relatively high with a maximum subsidence rate reaching 30 mm/year. [PERSON] et al. [30] used PSInSAR technique to monitor the main traffic infrastructure in Shanghai area and compared with the level data. They found that some traffic lines in Shanghai had serious subsidence and verified the feasibility of PSInSAR in subsidence monitoring in Shghai area. [PERSON] et al. [31] used InSAR technique to monitor Shanghai Lupu Bridge and verified the feasibility of PSInSAR technique in deformation monitoring of a single building. The subsidence monitoring in Shanghai is mainly focused on the local monitoring of infrastructure and other aspects. Studies on the large-scale surface subsidence monitoring in Shanghai are relatively few, and discussions on the subsidence mechanism are lacking. Moreover, the research data are mostly from 5 years ago, thereby lacking certain timeliness. In this paper, we first processed and analyzed the Sentinel-1A image data of 36 scenes based on PSInSAR technique, and then obtained the deformation rate field from January 2018 to March 2020 in Shanghai. Second, we investigated the surface subsidence spatial-temporal characteristics in Shanghai area in detail. Finally, the driving factors of subsidence were analyzed in combination with urbanization construction, groundwater level change, rainfall, and other factors in Shanghai. These factors provide scientific data support for the surface subsidence prevention and control in Shanghai area.
## 3 Monomode DFB Quantum Cascade lasers in the Mid Infrared Spectral Regime (MIR)
The latter uses short pulses (around 10 ns) and a DC-current ramp below threshold to heat up the laser and provide the desired tuning from pulse to pulse. Especially inter-pulse tuning introduces a high heat load into the chip, which needs to be dissipated before the next ramp-cycle to get the maximum tuning range. The more efficiently heat can be dissipated, the higher repetition rates for laser pulses and ramp-cycles can be chosen in order to gain higher average output power and therefore greater signal strength. Figure 9 shows the dependency of the available tuning range on the repetition rate in inter-pulse mode for two DFB QCL of matching measurements operating at 11.43 \(\upmu\)m. Device A is mounted epitaxial side up on c-mount, device B is mounted epitaxial side down on c-mount with a high reflectivity (HR) coating on its back facet. The positive effect of the epitaxial side down mounting and HR coating can clearly be seen from the stability of the output power and tuning range as the repetition frequency is increased up to 1 MHz. Figure 8: Intra-pulse tuning of a DFB QCL emitting at 11.43 \(\upmu\)m driven with 250 ns pulses at room temperature. The black line shows the raw pulse shape. A measurement using an etalon (green line) with a free spectral region (FSR) of 0.049 cm-1 indicates a chirp-rate of 0.382 cm-1/100 ns. An absorption experiment (red line) allows for precise determination of the wavelength axis using well known absorption lines of C\({}_{2}\)H\({}_{4}\).