esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Imaging Experiment and Analysis
### Ablation Study Evaluation  
We conducted ablation experiments to verify the effectiveness of the multimodal loss function and the image regional division and channel selection module. The multimodal loss function was removed, making the loss function \(\mathrm{L}_{DDZMR}=\mathrm{L}_{\mathrm{information}}\). The network was then trained with the same parameters, and the effectiveness of the trained network was tested, as shown in Figure 8, the chroma-specific contrast of the image was not recovered, and the blur and haze of the image were not effectively removed. Next, the regional division and channel selection module was removed, and the same steps were used to test the configuration. As shown in Figure 9, image restoration was minimal, and the edge artifacts of the image were more noticeable than before processing.  
Figure 6: SHDCIS outdoor imaging experiment; (**a**,**d**) are the raw images captured by the detectors taken outdoors by SHDCIS; (**b**,**e**) are the processed results using the convolutional neural network proposed in [10]; (**c**,**f**) are the reconstructed images calculated by the DDZMR network model.  
A quantitative comparison of the Ablation testing is shown in Table 1. Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) enable quantitative objective evaluation of reconstructed images. PSNR mainly evaluates the ratio of the maximum possible  
Figure 8: (**a**,**d**,**g**) are the original images captured by the sensor; (**b**,**e**,**h**) are the images after processing when the multimodal objective function was replaced by the standard cross-entropy function; (**c**,**f**,**i**) are the results of normal processing by the DDZMR network model.  
Figure 7: SHDCIS imaging test of resolution target; (**a**) is the image captured directly by the detector; (**b**) is the image reconstructed by the DDZMR network.  
Figure 9: (**a**,**d**,**g**) are the original images captured by the detector; (**b**,**e**,**h**) are the paths of 25 regions replaced with a 1-block area; (**c**,**f**,**i**) are the results of normal processing by the DDZMR network model.  
power of the real signal of the image to the destructive noise power that affects its representation accuracy. SSIM evaluates the quality of the image from three aspects: Luminance, Contrast, and Structure. The PSNR and SSIM of images processed by the DDZMR network are improved by 12% and 4% compared to the ablation component, and the training time of the network is shortened by 33%. Consquently, experiments show that the multi-modal loss function customized according to the HDOE imaging image quality degradation model is effective for SHDCIS, which can make the DDZMR network restore the image quality better. When the degree of image degradation in the image plane space under different off-axis fields of view is inconsistent, the image regional division and channel selection module can help the network to better learn the degradation process of images at different positions, and improve the convergence speed of the network.
# Review of Transition from Mining 4.0 to Mining 5.0 Innovative Technologies
## 3 Review of the Transition from Mining 3.0 to 4.0
### Smart Sensors in Mining 4.0  
It is impossible to analyze the transition to Mining 4.0 without considering the examples of smart sensor implementation in the areas of accurate and timely diagnostics and finding engineering solutions [72]. Successful examples of using smart sensors in mining are the following:  
* Belt 4.0--underground and surface mine conveyor condition and loading control system based on artificial intelligence, which makes it possible to eliminate sudden stops due to breakdowns completely [73];
* Air flow visualization system connected to mine ventilation equipment to radically increase the accuracy of modeling [74];
* LiDAR (light identification detection and ranging) systems integrated with GPS (global positioning system) when creating a 3D cloud to predict sudden movements of rock mass [75];
* Land area subsidence prediction in clusters with high concentration of underground mining using differential interferometric synthetic aperture radar [76];
* Integration of InSAR (interferometric synthetic aperture radar) and LiDAR data with analysis of photographs made by aerial drones to predict soil deformations [77];
* Remote creation of predictive maps of surface subsidence and collapse over working mine longwalls using differential radar interferometry with an accuracy of 4 cm [78], using the GrabCut method to create high-precision scalable visual attention models--the first step towards full robotization of deserted mines [79];
* Integration of data from geoscanners with information from global navigation satellite system devices to achieve the highest accuracy of 3D modeling of underground mine workings, sufficient for unmanned control of processes in underground mines (with time synchronization using pulse per second) [80]. At the same time, ground-penetrating radars are also developing in the direction of increasing the accuracy of determining the deformation of mine workings, already in the submillimeter range. For surface mines, we should note the effectiveness of such smart sensors as ground-based synthetic aperture radar, which operation is based on the frequency-modulated continuous wave [81];
* Application of the method of persistent scatterer interferometry for increasing the accuracy of forecasting man-made earthquakes by 35.2% (based on refined external model-based decomposition of deformation). Moreover, SAR single-look complex was effective for sequential analysis of surface images, followed by analysis of the spatial distribution of the interferogram [82];
* Complex application of various smart sensors integrated with drones, cameras, and equipment controllers, proved to be very useful for advanced decision-making and perspective planning based on 3D virtual models (an example of Rio Tinto, Australia-Figure 9 [83]).
# Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models
## 2 Materials and Methods
### Synthetic Data Evaluation
The generation of 1D template samples is done by randomly selecting a function and its parameters from the provided intervals. model parameters and from the fitted model. For the AE-based reconstruction algorithm, indirectly predicted spot parameters are computed from the reconstructed spot, and directly predicted parameters are from the bottleneck features. As the synthetic dataset holds information about spot background intensity, the AE can be trained to reconstruct the spot in two modes--either removing or leaving the background. Both cases are evaluated for reconstruction and for spot parameter prediction. The evaluation of spot modeling is based on the following metrics: the normalized sum of squared error (NSSE) (Equation (7)) for the spot reconstruction evaluation and the normalized mean of squared error (NMSE) for the spot parameter estimation. NMSE is MSE normalized by the variance of ground-truth parameters. During the modeling of protein spots (fitting baseline mathematical models), a patch of a 2 DGE image is approximated by a mathematical model. For the constrained minimization problem, the Matlab implementation of the Trust-region-reflective least-squares algorithm [52, 53] is used, which seeks to minimize the sum of squared residuals. For the evaluation of the spot modeling, the normalized sum of squared residuals \(E_{\text{r}}\) is used as a metric and is defined by the function:  
\[E_{\text{r}}=\sum_{x,y\in\mathbf{S}}\frac{\left(I_{\text{M}}(x,y)-I_{\text{GT}}(x, y)\right)^{2}}{\sqrt{n_{\text{S}}}\left(A_{\text{GT}}-B_{\text{GT}}\right)^{2}}, \tag{7}\]  
here \(I_{\text{M}}(x,y)\)--2 DGE image patch containing the protein spot being modeled; \(I_{\text{GT}}(x,y)\)--the ground-truth (target) image patch; \(n_{\text{S}}\)--the number of points in the modeled region  
Figure 7: Structure of the protein spot reconstruction algorithm. The autoencoder (AE) is the core part of the algorithm. In addition to the AE, there is a separate branch for the prediction of spot parameters from the bottleneck features (spot parameterizer). In this experiment, the spot parameterizer predicts the peak intensity and integrated intensity of the spot (\(n=2\)). Configurations of the encoder, decoder, and parameterizer are presented in blocks framed by the dashed line, showing the number of filters and kernel sizes of the convolutional layers, the number of units in dense layers, and the output shapes of all the layers. The loss of the current model has two components: mean absolute error (MAE) of spot reconstruction and MAE of spot parameter prediction. of the 2 DGE image; \(B_{\text{GT}}\) and \(A_{\text{GT}}\)--the background and the peak intensity values of the ground-truth spot, respectively.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
## 3 Results and Analysis
### Satellite Laser Ranging Validation  
Satellite laser ranging is a commonly used method for evaluating orbit accuracy, and its accuracy can reach the millimeter level [32]. Currently, BDS-3 has four MEO satellites (C20, C21, C29, and C30) that are observed by SLR stations coordinated by the International Laser Ranging Service (ILRS). This paper uses the SLR data to verify the BDS-3 satellite's best-fit arc length. The experiment uses three-month SLR data from 1 July 2021 to 30 September 2021. Moreover, we used 24 h predicted orbits for conducting evaluations.  
As shown in Figure 3, the best-fitting arc length is 42-48 h for C20, C21, and C30 satellites. The conclusion is consistent with the result of the final IGS precise ephemeris. However, the SLR residual of the C29 satellite is the smallest when the fitting arc is 60 h. It should be noted that the orbit prediction accuracy of the C29 satellite under the fitting arc length of 42-72 h is almost the same as 60 h, with differences within 3 mm.  
As is shown in Figure 4, most SLR residuals for BDS-3 satellites are within \(\pm\)20 cm. There are 361, 287, 238, and 245 normal points for C20, C21, C29, and C30 satellites, respectively. In this paper, the absolute values larger than 50 cm have been removed. After gross error elimination, there are 354, 281, 232, and 227 normal points left; thus, in this experiment, we used more than 96% of the original data for analysis. The accuracy of the predicted orbit is evaluated by SLR, and the RMS values of the SLR residuals for C20, C21, C29, and C30 are 5.36, 5.51, 5.49, and 6.01 cm, respectively. The overall RMS value of SLR residuals for the BDS-3 MEO satellites is 5.59 cm, which shows that the orbital accuracy of the BDS-3 MEO satellites is still maintained at a high level after 24 h of satellite orbit predictions.  
Figure 2: The accuracy of the first 6 h (**top**) and 24 h (**bottom**) of predicted BDS-3 IGSO orbits with different arc lengths.  
When the fitting arc length is 42 h, the laser-ranging validation residuals for those four satellites are shown in Figure 4.
## 3 Experiments and Results
### Experimental Environment and Parameter Settings  
All experiments in this paper were implemented in Python, the deep learning framework was Pytorch 1.10.0 from the Artificial Intelligence Research Institute of Facebook company in the United States, and the operating system was Windows 10 developed by Microsoft in the United States. The hardware system included a main frequency Intel i9-9900k CPU, 32 GB of running memory, and an Nvidia GeForce GTX 2080 ti GPU with 11 GB of graphics memory.  
The parameter settings of the model designed in this paper in the training process were as follows: the batch size was 16, [PERSON] was used as the optimizer, the global initial learning rate was set to \(1\times 10^{-3}\), the weight decay was set to \(1\times 10^{-4}\), the loss function was BCELoss, and the learning rate method was CosineAnnealingLR. A total of 600 epochs was used to train the models, and their weights were saved every 20 epochs. The loss function curve of the model is shown in Figure 8.
## 2 Basic Theory  
### Variational Mode Decomposition  
VMD is an effective method for processing non-stationary time series. Its core concept is to iteratively extract each mode's center frequency and bandwidth within a variational framework, minimizing the total bandwidth of all modes to achieve signal decomposition.  
The decomposition steps are as follows [31]:  
1. Convert the \(k\)-th mode component \(m_{k}\)\((t)\) into an analytic signal using the Hilbert transform to obtain its unilateral spectrum.
2. Modulate the analytic signal to the corresponding center frequency \(\omega_{k}\), confining it to the fundamental frequency band.
3. Estimate the bandwidth of \(m_{k}\)\((t)\) by calculating the gradient squared \(L^{2}\)-\(norm\) of the demodulated signal.  
The constrained variational problem of modal components is described as follows:  
\[\begin{split}\min\limits_{\{u_{k}\},\{\omega_{k}\}}\{||\; \partial_{t}[(\delta\;(t)+\frac{j}{\pi t})*u_{k}\;(t)]e^{-j\omega_{k}t}||_{2}^ {2}\}\\ \text{s.t.}\sum\limits_{k=1}^{K}m_{k}\;(t)=x\;(t),k=1,2,\ldots,K \end{split} \tag{1}\]  
To solve this, a quadratic penalty term and Lagrange multiplier are introduced to suppress Gaussian noise and ensure reconstruction accuracy. The problem is reformulated into an augmented variational problem:  
\[\begin{split} L\;(m_{k},\omega_{k},\beta)=&\quad \alpha\sum\limits_{k}||\partial(t)[\;(\delta\;(t)+j/\pi t)*m_{k}\;(t)]e^{-j \omega_{k}t}\;||_{2}^{2}+\\ &\left|\left|f(t)-\sum\limits_{k}m_{k}(t)\right|_{2}^{2}+\left< \beta(t),f(t)-\sum\limits_{k}m_{k}(t)\right>\right.\end{split} \tag{2}\]  
where \(\alpha\) is the penalty parameter.  
The solution is achieved iteratively through the alternating direction multiplier method (ADMM), with iterations stopping when  
\[\sum\limits_{k}\parallel m_{k}^{n+1}-m_{k}^{n}\;\big{|}_{2}^{2}<\varepsilon \tag{3}\]  
where \(n\) is the number of iterations; \(\varepsilon\) is solution accuracy.
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 1 Introduction
### Our Contribution  
In this paper, we propose an OQPSK synchronization parameter estimation algorithm based on burst signal detection in satellite communications. The main contributions of this paper are the following:  
(1) We exploit a simplified segment correlation algorithm in burst signal detection, where the sequential results of segment correlation are then efficiently used to estimate the synchronization parameters. We should note that there is no correlation between burst the signal detection and synchronization parameter estimation of traditional algorithms. To this degree, the proposed algorithm has significantly low complexity.  
(2) For frequency offset estimation, the proposed algorithm can achieve high estimation accuracy and is relatively robust to the estimation range.  
(3) For timing error estimation, the proposed algorithm is almost not affected by the roll-off factor, and it also has the ability of anti-frequency offset.  
(4) The simulation results show that the algorithm has high accuracy, and its performance is close to the Modified Cramer-Rao Bound (MCRB).  
The rest of this article is arranged as follows: Section 2 is the algorithm description; Section 3 is the performance evaluation; and Section 4 is the conclusions.
## 1 Introduction
When the contrast agent is injected into normal animals, it is distributed in the space of blood vessels and extracellular fluid, but cannot penetrate the intact blood-brain barrier (BBB). It requires metabolism through normal glomerular filtration to be discharged from the body [16; 17]. According to literature, paramagnetic contrast agent still has potential toxicity in animals; after being injected with the dose required for clinical imaging (0.2 mL/kg), it increases the risk of nephrogenic systemic fibrosis (NSF) in patients with abnormal glomerular filtration, and even increases the degree of renal injury [18]. Ferucarbotran is a superparamagnetic iron oxide magnetic nanoparticle contrast agent [19]. Although it is a relatively safe drug, it still has the possibility of causing allergic reactions after being injected at the dose required for clinical imaging (0.08 mL/kg) [20]. Based on the risk of side effects caused by contrast agents and the concern about the quality of imaging after injection, we wonder whether it is possible to guarantee the quality of imaging while reducing the risk of drug side effects under the condition of injecting a smaller amount of contrast agent, in order to improve the medication safety. This study is designed with two types of clinically used contrast agents with different properties as the experimental materials, namely OptiMARK (a paramagnetic contrast agent of Gd-DTPA-bisamide (2-oxoethyl) (Gd-DTPA-BMEA)) and Resovist (a superparamagnetic contrast agent of ferroxbotran). Based on the characteristics of different responses of T1W and T2W pulse sequences on images, in vitro experimental contrast imaging with different drug dilution ratios was carried out, and the images were visually counted [21] in order to quantitatively analyze the optimal dilution ratio as well as the contrast intensity of image signals [22]. In vivo experiments were carried out with the optimal dilution ratio analyzedthrough the in vitro experiment, and the presented images were subjected to quantitative statistical analysis by visual counting, thereby comparing the differences before and after the injections of contrast agents. Quantification of the optimal injection doses of the contrast agents and their contrast intensities of image signals was also carried out. The research results were compared with the FDA report to establish a reference basis for the medication safety of MRI contrast agent injection in experimental animals and clinical application [23].
# Measurement Grid Optimization for OTA Testing of 5G Smart Watches
## 2 Measurement Uncertainty Analysis with Grid Point Reduction
This impact is primarily reflected in deviations between TRPs obtained from different DUT orientations relative to the measurement grid. For some very coarse grids, these deviations can exceed 2 dB, which is unacceptable. This impact originates from lower grid resolution, which lacks sufficient sampling precision for accurately reconstructing the DUT's radiation patterns across various orientations. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Clenshaw–Curtis Quadrature**} \\ \hline
**i** & **0 [deg]** & **Weights** \\ \hline
0 & 0 & 0.007 \\ \hline
1 & 15 & 0.0661 \\ \hline
2 & 30 & 0.1315 \\ \hline
3 & 45 & 0.1848 \\ \hline
4 & 60 & 0.227 \\ \hline
5 & 75 & 0.2527 \\ \hline
6 & 90 & 0.262 \\ \hline
7 & 105 & 0.2527 \\ \hline
8 & 120 & 0.227 \\ \hline
9 & 135 & 0.1848 \\ \hline
10 & 150 & 0.1315 \\ \hline
11 & 165 & 0.0661 \\ \hline
12 & 180 & 0.007 \\ \hline \end{tabular}
\end{table} 
Table 1: Samples and weights for the Clenshaw–Curtis quadrature with \(\Delta\theta=15^{\circ}\). Consequently, for this impact and related MU analysis, the most realistic approach is to characterize the statistical distribution of TRP values across a large number of random DUT orientations relative to the measurement grid. Indeed, this analysis can be done in an experimental manner. However, it is required that the number of independent measurements be sufficient to achieve statistical significance. Considering the entire test time and resources required, including instruments, labor, chambers, etc., it is unrealistic for the industry to undertake such an experimental analysis on a large scale. Therefore, the relevant MU analysis proposed in this research is conducted through numerical simulations, wherein the relative orientation between the DUT (or, in other words, the reference patterns) and the measurement grid is altered randomly, and the standard deviation (STD) of TRPs for each measurement grid configuration is derived from a set of 10,000 random orientations. As a result, this STD is identified as the grid-dependent MU term, characterizing the impact of a given grid configuration on TRP calculation accuracy (or, OTA performance). The steps followed for the analyses are outlined below:  
Step 1: import the reference pattern with very fine angular resolution, i.e., \(\Delta\theta=\Delta\varphi=1^{\circ}\);  
Step 2: determine the reference TRP based on the reference pattern with very fine angular resolution;  
Step 3: apply 10,000 random rotations to the reference pattern, specifically, first rotating around the x axis, followed by the y axis and then the z axis in sequence;  
Step 4: discretize the rotated reference pattern to the specific coarse grid, for example, with an angular resolution of \(30^{\circ}/45^{\circ}\) in theta/phi;  
Step 5: calculate and store the TRP value for the coarse grid being analyzed with each rotation;  
Step 6: calculate the STD of the resulting 10,000 TRPs from the 10,000 random rotations to obtain the required MU.
## Results and Discussion
### PLRR Models Based on Full Spectra  
Three types of PLRR models were developed on the basis of full R, A, and KM spectra, respectively. A summary of the performance of 18 models established based on raw and preprocessed spectra is tabulated in Table 1. As shown, all the full-wavelength models gave acceptable performance in predicting adulteration levels of carrageenan in chicken meat. The R\({}^{2}\) and RPD of all the models were beyond 0.86 and 4.26, respectively. However, regardless of preprocessing methods, it can be observed that A-PLSR generally performed best among the three with the highest R\({}^{2}\) and RPD as well as the lowest RMSE. The R-PLSR and KM-PLSR models showed generally close performance to each other. The overall results indicated that full A spectra were the most suitable spectra in predicting carrageenan adulteration in chicken meat among the three used spectral units. As for different preprocessing methods, the best results were obtained when spectra were preprocessed with the Der1, SNV, and none for R, A, and KM spectra, respectively. Therefore, only the optimal preprocessed spectra were adopted for spectral inputs and are discussed in the subsequent analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Preprocessings**} & \multirow{2}{*}{**LVs**} & \multicolumn{3}{c}{**Calibration**} & \multicolumn{3}{c}{**Cross-validation**} & \multicolumn{2}{c}{**Prediction**} & \multirow{2}{*}{**RPD**} \\ \cline{3-3} \cline{5-10}  & & \(\mathbf{R_{c}}^{2}\) & **RMSEC** & \(\mathbf{R_{cv}}^{2}\) & **RMSECV** & \(\mathbf{R_{p}}^{2}\) & **RMSECV** & \(\mathbf{R_{p}}^{2}\) & **RMSEP** \\ \hline \multirow{4}{*}{R-PLSR} & None & 12 & 0.93 & 0.46 & 0.89 & 0.57 & 0.87 & 0.68 & 4.37 \\  & MSC & 14 & 0.94 & 0.40 & 0.90 & 0.54 & 0.86 & 0.66 & 4.51 \\  & SNV & 15 & 0.95 & 0.37 & 0.91 & 0.52 & 0.86 & 0.64 & 4.66 \\  & SNV + Detrend & 14 & 0.94 & 0.40 & 0.90 & 0.54 & 0.86 & 0.65 & 4.56 \\  & Der1 & 17 & 0.97 & 0.28 & 0.93 & 0.45 & 0.90 & 0.56 & 5.32 \\  & Der2 & 13 & 0.98 & 0.25 & 0.93 & 0.44 & 0.90 & 0.57 & 5.30 \\ \hline \multirow{4}{*}{A-PLSR} & None & 16 & 0.96 & 0.35 & 0.92 & 0.49 & 0.91 & 0.52 & 5.70 \\  & MSC & 16 & 0.97 & 0.32 & 0.93 & 0.46 & 0.92 & 0.48 & 6.17 \\  & SNV & 16 & 0.97 & 0.32 & 0.93 & 0.47 & 0.92 & 0.48 & 6.18 \\  & SNV + Detrend & 16 & 0.96 & 0.32 & 0.93 & 0.47 & 0.92 & 0.49 & 6.11 \\  & Der1 & 14 & 0.96 & 0.32 & 0.93 & 0.45 & 0.91 & 0.53 & 5.67 \\  & Der2 & 14 & 0.97 & 0.29 & 0.92 & 0.49 & 0.93 & 0.49 & 6.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of partial least squares regression (PLSR) model results based on raw and preprocessed full R, A, and KM spectra.  
Figure 2: Average (**a**) reflectance (R), (**b**) absorbance (A), and (**c**) Kubelka-Munck(KM) spectra with different adulteration levels (0–5 mL).