esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 7 Discussion  
With the continuous development of DL, there are many kinds of neural networks based on DL for radio modulation recognition. Some methods have high recognition rate, while others have low recognition rate. Of course, the recognition rate of the same network model is different due to the different hyperparameters such as the number of network layers and activation function. Below, we summarize the recognition rate of different methods based on dataset RadioML2016.10a, as shown in Table 14. From the table, we can see that DHN has the highest recognition accuracy because the model is different from the general model. In order to obtain the characteristics of modulated signals, the model is designed to extract two layer features. One is shallow information, the other is deep information. Shallow confidence corresponds to shallow features and the unemployment of shallow features is slightly larger than the sample of each symbol. Deep information corresponds to high-level features, which have global vision. In addition, the general model only takes SNR as the criterion of robustness, but the DHN model takes SNR as the weight of loss function, which can achieve stronger system robustness. The model makes full use of all data, extracts useful information from bad information and can play a greater role in the case of small datasets.  
Considering that the datasets used in some of the latest literature are different from those used in the literature analyzed in our table, we cannot compare the experimental results of all the latest literature. Only the latest literature of the dataset RadioML2016.10a can be selected for further comparison. ConvLSTMAE, a new method proposed in 2022 using the dataset RadioML2016.10a, was used for comparison. Through comparison, it is found that ConvLSTMAE can achieve higher accuracy than DHN. This is because it uses an AE as the backbone and convolutional AE and LSTM-AE are combined in parallel as temporal and spatial feature extractors. It has temperament structure and fewer parameters, which can keep high accuracy and lower down calculation costs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Structure** & **Dataset** & **Recognition Rate** \\ \hline CLDNN [67] & RadioML2016.10a & 88.5\% \\ \hline CM + CNN [32] & RadioML2016.10a & 90\% \\ \hline LSTM [57] & RadioML2016.10a & 90\% \\ \hline RNN [23] & RadioML2016.10a & 91\% \\ \hline A semi supervised approach to confrontation & & \\ training [65] & RadioML2016.10a & 91\% \\ \hline DHN [66] & RadioML2016.10a & 93\% \\ \hline ConvLSTMAE [110] & audioML2016.10a & 94.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Accuracy comparison of existing classical methods.
# Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
## 3 Combined Min-Sum Algorithm
Available online: [https://www.3 gpp.org/DynaReport/TDocExMtg.-RP-71-31637.htm](https://www.3 gpp.org/DynaReport/TDocExMtg.-RP-71-31637.htm) (accessed on 20 February 2021). * (29) [PERSON]. Quasi-cyclic Low-density Parity-check Codes from Circulant Permutation Matrices. _IEEE Trans. Inf. Theory_**2004**, _50_, 1788-1793. [CrossRef]
* (30) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On Algebraic Construction of Gallager and Circulant Low-density Parity-check Codes. _IEEE Trans. Inf. Theory_**2004**, _50_, 1269-1279. [CrossRef]
* (31) [PERSON]; [PERSON]; [PERSON]; [PERSON] Near-Shannon-limit Quasi-cyclic Low-density Parity-check Codes. _IEEE Trans. Commun._**2004**, _52_, 1038-1042. [CrossRef]
* (32) [PERSON]; [PERSON]; [PERSON]; [PERSON] Protograph-based Raptor-like LDPC Codes. _IEEE Trans. Commun._**2005**, _63_, 1522-1532. [CrossRef]
* (33) [PERSON]; [PERSON]; [PERSON] A Reduced-complexity Architecture for LDPC Layered Decoding Schemes. _IEEE Trans. Very Large Scale Integr. (VLSI) Syst._**2001**, _19_, 1099-1103. [CrossRef]
* (34) [PERSON]; [PERSON]; [PERSON] Algorithms of Finding the First Two Minimum Values and Their Hardware Implementation. _IEEE Trans. Circuits Syst. Regul. Pap._**2008**, _55_, 3430-3437. * (35) [PERSON]; [PERSON]; [PERSON]; [PERSON] A 2.37-Gb/s 284.8 mw Rate Compatible (491,3,6) LDPC-CC Decoder. _IEEE J. Solid-State Circuits_**2012**, _47_, 817-831. [CrossRef]
* (36) [PERSON]; [PERSON]; [PERSON] High-throughput Layered Decoder Implementation for Quasi-cyclic LDPC Codes. _IEEE J. Sel. Commun._**2009**, _27_, 985-994. [CrossRef]
* (37) [PERSON]; [PERSON]; [PERSON] A 528-Gb/s / LDPC Decoder with Time-domain Signal Processing for IEEE 802.15.3c Applications. _IEEE J. Solid-State Circuits_**2017**, _52_, 592-604. [CrossRef]
* (38) [PERSON]; [PERSON] A Reconfigurable LDPC Decoder Optimized for 802.11n/ac Applications. _IEEE Trans. Very Large Scale Integr. (VLSI) Syst._**2018**, _26_, 182-195. [CrossRef]
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 4 The Localization Switching Method in the Structure-Less Environment  
### Two-Dimensional (2D) LiDAR SLAM  
2D LiDAR SLAM technology uses LiDAR sensors to collect point-cloud data and scan matches. The SLAM technology then uses algorithms to optimize and loop closure  
Figure 8: The curve prediction in the ideal case and the actual case.  
detection for map building (Figure 9) and localization. However, in a structure-less environment as a long corridor, SLAM cannot determine its position on the map, leading to cause unexpected accidents easily. To avoid the mislocalization problem, we use the characteristics of 2D LiDAR data to recognize where the mobile robot is lost.  
In scan matching, SLAM will match the point clouds with the map features. If the localization is successful, the point clouds are superimposed on the black edge of the SLAM map. At this time, we can use all point clouds and the point clouds superimposed on the black edge of the map to determine whether the AGV is mislocalized as follows:  
\[m_{r}=1-\frac{p_{match}}{p_{all}} \tag{11}\]  
where \(m_{r}\) represents the missing rate (ranged from 0 to 1). If \(m_{r}\) is greater than 50%, most of the point clouds are not superimposed on the map features. In this case, it can be judged that the mobile robot is getting lost; otherwise, it represents localization success. \(p_{all}\) represents the number of all point clouds in a frame, and \(p_{match}\) represents the number of point clouds in a frame superimposed on the map features.  
However, this judgment method cannot detect the localization status under all conditions. when the surrounding environment has s repetitive pattern, the map features will be too consistent despite the point-cloud is superimposed on the map features. This makes SLAM localization impossible to confirm. For example, in the corridor part of the map (Figure 9), the point clouds extracted from the walls on both sides are too sparse, making it impossible to determine where the mobile robot is in the corridor.
## 5 Implementation of the Flow Rate Control System
### State Estimation in an Automatic Pouring Machine  
A Kalman filter was applied to estimate the state variables, such as the liquid height at the pouring mouth, the angular velocity of the tilting ladle, and the acceleration of transference on the \(Z\)-axis, as depicted in Figure 6. In this study, the state estimation approach is decomposed into two steady-state Kalman filters and one extended Kalman filter, for simple implementation of the state estimation approach in the automatic pouring machine.  
The two steady-state Kalman filters are applied for estimating the state variables in the drive systems of the \(\Theta\)- and \(Z\)-axes, respectively. The plant model for estimating the state variables of the drive system on the \(\Theta\)-axis is denoted as follows:  
\[\left[\begin{array}{c}\dot{\theta}\\ \dot{\omega}\end{array}\right] = \left[\begin{array}{cc}0&1\\ 0&-\frac{1}{t_{t}}\end{array}\right]\left[\begin{array}{c}\theta\\ \omega\end{array}\right]+\left[\begin{array}{c}0\\ \frac{K_{t}}{T_{t}}\end{array}\right]u_{t}, \tag{25}\] \[y = \left[\begin{array}{cc}1&0\end{array}\right]\left[\begin{array} []{c}\theta\\ \omega\end{array}\right]. \tag{26}\]  
The plant model on the \(Z\)-axis is formed in the same way as presented in Equations (25) and (26). Furthermore, in this study, the respective covariance matrices \(Q_{t}\) and \(R_{t}\) of the system noise and the measurement noise in the drive system of the \(\Theta\)-axis are given as follows:  
\[Q_{t} = \text{diag}(1\times 10^{-15},1\times 10^{-1}), \tag{27}\] \[R_{t} = 1\times 10^{-5}. \tag{28}\]  
The respective covariance matrices \(Q_{z}\) and \(R_{z}\) of the system noise and the measurement noise in the drive system of the \(Z\)-axis are also given as follows:  
\[Q_{z} = \text{diag}(1\times 10^{-3},1\times 10^{-3}), \tag{29}\] \[R_{z} = 1\times 10^{-7}. \tag{30}\]  
Further, the acceleration along the \(Z\)-axis can be estimated by estimating the state variables in the drive system of the \(Z\)-axis as  
\[\bar{a}_{z}=-\frac{1}{T_{z}}\bar{v}_{z}+\frac{K_{z}}{T_{z}}u_{z}, \tag{31}\]  
where the variables with a bar represent the estimated variables.  
In the design of the extended Kalman filter for estimating the state variables in the pouring process, the plant model can be denoted as follows:  
\[\left[\begin{array}{c}h\\ \dot{w}_{o}\\ \dot{w}_{l}\end{array}\right] = \left[\begin{array}{c}-\frac{q(h)}{A(\theta)}-\frac{1}{A(\theta )}\left(\frac{\partial A(\theta)}{\partial\theta}+\frac{\partial V_{S}(\theta )}{\partial\theta}\right)\tilde{\omega}\\ \rho q(h)\\ -\frac{1}{T_{l}}w_{l}  
In this study, the respective covariance matrices \(Q_{f}\) and \(R_{f}\) of the system noise and the measurement noise in the pouring process are given as follows:  
\[Q_{f}=\text{diag}(2.5\times 10^{-10},1\times 10^{-11},1\times 10^{-11}), \tag{34}\] \[R_{f}=5\times 10^{-4}. \tag{35}\]
## 3 Results
### Effect of Other Ions  
In order to test the selectivity of the method, the effect of other ions on the analyte ion to be analyzed in the solution environment was examined. After adding metal ions at different concentrations to 75 mL solutions containing 5 \(\mu\)g L\({}^{-1}\) mercury ions to be analyzed, the solidified organic drop micro-extraction method was applied and the effect of other ions was examined. The acceptable limit is defined as the added ion concentration that causes a relative error of less than \(\pm\)5%. As can be seen in Table 3, the most serious inhibition was observed in the lead ion, followed by copper and chromium ions. According to [PERSON], extraction of Hg(II) from a strong acid medium enables one to separate Hg from Cu, Bi, Zn, Ni, Pb, and other metals easily [21]. When dealing with wastewater containing high amounts of these interfering ions, as can be seen in Figure 2 in the pH scan graph, the effect of these ions can be avoided by sacrificing a little bit of the absorbance value (enhancement factor) by working at a pH lower than 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ion** & **[Hg(II)]/[Ion]** & **Added As** & **Ion** & **[Hg(II)]/[Ion]** & **Added As** \\ \hline K\({}^{+}\) & \textgreater{}1/5000 & KCl & Al\({}^{3+}\) & \textgreater{}1/100 & Al(NO\({}_{3}\))\textgreater{}9\textgreater{}2\(\textgreater{}\)O \\ \hline Na\({}^{+}\) & \textgreater{}1/100 & NaCl & Fe\({}^{3+}\) & 1/100 & Fe(NO\({}_{3}\))\textgreater{}9\textgreater{}2\(\textgreater{}\)O \\ \hline Zn\({}^{2+}\) & 1/100 & Zn(NO\({}_{3}\))\textgreater{}6\textgreater{}2\(\textgreater{}\)O & Cr\({}^{3+}\) & \textgreater{}1/25 & Cr(NO\({}_{3}\))\textgreater{}9\textgreater{}2\(\textgreater{}\)O \\ \hline Ca\({}^{2+}\) & \textgreater{}1/100 & CaCO\({}_{3}\) & Co\({}^{2+}\) & 1/500 & Co(NO\({}_{3}\))\textgreater{}2\(\textgreater{}\) \\ \hline Cd\({}^{2+}\) & 1/100 & Cd(NO\({}_{3}\))\textgreater{}2\(\textgreater{}\)4\(\textgreater{}\)H\({}_{2}\)O & As\({}^{3+}\) & 1/1000 & As\({}_{2}\)O\({}_{3}\) \\ \hline Pb\({}^{2+}\) & \textgreater{}1/10 & Pb(NO\({}_{3}\))\textgreater{}2 & SCN\({}^{-}\) & 1/1000 & KSCN \\ \hline Mn\({}^{2+}\) & \textgreater{}1/1000 & MnCl\({}_{2}\) & Cl\({}^{-}\) & \textgreater{}1/5000 & NaCl \\ \hline Ni\({}^{2+}\) & 1/100 & (NiNO\({}_{3}\))\textgreater{}6\(\textgreater{}\)2\(\textgreater{}\)O & CO\({}_{3}\)\({}^{2-}\) & 1/100 & Na\({}_{2}\)CO\({}_{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tolerance limits (error < 5%) of diverse ions on the determination of 5 \(\mu\)g L\({}^{-1}\) Hg(II) by the proposed method.  
Figure 9: Effect of final volume.
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 3 Technical Background
### Overview of Quantized Neural Networks (QNNs)  
Quantization is recognized as one of the superior schemes in terms of fulfilling excessive memory requirements and computational burden within the DL community. It is well known that 32-bit floating-point data types are adopted in DL algorithms to represent weights. Quantization executes the weights in the form of low-bit width values in place of floating-point values in a more compact way. The term of QNNs is referred to as a special type of CNN, with a growing history of remarkable achievements [22]. One advantage of QNNs is their ability to speed up network operation and enhance energy efficiency, as quantized weights require less computations. A prevalent trade-off related to QNN is the possibility of losing accuracy due to the incomplete representation of information. This accuracy degradation should be explicitly kept at a minimal level. In the literature, two main methods of quantization exist: post-training quantization (PTQ) and QAT.  
PTQ maintains a lightweight running process that quantizes a pre-trained FP32 network directly, with no need for labeled data. Due to the advantages of lightening the long duration of re-training and its low cost, PTQ receives a lot of research interest. However, it is susceptible to quantization errors on parameters in cases of intensely low-bit settings. On the other hand, QAT is reported to offer better quantization than PTQ by applying quantization-aware operations in training phase. Of course, this operation consumes extra resources in terms of time and parameter selection. These two classes of quantization methods are depicted in Figure 4.
# Model Predictive Control Algorithm for Converter Based on a Convolutional Neural Network
## 1 Introduction
In [26], an intelligent predictive multi-objective controller based on a dual-ANN architecture is designed to dynamically adjust weighting factors and power reference values, thereby enhancing MPC's multi-objective collaborative optimization capability. For FCSMPC algorithms, the introduction of neural networks has significantly improved control performance [27; 28; 29; 30]. In [31], a back propagation (BP) neural network is employed to calculate prediction errors for compensating prediction errors in FCSMPC algorithms. In [32; 33], ANNs are utilized to enable multi-step prediction computation of FCSMPC algorithms. To address the problem of calculating the multi-objective weighting factors (WFs) in cost functions, [PERSON]. [34; 35; 36; 37] developed neural network-based adaptive methods that optimize WFs in real time, which solves the problem of conventional empirical tuning methods being sensitive to working points. The above analysis demonstrates that the neural network can significantly reduce computational complexity while maintaining high-precision control performance. However, existing neural network-based FCSMPC algorithms exhibit critical limitations, including reliance on datasets and limited generalization capability. When the working states of power converters vary, sustaining higher control performance necessitates timely updates to neural network computational model parameters. Therefore, it is important to design appropriate switching rules for neural network controllers in response to varying load types and to rapidly identify the type of converter load. To address these challenges, this article proposes a converter load-type identification method based on a convolutional neural network (CNN), using principal component analysis (PCA) for dimension reduction and CNN-based computation to classify four different converter load types; then, using the identified load type as a signal to switch the neural network, thereby improving the adaptability and reliability of the neural network-based FCSMPC algorithm. The rest of this article is organized as follows: Section 2 presents the neural network controllers for converters and the model switching strategy. Section 3 introduces the load-type identification method based on a CNN. Section 4 validates the effectiveness of the proposed approach through simulation experiments. The conclusions are provided in Section 5.
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 3 Results
### Entropy Analysis (Physical Fatigue)  
Physical fatigue, understood as the inability to sustain performance over time, can be detected using the entropy analysis of HR signals, as proposed by [PERSON] et al. (2020) [PERSON] et al. (2020). The authors used data collected by other authors who had measured the HR of eight workers wearing a chest strap. As previously observed, nonlinear entropy analysis can provide more details regarding the variability of HR [35; 36]. The aim of the study was to develop a novel analysis method capable of predicting physical fatigue in real time using HR as the only indicator. For tiredness detection (the rating of perceived exertion (RPE) [49] was used to label the data), the suggested approach demonstrated accuracy, sensitivity and specificity rates of 90.36%, 82.26% and 96.2%, respectively, minimizing work accidents and injuries due to fatigue.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 3 Proposed Methods
### Multiscale Fusion  
The combined weight \(\hat{W}_{k}\) consists of a well-exposure weight and a chrominance weight and is defined as follows:  
\[\hat{W}_{k}=\frac{W_{e}^{k}\times W_{c}^{k}}{\sum_{k=1}^{N}W_{e}^{k}\times W_{c} ^{k}+\varepsilon} \tag{18}\]  
where \(\varepsilon\) is a tiny number to avoid the situation where \(W_{e}^{k}\) and \(W_{c}^{k}\) are both zero. Then use a multi-scale fusion strategy to fuse the Gaussian pyramid \(G\left\{\hat{W}_{k}(x,y)\right\}^{d}\) of the synthetic weight map and the Laplacian pyramid \(L\{Y_{k}(x,y)\}^{d}\) of the input image.  
\[L\{F\}_{First}^{d}=G\left\{\hat{W}_{k}(x,y)\right\}^{d}L\{Y_{k}(x,y)\}^{d} \tag{19}\]  
where \(L\{F\}_{First}^{d}\) represents the initial fusion Laplacian pyramid. To enhance the details of the DoE and BoE regions, we updated the Laplace coefficient of \(\left\{O_{OEI}^{DoE}(x,y)\right\}_{Max}^{d}\) and \(L\{O_{LI}^{BoE}(x,y)\}_{Max}^{d}\) to the corresponding DoE and BoE regions of \(L\{F\}_{First}^{d}\). Finally, the final fusion image \(F\) is generated by the inverse Laplace transform. Algorithm 1 shows the detailed calculation process.  
```
Parameter:\(\hat{Y}_{k}^{gray}\) means normalized to the grayscale image of \(Y_{k}\), \(a\) represents interpolation parameter, \(\hat{l}(x,y)\) represents average brightness Input: Source image sequences \(Y_{k}\),\(1\leq k\leq N\) Output: The result \(F\) after fusion
1:for each image \(Y_{k}\)do
2:\(\hat{Y}_{k}^{gray}\):=pgB2 gray (\(Y_{k}\))
3: Calculate \(W_{e}^{k}\) by the Equations (8) and (9)
4: Calculate \(W_{e}^{k}\) by the Equation (10)
5: obtain \(L\left\{Y_{k}(x,y)\right\}^{d}\) by computing the Laplace Pyramid of \(Y_{k}\)
6:endfor
7:for each \(W_{e}^{k}\) and \(W_{e}^{k}\)do
8: Calculate \(\hat{W}_{k}\) by the Equation (18)
9:endfor
10: Compute the Gaussian pyramid of \(\hat{W}_{k}\)
11: Compute \(\hat{l}(x,y)\) by the Equation (4)
12: Use \(\hat{l}(x,y)\) to determine DoE and BoE
13: Use K-Means to divide \(Y_{k}\) into OEI and LEI
14: Calculate the Gaussian pyramid \(g=G(x,y)\) of OEI and LEI
15:for each \(\sigma_{i}\) of \(g\) in DoE and BoE do
16: Calculate \(L\left\{R\left(\hat{Y}_{k}^{DoE,BoE}(x,y)\right)_{\sigma_{j}}\right\}^{d}\) by the Equation (13)
17: Calculate interpolation parameter \(a\) by using \(g=(1-a)\sigma_{j}+a\sigma_{j+1}\)
18: Calculate the corresponding pyramid coefficients in DoE and BoE by using Equations (14) and (15)
19:endfor
20: Calculate \(L\left\{O_{OEI}^{DoE}(x,y)\right\}_{Max}^{d}\) and \(L\left\{O_{LI}^{BoE}(x,y)\right\}_{Max}^{d}\) by using Equations (16) and (17)
21: Calculate \(L\{F\}_{First}^{d}\) by the Equation (19)
22: Update \(L\{F\}_{First}^{d}\) with \(L\left\{O_{OEI}^{DoE}(x,y)\right\}_{Max}^{d}\) and \(L\left\{O_{LI}^{BoE}(x,y)\right\}_{Max}^{d}\)
23: Use Laplacian Pyramid reconstruction to obtain fused image \(F\)
```  
**Algorithm 1**The proposed algorithm
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 3 Results
### Trend Detection Model Comparison
#### 3.1.1 Evaluation Based on Synthetic Data  
Figure 7 shows the ROC curve derived from the synthetic data set 1 and the model parameters described in Table A1. Ideally, the plots would show a dot in the upper left corner for a model. Such a dot would refer to a perfect classifier. This means that the model has a TPR of 1 and FPR of 0. Such a model would detect all trends and trigger no false alarms. The LR model and the MA model achieve these perfect classification results. The variation of parameters of the CS model does not influence the model performance and the MK model shows high TPR values only at the expense of an increased false positive rate. The results of synthetic data set 2 with the same model parameters are shown in Figure 8. Here, the CS model shows the best performance as a parameter combination exists where no false alarms are triggered and all trends are detected. It is followed by the MK model, which also yields a performance where all trends are detected and the FPR is small. The LR and the MA models achieve high TPR values only at the expense of increased FPR. The AUC values of the models for both data sets are presented in Table A2. Based on these results, it was decided to apply the CS and the MK model to the accelerated wear tests as they performed best on the more difficult data (synthetic data set 2) and based on their average AUC values.  
Figure 8: Trend detection model comparison based on the synthetic data set 2.  
Figure 7: Trend detection model comparison based on the synthetic data set 1.
## 3 Results
### Feasibility for Creating Prescription Maps
(**a**) Sentinel-2A prescription map based on the spectral model; (**b**) Sentinel-2A prescription map based on the texture model; (**c**) Sentinel-2A prescription map based on the spectral-texture model. the treatment category based on the spectral model was the highest for Fields 6-8. From Figure 9c, the UA for the treatment category based on the spectral model was the highest for Fields 1, 3-5, and 7, while the UA for the treatment category based on the spectral-texture model was the highest for Fields 2, 6, and 8. This result indicates that it is feasible to create prescription maps using different features of Sentinel-2A images. The spectral-texture model has certain advantages for cotton fields with low or moderate infestation, while the spectral model is more suitable for severely infested cotton fields. Figure 9: Comparison of classification accuracy of prescription maps generated from Sentinel images by three binary logistic regression (BLR) models for Fields 1–8: (**a**) overall accuracy (OA); (**b**) producer’s accuracy (PA) for the treatment category; (**c**) user’s accuracy (UA) for the treatment category.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 1 Introduction
Finally, the impact of a partial or total lack of drag control and of a coarse attitude control on the measurement error of the non-gravitational acceleration is provided with the consequent implications on the gravity field retrieval.