esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the continuous development of artificial intelligence has largely been driven by algorithms and computing power. This paper mainly discusses the training and inference methods of artificial intelligence from the perspective of computing power. To address the issue of computing power, it is necessary to consider performance, cost, power consumption, flexibility, and robustness comprehensively. At present, the training of artificial intelligence models mostly are based on GPU platforms. Although GPUs offer high computing performance, their power consumption and cost are relatively high. It is not suitable to use GPUs as the implementation platform in certain application scenarios with demanding power consumption and cost. The emergence of high-performance heterogeneous architecture devices provides a new path for the integration of artificial intelligence training and inference. Typically, in Xilinx and Intel's multi-core heterogeneous architecture, multiple high-performance processors and FPGAs are integrated into a single chip. When compared with the current separate training and inference method, heterogeneous architectures leverage a single chip to realize the integration of AI training and inference, providing a good balance of training and inference of different targets, further reducing the cost of training and implementation of AI inference and power consumption, so as to achieve the lightweight goals of computation, and to improve the flexibility and robustness of the system. In this paper, based on the LeNet-5 network structure, we first introduced the process of network training using a multi-core CPU in Xilinx's latest multi-core heterogeneous architecture device, MPSoC. Then, the method of converting the network model into hardware logic implementation was studied, and the model parameters were transferred from the processing system of the device to the hardware accelerator structure, composed of programmable logic through the bus interface AXI provided on the chip. Finally, the integrated implementation method was tested and verified in Xilinx MPSoC. According to the test results, the recognition accuracy of this lightweight deployment scheme on MNIST dataset and CIFAR-10 dataset reached 99.5 and 75.4% respectively, while the average processing time of the single frame was only 2.2 ms. In addition, the power consumption of the network within the SoC hardware accelerator is only 1.363 W at 100 MHz.  
deep learning; MPSoC; FPGA; CNN +
Footnote †: journal: Applied Sciences
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 4 Simulation Results
### System Performance versus Maximum Doppler Frequency  
In this subsection, we evaluate the influence of the maximum Doppler frequency \(f_{D}\) on the proposed deep learning models. As shown in Figure 16, when the maximum Doppler frequency increased, the performance of the deep learning models decreased. This can be explained by the fact that the channel varied faster as the Doppler frequency increased. From the figure, we also see that the performance of bi-LSTM model decreased more severely compared to the others. However, its performance was still significantly better than that of FDNN and CNN models.  
In practice, to investigate the sensitivity of the neural networks, we considered a scenario in which the Doppler frequency \(f_{D}\) varied constantly due to the change of the receiver's velocity. Alternatively, we evaluated the prediction accuracy of the proposed models when there was a mismatch of the Doppler frequency between the training stage and testing stage. In this simulation, we kept the value fo \(f_{D}=100\) Hz in the training stage, while the value of \(f_{D}\) in the testing stage was randomly distributed from a uniform distribution. The result is illustrated in Figure 17. As seen from the figure, all the deep-learning channel estimation models were robust to the mismatch of the Doppler frequency. Only the performance of the bi-LSTM model decreased slightly when \(\mathrm{SNR}=20\) dB. The performance of the DNN models in the different cases was also compared to the LS and LMMSE estimation methods when \(f_{D}=100\) Hz. All the deep learning-based channel estimation models outperformed the conventional models, even in the case of channel mismatching. From the observations, we conclude that the bi-LSTM model is more  
Figure 14: The BER of the channel estimate vs. the SNR level with \(f_{D}=200\) Hz.  
Figure 13: The BER of the channel estimate vs. the SNR level with \(f_{D}=36\) Hz.  
sensitive to the Doppler frequency compared to the FDNN and CNN models. The reason is that the bi-LSTM model exploits the time-varying properties of channels; thus, the Doppler frequency has more serious effects on the bi-LSTM model. However, the three proposed models are still robust to the changes of the Doppler frequency and therefore more efficient than the conventional methods.
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM)  
After decalcification, samples were subjected to a systematic dehydration process for scanning electron microscopy (SEM) analysis. This involved sequential immersion in ethanol solutions with increasing concentrations of 30, 50, and 80, reaching 100%. Once fully dehydrated, the samples were sputter-coated with a fine layer of gold to improve electronic conductivity, making them ready for optimal imaging. High-resolution images of these samples were acquired using a Zeiss scanning electron microscope (Munich, Germany) at an acceleration voltage of 15 kV.
## 3 Results and Discussion
### Interference  
Next, we investigated the interference effects of AA and UA. Figure 4A shows CV curves of solutions containing 100 uM of AA, UA, DA, and a mixture of the three. No significant peaks were observed in the presence of AA or UA. Furthermore, the CV curves for DA were nearly identical in the presence and absence of AA. CV curves of chitosan/rGO/SPCE electrodes were examined to verify the role of tyrosinase. As shown in Figure S5, the oxidation peak for detection in the AA solution was significantly higher than that for detection in the DA solution in the presence of AA and DA. The AA concentration in the biospecimen was generally far higher (-100-500 uM) than that of DA; therefore, we increased AA's concentration 10-fold in the presence of various DA concentrations. The peak oxidation current levels in samples with high AA concentrations (500 uM) and DA were nearly identical to those obtained from samples containing DA alone (Figure 4B). We examined the electrodes' stability by measuring the response to 50 uM DA in the presence of 0.5 mM AA. As shown in Figure S6, the peak oxidation current levels obtained from the electrodes were essentially unchanged after being stored at 4 \({}^{\circ}\)C for 28 days.
# Spatial Performance Indicators for Traffic Flow Prediction
## 3 Materials and Methods
### Spatial KPIs
#### 3.1.2 Spatial Clustering of High/Low Values (Getis-Ord General G)  
The Getis-Ord General G statistic [17] is a statistical measure that can be used to infer the presence of spatial clustering of high/low node value in the network graph. The null hypothesis associated with this statistic assumes no spatial clustering. The Getis-Ord General G statistic provides an overall assessment of the spatial clustering.  
Getis-Ord General G provides a simple and straightforward way to assess the degree of spatial clustering in an entire network [26]. Getis-Ord General G has seen some applications in traffic management systems such as hotspot analysis in traffic safety management [27] and high-risk area identification for traffic crashes [28]. These applications highlight the statistic's effectiveness in identifying spatial clusters. Getis-Ord General G is calculated using Equation (6).  
\[G=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{i,j}x_{i}\,x_{j}}{\sum_{i=1}^{n}\sum_{j =1}^{n}x_{i}\,x_{j}},\forall j\
eq i \tag{6}\]  
The Getis-Ord General G statistic is a measure used to assess the spatial clustering of feature values. It calculates the \(z_{G}\)-score by considering the number of features, the feature values in each location, and the elements of the spatial weights matrix. The adjacency matrix cell values are typically between 0 and 1, which ensures that the resulting range of the Getis-Ord General G statistic is also between 0 and 1.  
\[z_{G}=\frac{G-\mathrm{E}[G]}{\sqrt{V[G]}} \tag{7}\]  
\[E[G]=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{i,j}}{n(n-1)},\forall j\
eq i \tag{8}\]  
\[V[G]=E[G^{2}]-E[G]^{2} \tag{9}\]  
\[p_{G}=1-\mathrm{CDF}(z_{G}) \tag{10}\]  
In the equation above, \(E[G]\) represents the expectation of \(G\), \(V[G]\) represents the variance of \(G\), and \(\mathrm{CDF}(z_{G})\) represents the cumulative distribution function of \(z_{G}\)-score. When the obtained \(p_{G}\)-value (derived from the \(z_{G}\)-score) of the Getis-Ord General G statistic is deemed statistically significant, we can reject the null hypothesis. A positive \(z_{G}\)-score indicates that high-value nodes in the network are clustered together in proximity (hotspots). Conversely, a negative \(z_{G}\)-score signifies that low-value nodes in the network are spatially clustered (coldspots).  
Understanding the spatial clustering tendency of a traffic prediction method is an important task. A sporadic high-value node can be caused by an event or sensor fault. However, a spatial clustering of high-value nodes suggests a systematic problem. With Getis-Ord General G, we can infer the tendency of hotspots or coldspots in the traffic prediction error distribution.
## 2 Related Work
### Contributions  
Most of the presented solutions in the existing literature mostly considered a conventional cellular network scenario where the challenges are different compared to an emergency communication scenario. Thus, their presented solutions are not valid for emergency communication scenarios without proper modification. Motivated by this, in this paper, we propose a prioritized user association solution that adopts a Q-learning algorithm and prioritize the emergency users by using the proposed reward function, that in turn significantly enhances the sum rate and reduces the outage. Moreover, we also compare the impact of varying the number of deployed ABSs on the system sum rate and outage probability.
## 3 Flight Control
### Altitude Controller  
The altitude controller is similar to the attitude controller and is designed according to the dynamic model as shown in Equation (1).  
\[\left\{\begin{array}{l}\dot{z}_{1}=z_{2}\\ m\dot{z}_{2}=F_{zT}-mg\end{array}\right. \tag{24}\]  
where \(z_{1}\) and \(z_{2}\) are the altitude and vertical velocity of the FMAV, respectively. Different from attitude control, the altitude controller chooses the vehicle mass estimator \(\hat{m}\) as an adaptive parameter to account for misalignment between the theoretical and practical thrust model or the change in the vehicle's weight. The difference between the mass estimator \(\hat{m}\) and the practical mass \(m\) is defined as the adaptive parameter error \(\hat{m}=m-\hat{m}\). Similar to the attitude controller design, the altitude error \(\delta_{z1}\) and the vertical velocity error \(\delta_{z2}\) associated with the altitude dynamics of the FMAV is defined by:  
\[\begin{array}{l}\delta_{z1}=z_{1d}-z_{1}\\ \delta_{z2}=z_{2d}-z_{2}\end{array} \tag{25}\]  
where, \(z_{1d}\) and \(z_{2d}\) are the desired altitude and vertical velocity of the FMAV, respectively. Then, we define the Lyapunov function \(V_{z1}\), \(V_{z2}\) as \[\left\{\begin{array}{l}V_{z1}=\frac{1}{2}K_{1}\delta_{z1}^{2}\\ V_{z2}=\frac{1}{2}K_{1}\delta_{z1}^{2}+\frac{1}{2}m\delta_{z2}^{2}+\frac{1}{2} \Gamma_{z}\vec{m}^{2}\end{array}\right. \tag{26}\]  
So we can get the time derivative of the Lyapunov function \(\dot{V}_{z1}\), \(\dot{V}_{z2}\) as follow:  
\[\left\{\begin{array}{l}\dot{V}_{z1}=K_{1}\delta_{z1}\dot{\delta}_{z1}=K_{1} \delta_{z1}(\dot{z}_{1d}-z_{2})\\ \dot{V}_{z2}=\dot{V}_{z1}+m\delta_{z2}\delta_{z2}+K_{3}\hat{m}\hat{m}\\ =\dot{V}_{z1}+\delta_{z2}(m\dot{z}_{2d}-(F_{zT}-mg))+\Gamma_{z}\hat{m}\hat{m} \end{array}\right. \tag{27}\]  
We propose the control law as  
\[\begin{array}{l}z_{2d}=\dot{z}_{1d}+K_{2}\delta_{z1}\\ F_{zT}=K_{1}\delta_{z1}+\hat{m}\dot{z}_{2d}+\hat{m}\hat{g}+K_{3}\delta_{z2} \end{array} \tag{28}\]  
As a result, Equation (27) becomes  
\[\left\{\begin{array}{l}\dot{V}_{z1}=K_{1}\delta_{z1}(-K_{2}\delta_{z1}+ \delta_{z2})\\ \dot{V}_{z2}=-K_{1}K_{2}\delta_{z1}^{2}-K_{3}\delta_{z2}^{2}+\delta_{z2}\hat{m }(\dot{z}_{2d}+g)+\Gamma_{z}\hat{m}\hat{m}\end{array}\right. \tag{29}\]  
To make the system asymptotically stable, we derive the adaptive law as:  
\[\dot{\hat{m}}=-\Gamma_{z}^{-1}\delta_{z2}(\dot{z}_{2d}+g) \tag{30}\]  
In this way, Equation (29) can be rewritten as  
\[\dot{V}_{z2}=-K_{1}K_{2}\delta_{z1}^{2}-K_{3}\delta_{z2}^{2}\leq 0 \tag{31}\]  
which renders the derivative of the Lyapunov function \(V_{z1}\) and \(V_{z2}\) to be negative define. According to the Invariant set theorem, the altitude control will be asymptotically stable. the height error \(\delta_{z1}\) will converge to 0. That is, the altitude state of the vehicle can follow the altitude command.
# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 3 Methodology
### Clustering-Based Pseudo-Labeling
#### 3.1.3 Consistency Training  
Since the k-means clustering algorithm is susceptible to noise points and outliers, there may be mislabeling in the pseudo-labeled dataset, and directly accessing the pseudo-labeled dataset for subsequent training will introduce too much noise. Therefore, the consistency training of the unlabeled data-enhanced samples is added. The forced model of the consistency loss function produces similar outputs for similar inputs, which contributes to the robustness and generalization performance of STCPC.  
For a large number of unlabeled dataset \(X_{u}\), the output distribution of pseudo-label generation by clustering is denoted as \(p_{\bar{g}}(y|x)\), indicating that the pseudo-label \(y\) of the unlabeled dataset is obtained from its unlabeled data \(x\) by transformation; \(\hat{X}_{u}\) is the dataset generated from the unlabeled dataset \(X_{u}\) using back translation method data augmentation, and its output distribution by clustering task is denoted as \(p_{\theta}(y|\hat{x})\). Considering the consistency constraint between the unlabeled data and its augmented samples to minimize the metric between the two distributions, the pseudo-label consistency loss is  
\[\text{min}L_{U}(\theta)=E_{x\in X_{u}}E_{\hat{x}\sim q(x|x)}\big{[}D_{KL}\big{(} p_{\bar{\theta}}(y|x)\big{|}p_{\theta}(y|\hat{x})\big{)}\big{]}, \tag{6}\]  
where \(\theta\) is a model parameter, \(\overset{\sim}{\theta}\) is a fixed replica of \(\theta\) that is not involved in backpropagation. \(q(\hat{x}|x)\) is the data augmentation function, and the distance between two distributions is measured using Kullback-Leibler (KL) Divergence. The consistency training technique forces the model to be insensitive to noise by aligning the clustering results of the unlabeled data with the clustering results of its augmented samples, resulting in smoother changes in the input space. Moreover, minimizing the consistency loss will achieve full utilization of a large amount of unlabeled data.
## 2 Materials and Methods
### UPLC-MS/MS Detection of the Nonvolatile Compounds
#### 2.4.2 UPLC-MS/MS Detection  
The chromatography was performed on ACQUITY UPLC(r) HSS T3 (2.1 \(\times\) 100 mm, 1.8 \(\upmu\)m) (Waters, Milford, MA, USA) column with a flow rate of 0.3 mL/min at a column temperature of 40 \({}^{\circ}\)C. The sample size was 2 \(\upmu\)L. Each sample was analyzed separately in positive ion mode and negative ion mode to ensure optimal data quality. In the positive ion mode, the mobile phase consisted of 0.1% acetonitrile formate (B\({}_{2}\)) and 0.1% formic acid water (A\({}_{2}\)), and the gradient elution procedure was 0-1 min 8% B\({}_{2}\), 1-8 min 8-98% B\({}_{2}\), 8-10 min 98% B\({}_{2}\), 10-10.1 min 98-8% B\({}_{2}\), and 10.1-12 min 8% B\({}_{2}\). In the negative ion mode, the mobile phase consisted of acetonitrile (B\({}_{3}\)) and 5 mM ammonium formate water (A\({}_{3}\)), and the gradient elution procedure was 8% B\({}_{3}\) for 0-1 min, 1-8 min 8-98% B\({}_{3}\), 8-10 min 98% B\({}_{3}\), 10-10.1 min 98-8% B\({}_{3}\), and 10.1-12 min 8% B\({}_{3}\)[23]. An Orbitrap Exploris 120 mass spectrometry detector (Thermo Fisher Scientific, Waltham, MA, USA) and electrospray ion source ESI positive and negative ion mode were used to collect data, respectively. The positive (+) and negative (\(-\)) ESI modes parameters were as follows: ion spray voltage 3.50 kV (+) and 2.50 kV (\(-\)), sheath gas 40 arb and auxiliary gas 10 arb, capillary temperature 325 \({}^{\circ}\)C. The first-level (I) and second-level (II) modes parameters were as follows: full scanning resolution 60,000 (I) and 15,000 (II), automatic gain control (AGC) value 1 \(\times\) 10\({}^{6}\) (I) and 5 \(\times\) 10\({}^{4}\) (II), acquisition rates 12 Hz (I) and 30 Hz (II), the maximum injection times 50 ms (I) and 22 ms (II), ion scanning range (m/z) 100-1000 (I), collision energy of HCD 30% (II). Simultaneous dynamic exclusion was used to remove unnecessary MS/MS information [24]. Data correction was achieved based on the LOESS signal-correction method using pooled QC samples (a mixture of equal volumes from all samples) to eliminate systematic errors. In data quality control, compounds with RSD > 30% in QC samples were filtered out.
# Bridge Crack Detection Based on SSENets
## 4 Conclusions  
In this paper, an image classification model SSENets for crack detection is proposed, which is mainly composed of the SSE module using the skip-connection strategy and the ASPP module using the atrous convolution with multi-sample rates. By applying the channel weights generated by shallow feature map to the deeper feature map, SSE module establishes the gradient connection between the shallow network and deeper network. It will alleviate the vanishing gradient during the network training, increase the gradient correlation, and enhance the transmission ability of the model. In view of the crack detection task, we introduce the ASPP module to capture multi-scale features from crack images, thereby improving the accuracy of crack detection. The proposed model can achieve a detection accuracy of 97.77%, which performs better than the comparison models.  
Furthermore, the SSE module can be embedded in any convolutional neural network to improve performance. In future work, we will apply SSE module to pixel-level crack detection. Given the computational complexity of this task, we hope that the SSE module will reduce the model parameters while improving the detection accuracy.  
Conceptualization, H.L. and X.T.; methodology, H.L.; software, H.L. and H.X.; validation, H.L. and Y.W.; formal analysis, H.C. and H.X.; writing--original draft preparation, H.L.; writing--review and editing, Y.W. and X.T.; supervision, H.C. and X.C.; project administration, X.C. and K.C.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Tianjin Municipal Transportation Commission Science and Technology Development Plan Project (2019C-05). The authors declare no conflict of interest.
## 2 Systems Overview
### Power Supply Technologies  
The power supply technologies used in the aforesaid devices can be classified according to the applications of the implanted devices. Generally, the implanted devices can be powered by batteries such as in the case of pacemakers, cochlear implants, retinal prosthesis, brain implants, _etc._ However, the limited lifetime of the implanted batteries lead to many challenges to improve their longevity. The wireless telemetry bio-devices can also be powered by radio frequency (RF) signals, transmitting power from the external part to the internal part through an inductive coupling coil and then converted into AC and DC voltage [16]. Another source of harvested energy is used to power the implanted devices such as knee implants that use the vibration of piezoelectric materials [17] or body motion [18].
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 3 Results
### Analysis of Ascorbate Levels in Transgenic Lines  
#### 3.2.1 Analysis of Ascorbate Levels in Transgenic Tomato Fruits  
The determination results indicate that the overexpression of _RrGGP2_ and _RrDHAR_ genes substantially augmented the levels of AsA and DHA in tomato fruits (Figure 3A,B). The content of AsA increased significantly from 209.7% to 384.3% in all the four tomato lines that overexpressed _RrGGP2_, and there was an increase in DHA that ranged from 69.2% to 309.0% (Figure 3A). Similarly, in the four tomato lines overexpressing _RrDHAR_, AsA content was increased by 175.4% to 213.6%, and DHA content was increased from 116.8% to 261.2% (Figure 3B). On average, the overexpression of _RrGGP2_ in the tomato lines resulted in an increase of 294.3% for AsA and 190.8% for DHA, compared to an increase of 179.9% for AsA and 182.6% for DHA in the tomato lines overexpressing _RrDHAR_ (Figure 3A,B). Hence, it is clear that the overexpression of _RrGGP2_ is more effective in enhancing the AsA content of tomato fruits than the overexpression of _RrDHAR_.
## 4 Performance Analysis
### SINR Comparison of All Beamformer with Respect to Disparity Angle
#### 4.2.2 SINR Comparison of the ODL-Based Beamformer with Respect to the Disparity Angle  
In Figure 3b, the SINR of the LCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is 36.31 dB, 33.13 dB, and 30.39 dB, respectively. With 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, the SINR of the LCPAA beamformer is 37.94 dB, 37.82 dB, and 37.66 dB, respectively. The SINR LRAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is 38.69 dB, 37.29 dB, and 35.26 dB, respectively.  
Figure 3: (**a**) SINR comparison of FDL-based beamformer with respect to the disparity angle. (**b**) SINR comparison of ODL-based beamformer with respect to the disparity angle. (**c**) SINR comparison of VDL-based beamformer with respect to the disparity angle. (**d**) SINR comparison of the NVL-based beamformer with respect to the disparity angle.  
The SINR of the LCCA beamformer is 37.52 dB, 37.52 dB, and 37.49 dB for 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, respectively. The SINR of all beamformers without disparity is 40.45 dB.