esa-sceva/satcom-chunk-collection huggingface dataset:
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 3 Methods
### Overview  
Cropped and data-augmented fundus images are the input sent to the BEAC-Net network. In Figure 2a, we propose a BEAC-Net that consists of encoders and decoders. The encoder captures the multi-scale contextual information to progressively reduce the feature map while capturing more advanced semantic information, and the encoder implements the gradual recovery of spatial information to capture clearer OD and OC boundaries. In the encoder, we develop an adaptive context module (ACM) to enhance both local-range and long-range contextual representations. More specifically, to model adaptive spatial pyramid pooling (ASPP), a pyramid of ACM modules with muti-scale shifted window sizes is designed. To accurately recognize the OD and OC boundaries, it is necessary to extract and fuse features from different levels to capture a better long-range dependency context simultaneously. In the channel dimension, different levels of feature maps are concatenated together. Efficient boundary pixel attention (EBPA) is used to realize the inter-relationship between the boundary feature pixel points of the OD/OC and other pixel points. Furthermore, the boundary pixel points are weighted with the inter-relationship to extract the high-level semantic features, which further extracts and integrates the feature maps. To guide the decoder processing, richer contextual information is captured by upsampling via 4 \(\times\) 4 convolution to obtain high-level semantic information, which is fused with the low-level semantic information via attentional feature fusion (AFF) to solve the blurred boundaries during the upsampling process. We obtain more effective segmented boundary features, which are significant for fundus image analysis.
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 3 Methods
### Overview  
Due to the memory limitation of intelligent systems, existing semantic segmentation models are difficult to achieve the requirements of real-time applications for unmanned vehicle systems. Therefore, based on the 3D-Unet [32] model, a real-time lightweight, fully convolutional network, LFNet, based on an attention mechanism and a sparse tensor, is proposed (as shown in Figure 1). Our model is a lightweight, fully convolutional neural network implemented using a sparse tensor. The left half of the figure shows the feature extraction network, which uses multiple stacked convolution blocks and pooling operations to learn point cloud features at various scales. The right half is the feature fusion network where feature fusion is achieved through transposed convolutional operations and the MFFM.  
The GFLM and MFFM are designed. Our model improves the feature representation of important regions by acquiring contextual information and learning the importance of spatial neighborhoods through a global feature-learning module. Our model learns the weight distribution of features at different scales through the MFFM, thus enhancing the fusion of multi-scale semantic information. To improve the calculation efficiency of the model, we use a sparse tensor representation of the rasterized point cloud data. The sparse approach only stores the coordinates and feature information of the raster, thus reducing the memory footprint. Convolution, pooling, normalization, and other operations in themodel are implemented based on the sparse tensor in order to reduce the unnecessary calculation quantity.
## 3 Result and Discussion
### EK Remediation Experiments  
#### 3.2.1 Post-Experiment Water Content  
Figure 4 shows the post-experiment water content in the sample, which was determined from five separate ports upon completing the experiment. Each value of the water content in general was not much different from the initial value of 60%; however, it tended to be at a point of x/L = 0.9. Considering that the water content remained similar to that of the initial value, the movement of the improver within the soil was confirmed to be most active during the EK remediation.
## 5 Evaluation  
This section presents the experimental results for ASEM-CAD. We evaluated the performance of a fully supervised model, a semi-supervised model with EM, and the ASEM-CAD model using high-entropy and average classification entropy-based label acquisition policies. We evaluated the results based on metrics such as AUC/ROC, accuracy, sensitivity, and specificity.  
The initial model was fully trained until convergence, and for each additional EM iteration, the model was retrained for 10 epochs. We conducted experiments on an AMD processor with a clock rate of 1885 MHz, 32 cores, 658 GB of storage space, and three NVIDIA GeForce RTX GPUs, each with 11 GB of memory. Table 3 displays the training time (wall clock time) for the ASEM-CAD model. The overall increase in the training time is around 30-50%, rather than a tenfold increase in runtime, as the model reuses the parameters (weights) after each iteration instead of retraining. During the maximization phase of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Predicted (True)** & **Predicted (False)** \\ \hline Actual (True) & True Positive & False Negative \\ Actual (False) & False Positive & True Negative \\ \hline \hline \end{tabular}
\end{table}
Table 2: Binary classifier confusion matrix.  
EM, each ASEM-CAD iteration strives to enhance the maximum likelihood estimates of the model parameters. Hence, the weights from the previous ASEM-CAD iteration can be utilized to assess the weights of the next iteration, which significantly reduces the number of epochs, ultimately decreasing the training time for the ASEM-CAD iterations.  
In addition to active, semisupervised learning with Bayesian approximation, we applied upsampling, label smoothing, batch normalization, hyperparameter tuning, and early termination techniques to improve the model performance.  
ASEM-CAD was trained using batches of 32 samples, after which the initial model was saved. In the following phase, EM iterations load the parameters that were saved from the initial model and begin the active EM training process. The active component selects 10 samples and determines which labels to acquire ground truth labels for based on an entropy-based label acquisition policy. For each dataset, we evaluated ASEM-CAD with increasing labeled samples in steps of 10 iterations (step10) and analysis of active learning phases (active10) on two different neural network architectures.
## 3 Distance Vector-Hop (DV-Hop) and Differential Evolution (DE) Algorithms
### Differential Evolution Algorithm
#### 3.2.2 Mutation  
For each target individual \(x_{i}^{t}\), where \(i=1,\ 2,\cdots,\ NP\), its mutant \(v_{i}^
# 2 \(\times\) 2 Matrices: Manifolds, Realizations, Applications
## 1 Introduction: Linear Transformations
Thus defined, the group is called the _general linear_ group of \(2\times 2\) matrices, indicated as \(\mathsf{GL}(2,\mathsf{R})\) or \(\mathsf{GL}(2,\mathsf{C})\) for real or complex matrices respectively. Since the determinant of a product of matrices is the product of their determinants, a handier three-parameter subgroup is formed by the subset of those matrices that have unit determinant, \(\det\mathbf{M}=ad-bc=1\), which is called the group of _special linear_ matrices, indicated \(\mathsf{SL}(2,\mathsf{R})\) or \(\mathsf{SL}(2,\mathsf{C})\), whose elements are identified as points in a manifold of 3 or 6 real parameters respectively. The purpose of this review is, first, to explore the topology of the manifold and, second, show their efficacy for models of mechanical and optical systems such as those cited above. In Section 2, we present the concept of manifold covers through the examples of the two-fold cover that spin matrices provide over rotation matrices, and the two-fold cover of \(\mathsf{SL}(2,\mathsf{R})\) over the group of \(3\times 3\) matrices of '\(2+1\)' relativity. Section 3 will identify \(2\times 2\) matrices with the basic elements of plane paraxial geometric optical setups (free displacements and lenses) to form imagers, classical fractional Fourier transformers, and negative displacements. In Section 4 we detail the infinite cover of \(2\times 2\) matrices for wave optics and quantum oscillator mechanics. Thus we place the two-fold cover that is necessary to parametrize the manifold of canonical integral transforms. Finally, in the concluding Section 4 we add comments on all the irreducible representations of \(2\times 2\) matrices, whose recount is beyond application in geometric optics.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 4 The Proposed DNA Data Storage Readout Method Based on Heterogeneous Computing
### Architecture of Hardware Acceleration Kernel  
The FPGA-based DNA data readout hardware acceleration circuit was developed based on the proposed DNA storage encoding scheme. As illustrated in Figure 4, the hardware kernel utilizes off-chip DRAM for storing intermediate results and employs a two-stage pipelined architecture to optimize the read process. The first stage focuses on primer sequence identification and index checking, supporting the parallel processing of two sequencing reads. In this stage, the sequencing reads are stored in DDR BANK1, while the payloads obtained after primer sequence identification and index checking are stored in DDR BANK2. The second pipeline stage encompasses majority voting, valid-sequence filtering, deinterleaving, and LDPC decoding, and saves the decoded result in DDR BANK1. The memory read and write channels of the two pipeline stages are separated, effectively preventing data congestion and significantly improving overall system efficiency.  
The copy count memory is used to store the number of sequencing reads that have successfully identified the same reference. The copy count memory is constructed using BRAM resources with a design specification of 64 bits wide and 393,216 bits deep. Each 64-bit element is subdivided into 16 4-bit count cells, each with a maximum count limit of 15, allowing up to 15 sequencing read copies to be stored. This is sufficient for data recovery, as demonstrated by [10].  
Figure 3: Overall framework of DNA data storage readout method based on heterogeneous computing.  
In index sequence design, sequences with homopolymer lengths greater than three are excluded from the index, resulting in discontinuous index sequences in the synthesized DNA strands. A direct table lookup approach to map these discrete indices to a continuous index sequence would require excessive storage resources. To address this, we stored the identified data payload copies in memory in a dispersed manner according to their original indices. After majority voting, sequences corresponding to invalid indices are discarded. Compared to the direct-lookup-based index mapping method, the proposed approach significantly reduces BRAM resource consumption.The workflow of the DNA data storage acceleration kernel is as follows.
## 5 Discussion and Conclusions
### ANN Prediction Results  
1. The ANN system presents a better source credibility recognition rate for mainstream news than content farms.  
The experimental results with the ANN system reveal the true news hit rates of six mainstream news platforms, as follows: Apple Daily, 82.0%; China Times, 71.0%; Liberty Times, 96.5%; United Daily News, 93.0%; ETToday, 66.0%; and CNA, 88.5%. The highest hit rate is 96.5% and the lowest hit rate is 66.0%. The fake news hit rate of six content farms shows mission-tw.com with 85.0%, nooho.net with 65.5%, kknews.cc with 87.0%, BuzzHand with 98.0%, qiqi.news with 86.0%, and Global Military with 87.0%, with the highest hit rate of 98.0% and the lowest hit rate of 65.5%.  
To further analyze the above data, it is discovered that the ANN system presents better true news hit rates for mainstream news platforms than the fake news hit rates for content farms in the training set. This is possibly because the ANN system uses word-frequency-based recognition, which calculates the frequency of words in an article, and headlines and content on mainstream news platforms follow a fixed news writing format, the word expression is kept at a certain level, and the words used are careful with less tautology and leponasm. In this case, the ANN system is more suitable for the prediction of mainstream news platforms than for content farms.
2. The ANN system found the lowest source credibility recognition rate for nooho.net.  
Among all experimental results, prediction with the ANN system resulted in a mere 65.5% hit rate, the lowest source credibility recognition rate, for nooho.net. This may be due to the few training news parameter data of nooho.net, and the extremely low quantity of daily news results in a larger prediction amplitude. It is therefore not suitable for data  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Mainstream News**} & Central \\ \hline Test data & & & Liberty & United Daily & EToday & News \\ source & Apple Daily & China Times & Times & News & Agency & 200 \\ N & 200 & 200 & 200 & 200 & 200 & 200 \\ Hit rate & 90.5\% & 90.5\% & 100.0\% & 96.5\% & 89.0\% & 100.0\% \\ CI (95\%) & 86.35–94.65\% & 86.35–94.65\% & 100.0\% & 93.90–99.10\% & 84.58–93.42\% & 100.0\% \\ Error & 4.15\% & 4.15\% & 0\% & 2.60\% & 4.42\% & 0\% \\ \hline \multicolumn{8}{c}{**Content Farm News**} & & \\ \hline Test data & mission- & & nooho.net & kknews.cc & buzzhand.com & qiqi.news &
\begin{tabular}{c} Global \\ Military \\ \end{tabular} \\ source & tw.com & 200 & 200 & 200 & 200 & 200 \\ N & 200 & 79.0\% & 85.0\% & 89.0\% & 83.0\% & 88.5\% \\ Hi rate & 98.5\% & 73.24–84.76\% & 79.95–90.05\% & 84.58–93.42\% & 77.69–88.31\% & 93.01–83.99\% \\ Error & 1.72\% & 5.76\% & 5.05\% & 4.42\% & 5.31\% & 4.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The result of the BERT method for mainstream news and content farm news.  
analysis with this system and might affect the news source credibility prediction of the word-frequency-based ANN system.
# Multi-Angle Fast Neural Tangent Kernel Classifier
## 3 Background Techniques
### Kernel Functions
#### 3.3.2 Neural Tangent Kernel  
[PERSON] et al. [41], building on a series of studies such as [PERSON] et al. [42] and [PERSON] et al. [43], first connected a neural network with all layers trained, infinitely large, and trained using gradient descent to a kernel predictor, expressing the dynamics of the neural network in terms of a representation through an ordinary differential equation, so that the training process of the DNN can be approximated with the help of NTK.  
Specifically, let \(x\in R^{d}\) be the input, \(t^{(0)}(x)=x\) denotes input variables that are not nonlinearized, and \(d_{0}=d\) represents the width of the initial layer. Then, a neural network with \(L\) hidden layers can be defined as follows:  
\[\begin{array}{ll}f(w,x)&=f^{(L+1)}(x)=W^{(L+1)}t^{(L)}(x)\\ &=W^{(L+1)}\sqrt{\frac{\sigma_{c}}{d_{L}}}\sigma(W^{(L)}\cdots\sqrt{\frac{ \sigma_{c}}{d_{1}}}\sigma(W^{(1)}(x)))\end{array} \tag{8}\]  
\(W^{(h)}\in R^{d_{h}\times d_{h-1}}\) is the weight matrix of layer \(h\). The set of network parameters is \(w=(W^{(1)},\cdots,W^{(L+1)})\). All elements are initialized with independent standard normal distributions. \(\sigma:R\to R\) is the activation function, RELU is recommended. \(c_{\sigma}\) is the scaling factor, usually defined as 2, which can be used to avoid gradient explosion or disappearance in deep networks.  
[PERSON] deduces that the dynamics of the neural network exhibit limiting behavior along the gradient trajectory when the width \(d_{1},d_{2},\cdots,d_{L}\rightarrow\infty\) of each layer. Let \(x,x^{\prime}\in R^{d}\) be two data points, then the covariance kernel \(\sum^{(h)}(x,x^{\prime})=f^{(h)}(x)\cdot f^{(h)}(x^{\prime})\) of the output of layer \(h\) can be recursively defined as:  
\[\begin{array}{ll}\sum^{(0)}(x,x^{\prime})=f^{(0)}(x)\cdot f^{(0)}(x^{\prime })=x^{T}x^{\prime},\\ \Lambda^{(h)}(x,x^{\prime})=\left(\begin{array}{cc}\sum^{(h-1)}(x,x)&\sum^ {(h-1)}(x,x^{\prime})\\ \sum^{(h-1)}(x^{\prime},x)&\sum^{(h-1)}(x^{\prime},x^{\prime})\end{array} \right),h\in[L]\\ \sum^{(h)}(x,x^{\prime})=c_{\sigma}E_{(u,v)\sim N(0,\Lambda^{(h)})}[\sigma(u) \sigma(v)],\end{array} \tag{9}\]  
It is important to note that this recursive form holds during initialization and training (gradient descent training of \(\eta\to 0\) is required). Formally, NTK is shown to be the kernel in the limit case of:  
\[\begin{array}{ll}\Theta(x,x^{\prime})&\triangleq\left\langle\frac{\partial f (w,x)}{\partial w},\frac{\partial f(w,x^{\prime})}{\partial w}\right\rangle \\ &=\sum\limits_{h=1}^{L+1}\left\langle\frac{\partial f(w,x)}{\partial W^{(h)}},\frac{\partial f(w,x^{\prime})}{\partial W^{(h)}
# Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey
## 3 Wearable Medical Devices
This antenna array is based on the crossed dipole topology and is analyzed in free space and on the body. Figure 13 shows the antenna structure in detail where it can replace a button on clothing and thus be wearable. Figure 11: Printed spiral antenna proposed in [48]: top view (**left**) and bottom view (**right**). Figure 4:0 The localized hand palm channel for the five test subjects [49]. From research and industry perspectives, monitoring sleep has become important in the sleep healthcare systems. In [51], a new sleep healthcare system based on radio frequency science for leveraging signal processing was presented, namely Sleep Guardian. This system can detect and notify of any abnormal situation arising when a patient is sleeping; additionally, it can be integrated in present Wi-Fi infrastructure. The healthcare systems are connected through the base stations and the required data are collected through accumulated healthcare big data where machine learning technology is employed for predicting and better diagnosing patients. [PERSON] et al. [52] proposed electroencephalography (EEG) biosignals' acquisition--that is reusable--on e-textile and convenient. The proposed fabric-based headband tackles the drawbacks of the bulkiness of the system, an unsuitable situation caused by the rigid electrodes, and laborious tasks. In this study, the proposed headband where the printed electrodes are getting advantage of a very low skin-electrode impedance. [PERSON] et al. presented a dual chip flexible epidermal antenna that is for measuring the skin parameters using the UHF Radio Frequency Identification (RFID) technology inside the frequency band of 860 MHz-960 MHz [53]. Figure 14 describes the use of this proposed RFID that is used for monitoring health by directly sampling from the skin of patients. The used antenna is with two independent broadside radiating modes and the results present the turn-on powers (\(P_{to}\) (dBm)) with the differential power (\(\Delta P_{to}\)) that the chip is attached to different parts of the body such as the chest, forehead, abdomen, and hand, where the antenna is within a distance of 50 cm from the body. Figure 4:1 Illustration of the wearable button antenna. Reprinted/adapted with permission from Ref. [50]: Copyright 2022, IEEE. (**a**) top view; (**b**) bottom view; (**c**) radiator structure; and (**d**) side view. In [54], a new flow-guided nano-communication network was presented that can be used in up-to-date medical applications to monitor, collect, and transmit various data from the human body (Figure 15). This network can be operated in the bold circulatory system. The presented optimization in this paper leads to maximize throughput by determining a suitable frame size as a function of the available energy, nano-router location, and transmission rate.
## 5 Conclusions  
Considering the unidirectional flow of wind power transmission, a topology based on a distributed parallel-series DR and its control strategy is proposed.  
1. The coordinate control strategy is proposed to solve the energy curtailment, where the voltage control strategy for the parallel part and a current control strategy are for the series part according to the coupling mechanism of constant DC voltage and constant DC current.
2. A triple target control strategy based on the MMC-HVDR is proposed with an intermediate variable according to the characteristics of the DR, which maintains the capacitor voltage, input DC voltage, and output voltage simultaneously.
3. Energy accumulating during onshore faults is analyzed, and an active voltage injection control strategy is proposed for the ride-through of onshore faults without communication.  
The simulation results verified the effectiveness of the system and its control strategies. Under the proposed control strategy, stable operation will not be affected by different kinds of disturbances. The wind turbine automatically operates at the MPPT without overvoltage. The system can achieve onshore fault ride-through quickly without a braking resistor.  
Figure 16: The waveforms for the onshore converter. (**a**) The DC voltage of HVDC link; (**b**) The DC current of HVDC link; (**c**) Average capacitor voltage of onshore MMC.  
Figure 17: The waveforms for the wind turbines and MMC-HVDR. (**a**) Active power of wind turbine; (**b**) Output voltage of MVDR; (**c**) Average capacitor voltage of MMC.  
The characteristics and control strategies for transient conditions such as asymmetric grounding faults and frequency drops in the onshore grid will be analyzed in the future. The hardware in the loop simulation platform will be built to verify the topology and control strategy.  
L.X.: Writing--original draft, Conceptualization, Formal analysis, Methodology, Software, Writing--review and editing. R.H.: Supervision, Writing--review and editing. Z.L.: Supervision, review and editing. B.L.: Writing--review and editing. Y.W.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Grid Corporation, grant number 5500-202358504A-3-2-ZN.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the first author upon reasonable request. (First author E-mail address: [EMAIL_ADDRESS]).  
[PERSON] and [PERSON] supervised this paper well. We thank [PERSON] and [PERSON] for their timely help in reviewing. Support from the State Key Laboratory of Advanced Power Transmission Technology is gratefully acknowledged.  
Author [PERSON], [PERSON] were employed by Beijing Jiaotong University, and Author [PERSON], [PERSON], [PERSON] and [PERSON] were employed by the company China Electric Power Research Institute. The authors declare that the re-search was con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.