esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost 400 Gbps DR4 Silicon Photonics Transmitter for Short-Reach Datacenter Application
## 2 MACOM's Silicon Photonics Roadmap  
Figure 1 schematically depicts a transceiver block diagram. In optical routing, our high output (>20 dBm) laser chips, which are yield enhanced and cost reduced by etched facet technology [24; 25], are first packaged in Transmit Optical Sub-Assembly (TOSA) or Transistor Outline can (TO-can) and the light is coupled into the silicon photonics engine using optical lenses. Then, the light is split into several lanes, modulated, multiplexed (following 100 G Lambda MSA FR4 standards) and routed out to a fiber (array) as Tx. The laser chips can also be flip-chip bonded into the silicon photonics engines, as described in our previous work [23; 25]. For Rx, the optical signal is coupled into the engine chip through a fiber (array), TE polarized, de-multiplexed (following 100 G Lambda MSA FR4 standards) and converted to an electrical signal by a Ge/Si photodetector [26].  
In electrical routing, the serial electrical signal is first sent into a MACOM PrismTM chip, where the DSP chip is co-packaged with the driver IC for pre-emphasis, PAM-4 mapping and linear amplification. Then, the amplified signal is fed  
Figure 1: Way to build an optical transceiver using silicon photonics engine.  
engine through wire bonding as Tx. The microcontroller reads the monitor feedback from the silicon photonics engine and generates DC controls, such as modulator bias, modulator phase and MUX filter tuning vs. temperature. The microcontroller also manages the driver swing tuning and the data processing complexity in the PrismTM chip. For Rx, the received electrical signal is first amplified by a two-stage TIA (MAMF-03819) and then processed by the PrismTM chip for Feed Forward Equalization (FFE), Decision Feedback Equalization (DFE) and some other proprietary equalization before PAM-4 de-mapping. Finally, the recovered electrical signal goes to the serial interface. Note that the DSP is optimized for the 100 Gb/s per lane silicon photonics engine for both Tx and Rx, providing lower latency data processing and low power consumption. The DSP is also able to activate real time KP4-FEC (forward error correction) (de-)coding, which is 'overclocking' the silicon photonics engine for >100 Gb/s per lane transmission.
## 4 Discussion  
### Input Factor Selection in the Model  
It can be seen from Section 3.2.2 that the R2 variation range of the eight types of machine learning algorithm models in the six combinations was 0.22-0.62, the RMSE variation range was 2.10-2.99, and the MAE variation range was 1.08-1.65. The input factors in combination 1 were the top three meteorological factors (RG, VPD, Soil_G), which were ranked by the correlation coefficients in Figure 4. When the temperature was added, the prediction accuracy of the eight models was improved, and the R2 increased by 2-13%, which is consistent with the conclusion obtained by [PERSON] et al. [35] when using machine learning to fit water flux; that is, the accuracy of the models obtained by each algorithm increases with an increase in the number of input features. Then, the meteorological factors RH, Soil_T, Soil_VWC, and WS were added to the model input factors in turn. The results showed that, when RH, Soil_T, and Soil_VWC were added, the prediction accuracies of MLR, RF, and MLP were almost unchanged and that the prediction accuracies of KNN, Adaboost, GBRT, and XGBoost fluctuated obviously. In  
Figure 8: Comparison of NEE hourly variation characteristics estimated by Reddyproc and MLP models.  
terms of stability, MLP was the highest, and CART and RF were the worst, which is the same as the research results of [PERSON] et al. (2018) and [PERSON] et al. (2018). In other words, the RF algorithm had a serious overfitting phenomenon because the tree model had a high dependence on the continuity of the data distribution, and the RF model was easy to fall into overfitting in some sample sets with large noise. After adding WS, the R\({}^{2}\) of the eight models increased significantly. The Pearson correlation coefficient between the CO\({}_{2}\) flux and WS was zero, and the significant \(p\) value was less than 0.001, indicating that WS was also a key factor affecting the CO\({}_{2}\) flux in the Longbao area. This conclusion shows that the determination of the input factors in the algorithm model was not completely consistent with the correlation conclusion. The selection of the input factors needed to be added one by one on the basis of the correlation analysis to obtain the best model feature combination.
## 1 Introduction  
The ultra-fast microwave frequency measurement with a broadband is significant for application in the electronic warfare system [1, 2]. A traditional electrical frequency measurement system can realize an ultra-high resolution within a limited processing bandwidth. In order to increase the measurement bandwidth as well as reduce the volume of the hardware and the expense, numerous microwave photonic frequency measurement methods [1] including frequency-to-power mapping, photonics-assisted microwave channelization, and frequency-to-time mapping (FTM) have been demonstrated. The technique, where the frequency information is mapped to an optical/microwave power has been reported to implement the instantaneous frequency measurement [3, 4, 5, 6]. The power ratio between dual-channel optical/microwave powers is established using an optical comb filter [3], an optical mixing unit [4], or a couple of dispersive delay elements [5, 6] as frequency-dependent transfer function. The FPM-based technique can capture the instantaneous frequency in a wideband and high resolution (up to multi-MHz), but it cannot be used to measure the multitone signal. The channelization [1, 7] has been developed based on space-division multiplexing, wavelength-division multiplexing, and time-division multiplexing. Although the instantaneous bandwidth is larger than hundreds of GHz, the resolution is vastly limited by the channel resolution. In [8, 9, 10, 11], the FTM-based approaches to implement multiple-frequency measurement have been proposed using the optical time gate with the resolution up to hundreds of MHz. Recently, our group has proposed an FTM technique using frequency scanning Fourier domain mode-lock optoelectronic oscillator [11] to achieve a high-resolution single/multitone signal measurement. It is obvious that the multiple discrete devices used to set up the measurement system increase the cost and complexity of the frequency measurement system.  
In this letter, a compact microwave multifrequency measurement system based on FPM is proposed and experimentally demonstrated using an integrated linear-frequency modulation (LFM) signal generator. For an unknown microwave signal, the frequency information is inversely mapped using the relationship between the input microwave frequency and time difference of a pair of pulses. As a main part, the LFM signal is generated by heterodyning of two lasers with a monolithic integrated laser array, where a saw-tooth signal is added to modulate on one of the two lasers. Using our proposed scheme, single/multitone microwave signal can be measured with the property of broadband and high resolution. The reconfiguration among multiple bands is easy to achieve by tuning the bias current of lasers. From the experimental results, a large microwave frequency measurement ranges from 4 to 12 GHz and a low measured error of 90 MHz is realized. Moreover, a measurement resolution for two-tone signal is approximately to 150 MHz.
## 3 Research Methods
### CRNN Grading Algorithm Based on Time Series
#### 3.2.2 Long Short-Term Memory Network  
LSTM is a special RNN model that solves the problem of the standard RNN structure performing poorly at capturing long-term information. LSTM introduces three gate units and memory cells that are the same as the hidden state to record additional information. A structural diagram is shown in Figure 3.  
Figure 3: LSTM network structural diagram.  
The input of LSTM is the input of the current time step and the hidden state of the previous time step. The output is the fully connected layer's output for the calculation of the Sigmoid function. The calculation of each gate unit is shown in Equation (10).  
\[\begin{array}{l}I_{t}=\sigma(X_{t}W_{xi}+H_{t-1}W_{hi}+b_{i})\\ F_{t}=\sigma(X_{t}W_{xf}+H_{t-1}W_{hf}+b_{f})\\ O_{t}=\sigma(X_{t}W_{x0}+H_{t-1}W_{ho}+b_{o})\end{array} \tag{10}\]  
where \(I_{t}\) is the input gate output at time step \(t\); \(F_{t}\) is the forget gate output at time step \(t\); \(O_{t}\) is the output gate output at time step \(t\); \(W_{xi}\), \(W_{xf}\), \(W_{xo}\), \(W_{hi}\), \(W_{hf}\), and \(W_{ho}\) are model weight parameters; and \(b_{i}\), \(b_{f}\), and \(b_{o}\) are model bias parameters. Then, the candidate memory cell parameter, \(\widetilde{C}_{t}\), at time step \(t\) is calculated, as follows:  
\[\widetilde{C}_{t}=\tanh(X_{t}W_{xc}+H_{t-1}W_{hc}+b_{c}) \tag{11}\]  
where \(C_{t}\) is the memory cell of the current time step, and \(\odot\) represents the element multiplication of the matrix. Finally, the output gate controls the output of the memory cell information to the hidden state, as shown in Equation (12). When the output gate is approximately 1, the memory cell will be passed to the hidden state for use by the output layer; when the output gate is approximately 0, the memory cell information is only retained by itself.  
\[H_{t}=O_{t}\odot\tanh(C_{t}) \tag{12}\]  
The CRNN architecture integrates spatial feature extraction and temporal sequence modeling for video-based time series grading, as shown in Figure 4. The entire network architecture is analyzed as follows: the network uses a CNN to process video clips by frame. Through three layers of convolution, the number of channels increases from 32 to 48 and then to 64, and then single-frame spatial features are extracted. Each layer is followed by maximum pooling to compress the spatial dimension. The feature sequence is modeled with temporal dependencies through a bidirectional LSTM, with a total of 256 units. Finally, the classification is completed by aggregating the time step output through a fully connected layer.  
Figure 4: CNN + LSTM (CRNN) network structure diagram.
## 4 Experiment
### Dataset and Experimental Environment  
The light field cameras used in this experiment include: Raytrix R12, Raytrix R32, and HR260. Among these, the first two were manufactured by Raytrix 3D Light-Field Vision corporation of Germany, and the last one was independently developed by the research group, as shown in Figure 11. Specific camera information and experimental configurations are presented in Table 1.  
The experimental environment was a workstation T3650 (Intel Corei7-11700X CPU, GeForce RTX 3060 GPU, 64 GB memory, 1 TB solid-state drive, 8 TB mechanical hard drive), with Ubuntu 20.04 as the operating system.  
Dataset and Scene Design: (1) One group of public datasets, using light field raw images captured by Raytrix R12 camera, which can be obtained from the public website: [[https://github.com/comsee-research/plenoptic-datasets](https://github.com/comsee-research/plenoptic-datasets)]([https://github.com/comsee-research/plenoptic-datasets](https://github.com/comsee-research/plenoptic-datasets)) (accessed on 20 August 2023), consisting of three scenes with stereoscopic objects such as dolls, plane objects with textures like books and postcards, and calibration boards with multiple repeated textures, named R12-Indoor; (2) two groups of field-collected datasets, using light field raw images captured by Raytrix R32 and HR260 cameras. To verify the algorithm's applicability in different scenarios, this study selected light field datasets collected in indoor and outdoor scenes, with each dataset containing light field raw images and laser scanning point clouds. 1 Indoor Scene: Three scenes consisting of plane calibration boards with repeated textures, ordinary plane objects with rich texture features, and stereoscopic objects such as cups and toolboxes, named R32-Indoor. 2 Outdoor Scene: Consisting of plane calibration boards with repeated textures in a playground, distribution boxes with texture features surrounded by railings, and stereoscopic objects such as batches of parked buses, named HR260-Outdoor.  
Finally, this study employed the probabilistic depth estimation algorithm by [PERSON] (2016) [25], the BLADE algorithm by [PERSON] et al. (2023) [34], and other famous multi-focus light field image depth estimation algorithms for experimental comparison and analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Camera** & **Raytrix R12** & **Raytrix R32** & **HR260** \\ \hline Aperture & F/2.4 & F/2.4 & F/5.6 \\ Focal Length & 35 mm & 300 mm & 115 mm \\ Lens Array (Row \(\times\) Col) & 176 \(\times\) 152 & 194 \(\times\) 126 & 50 \(\times\) 65 \\ Pixel Size & 0.0055 mm & 0.0065 mm & 0.0037 mm \\ Raw Image Resolution & 4080 \(\times\) 3068 & 6560 \(\times\) 4948 & 6240 \(\times\) 4168 \\ Distance Range (m) & 0.4–1.5 & 2–4 & 8–23 \\ Focusing Distance & 2–2.5 & \(\infty\) & 2–2.5 \\ \hline \end{tabular}
\end{table}
Table 1: Camera information and experimental configuration table.  
Figure 11: Light field cameras. (**a**) Raytrix R12 camera; (**b**) Raytrix R32 camera; (**c**) HR260 camera.
## 4 Results
### Tests of DE with the Real Experimental Data  
Based on the DE tests with artificial experimental data, MC = 500 was chosen to run the fitting of real experimental data. PSIIRC linear spectroscopy was taken from two sources: absorption and steady-state fluorescence spectra are from [48] and dichroism spectra are from [25]. The total number of free parameters of the PSIIRC exciton model was eleven: eight electron transition energies \(\Omega_{n}\), the dielectric constant \(\varepsilon\) to scale the Chl-Chl coupling energies (6), the full width at half maximum of inhomogeneous broadening \(FWHM_{\Omega}\), and the D1-D2 special pair coupling \(J_{12}\). Some features of the applied PSIIRC exciton model and DE settings can be found in [40]. DE was run ten times; the best results are presented in Figure 4.  
Figure 4: Fitting of the PSIIRC linear optical spectroscopy measured at 77 K. Black thick lines are the experimental spectra. The best results of optimization (blue lines with circular markers) for absorption (**A**), steady-state fluorescence (**B**), circular (**C**), and linear (**D**) dichroism are presented. Thin, colored lines are the simulated spectra found by DE after the first 10 generations. \(\chi^{2}\) evolution during 100 generation of DE (**E**).
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models
### Linear Regression  
LR attempts to determine the relationship between a dependent variable (response variable) and one or more independent variables (explanatory variables) by fitting a linear regression equation [51]. Given our dataset, \(T=\{(x_{i},y_{i}),i=1,2,\cdots,n\}\), where \(n=617\) is the size of the sample dataset, \(x_{i}\in R^{n}\) are the independent variables representing a sample of selected features from the FA, SF, LP, and crack width before self-healing, \(R^{n}\) is \(n\)-dimensional space, and \(y_{i}\in R^{1}\) is the target output (crack width after self-healing) that corresponds to \(x_{i}\). Let \(d=4\) denote the number of an independent variable of a random vector \(x=\{x_{1};x_{2};\cdots;x_{d}\}\) and \(y\) be the corresponding output (dependent variable). The general formula of LR for predicting the self-healing capability of ECC can be expressed as follows [51]:  
\[y=w_{1}x_{1}+w_{2}x_{2}+\cdots\cdots+w_{d}x_{d}+b \tag{2}\]  
where \(w_{i},(i=1,2,\cdots,d)\) is the regression coefficient, while \(b\) is an error term. The prediction performance of LR was used as a benchmark to compare the performance of other individual and ensemble models in this study.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multirow{2}{*}{**Number of Cracks**} & \multicolumn{2}{c}{**Crack Width before Self-Healing**} & \multicolumn{2}{c}{**Crack Width after Self-Healing**} \\ \cline{3-6}  & & **Min (\(\upmu\))** & **Max (\(\upmu\))** & **Min (\(\upmu\))** & **Max (\(\upmu\))** \\ \hline FA70 & 87 & 3.28 & 134.69 & 0 & 121.37 \\ FA65-SF5 & 77 & 4.37 & 135.47 & 0 & 124.01 \\ FA60-SF10 & 88 & 5.18 & 121.78 & 0 & 113.11 \\ FA55-SF15 & 88 & 3.45 & 115.8 & 0 & 109.53 \\ FA65-LP5 & 112 & 7.65 & 119.45 & 0 & 105.65 \\ FA60-LP10 & 37 & 5.62 & 126.82 & 0 & 110.97 \\ FA55-LP15 & 61 & 6.42 & 132.65 & 0 & 115.95 \\ FA55-SF5-LP10 & 34 & 8.74 & 123.09 & 0 & 110.78 \\ FA55-SF10-LP5 & 33 & 4.64 & 131.57 & 0 & 119.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of crack samples and range of the crack width before and after self-healing collected from the ECC mixes.
## 3 Results
### Estimating the Vertical Resolution of Temperature and Water Vapor Retrievals
The distance between peaks was then converted from pressure to height (km) above the surface, assuming a standard atmosphere. The height values from AKM peaks were then subtracted to estimate the layer thickness (km). Figure 5: Mean averaging kernel diagonals for (**a**) temperature and (**b**) water vapor retrievals where the IR + MW retrieval was successful (112 profiles). Thin black lines show the individual profiles, while thicker lines are the average. Solid horizontal lines are the standard deviation. Thinner dashed lines are local maxima or peaks of the individual profiles. Dashed lines are darker where more profiles peaked. The average vertical resolution of successful IR + MW retrievals of temperature and water vapor for all matched flights is summarized in Table 3. Near the surface, NUCAPS could retrieve temperature and water vapor with a 1.7 km resolution and 2.2 km, respectively, for all 181 profiles. These values agree with those in [28], which show that the global average resolution at 850 mb for NUCAPS temperature retrievals is 2-4 km, but it is closer to 1.6-3.0 km over the tropical Atlantic. The results in Table 3 are also consistent with [23], which estimate the vertical resolution of temperature and water vapor to be roughly 2.5 km and 2.8 km, respectively, over the Atmospheric Radiation Measurement (ARM) sites in the Southern Great Plains (36\({}^{\circ}\)N, 97\({}^{\circ}\)W) in the United States and Manus Island (2\({}^{\circ}\)S, 147\({}^{\circ}\)E) in the tropical western Pacific. Sounding retrievals are often more skillful over ocean than land, which is in part because the IR surface emissivity is more homogenous than over land.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Hybrid Methods  
Figure 11 demonstrates how combining KF and NN can improve GPS/INS integrated systems' overall performances by overcoming their respective flaws. KF observations will not be updated if the GNSS signal is disrupted. When NNs support INS, navigation performance can be enhanced. To fill in the gaps left by GPS data disruptions, an NN-based fusion approach is suggested. A properly trained NN is capable of anticipating and correcting the interrupted position signal inaccuracy. Several designs were used to investigate an NN-aided adaptive EKF, including the prediction of adaptive factors for gain scaling or tuning, prediction of INS errors for error correction, and measurement reconfiguration. The hybrid methods essentially include the following three categories: (1) prediction of adaptive factors for gain scaling or tuning [57; 58; 59; 60; 61; 88]; (2) an NN as the INS errors predictor for error compensation [57; 59; 69; 78; 80; 81; 82; 89]; and (3) measurement reconfiguration based on an NN [76; 90; 91; 92; 93].  
Figure 10: (**a**) Training and (**b**) prediction modes of GNSS/INS integrated navigation using RNN as the estimator.
## 2 Materials and Methods
### Mechanical Properties Test  
#### 2.2.1 Experimental Setup  
The mechanical properties of the proposed structures were analyzed by finite element simulation under various load conditions. To validate the accuracy of the simulation results, 3D printing technology was employed to fabricate the proposed structures. Compression and bending tests were conducted using an Instron-5566 universal testing machine, while torsion tests were performed using a CTT500 microcomputer-controlled electronic torsion test machine. The compression tests were carried out at a loading displacement rate of 1 mm/min, and force-displacement curves were obtained. For the 3-point bending tests, a CMT5105 electronic universal testing machine with a loading displacement rate of 2 mm/min was used, with a setup consisting of a 20 mm diameter compression head and a 60 mm span. The crashworthiness behaviors of the proposed structures were evaluated using a drop impact tester (DIT302A-TS, Shenzhen Wance, Shenzhen, China).
## 3 Test Data Analysis
### Mechanism of Adsorption of BG Dye onto H.N.T.  
According to the literature, the studies establish the high surface area of H.N.T., such as 65 m\({}^{2}\)/g and specific weight = 2.53 g/cm\({}^{3}\)[59]. The HNT particles surface gap depends on the factors recognized with measurements, shape, cross-segment, and appearance, and length (20-10,000 nm), and the unpredictable state of cross-segments prompt the outside surface of HNT particles to expand the surface region. Moreover, the HNT particles present as individuals, with no lump formation causing surface regional expansion [59; 60]. Additionally, [PERSON] et al. assert that HNT have significant porosity and, thus, forms an entity of interest with the (50 nm) qualities [61].  
The plausible mechanism of cationic BG dye adsorption onto Halloysite Nanotubes is likely to occur due to the progression of a multi-step activity. The factors having a credible influence on adsorption are solution acidic level (pH), the concentration of dye, amount of adsorbent used, and variation in the temperature. Moreover, monolayer formation is initiated for the mass transfer of BG onto Halloysite Nanoclay. Additionally, the process of diffusion is likely to be a slow process. Therefore, the strong adherence of BG dye into Halloysite Nanoclay is probably by the bonds established between dye HSO4+ anions and the -CH3 group. Lastly, weak interactions are due to attract Vander Waal forces, and strong electrostatic forces of attraction of BG dye are because of the HSO4+ cationic group and -CH3 group. Thus, Halloysite Nanoclay contributes substantially to the adherence of the BG dye.
## 3 The PMT Readout and Trigger System
### First and Second-Level Triggers  
All the trigger masks that include anticoincidences, such as the GRB masks, are prone to produce spurious triggers if signals present delays between them.  
A second-level trigger system has been developed to minimize the occurrence of these problems. This system allows the sampling of the trigger masks twice, assuring that the overlap between signals is long enough to exclude spurious triggers.  
If this overlap lasts at least 30 ns, a \"valid trigger\" signal is produced and the FPGA starts the acquisition and the ADC conversion. If not, the CITIROCs analogue memories are cleared and the peak detection circuit is reset.