esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Low-Power ECG Sampling and Compression Circuit
## 2 Sampling and Compression Circuit Based on Compressed Sensing  
### Compressed Sensing  
CS is a sampling theory that compresses and reconstructs data based on signal sparsity [15]. The mathematical model of the CS algorithm is defined as Equation (1):  
\[\mathrm{Y}=\Phi\mathrm{X} \tag{1}\]  
where \(\mathrm{X}\) represents the \(N\)-dimensional original signal \(\mathrm{X}=\left[x_{1},x_{2},\,\ldots,x_{N}\right]^{\mathrm{T}}\), \(\mathrm{Y}\) is the \(M\)-dimensional compressed signal \(\mathrm{Y}=\left[y_{1},y_{2},\,\ldots,y_{M}\right]^{\mathrm{T}}\), and \(\Phi\) denotes the \(M\times N\) measurement matrix (\(M<N\)).  
The measurement matrix largely determines the signal reconstruction quality. The measurement matrices commonly used in the CS algorithm are the Gaussian, Bernoulli, and binary. Considering the difficulty and consumption of hardware implementation, this paper utilizes a binary matrix as the measurement matrix, which converts the matrix multiplication and addition operations to selective addition operations [16], remapping the compression process into Equation (2):  
\[\begin{bmatrix}y_{1}\\ \vdots\\ y_{M}\end{bmatrix}=\begin{bmatrix}\Phi_{1,1}&\cdots&\Phi_{1,N}\\ \vdots&\ddots&\vdots\\ \Phi_{M,1}&\cdots&\Phi_{M,N}\end{bmatrix}\times\begin{bmatrix}x_{1}\\ \vdots\\ x_{N}\end{bmatrix}=\begin{bmatrix}\Phi_{1,1}\\ \vdots\\ \Phi_{M,1}\end{bmatrix}\times x_{1}+\begin{bmatrix}\Phi_{1,2}\\ \vdots\\ \Phi_{M,2}\end{bmatrix}\times x_{2}+\ldots+\begin{bmatrix}\Phi_{1,N}\\ \vdots\\ \Phi_{M,N}\end{bmatrix}\times x_{N} \tag{2}\]The reconstruction process aims to find a definite and unique solution under certain constraints. Typical reconstruction algorithms are the Orthogonal Matching Pursuit, Compressed Sampling Matching Pursuit, and Block-Sparse Bayesian Learning (BSBL), with BSBL presenting a more stable and efficient reconstruction quality [17]. The performance of the CS algorithm is usually measured by CF and PRD metrics. CF is calculated in Equation (3):  
\[\text{CF}=\frac{N\times\text{B}_{x}}{M\times\text{B}_{y}} \tag{3}\]  
where B\({}_{x}\) and B\({}_{y}\) are the numbers of bits of the original signal and compressed signal, respectively. A large CF represents high compression efficiency, and a small PRD indicates good reconstruction performance.
## 3 Experiments and Results
### Experimental Setup
#### 3.1.2 Evaluation Metrics  
The performance of the model was assessed by the evaluation metrics used in the DCASE2021 Challenge, which consider joint dependencies between the performance of the SED and DOAE. In the SELD, the F1-score \(F_{\leq T}\) and error rate \(ER_{\leq T}\) for SED are location-dependent, considering true positives for the predictions only when the difference between the DOAE and the reference is under a threshold \(T\). Here, \(T\) was set to 20\({}^{\circ}\). For the metrics of DOAE, localization error \(LE_{\text{CD}}\) and localization recall \(LR_{\text{CD}}\) are designed class-dependent, meaning that they are computed only across each class and then averaged. \(LE_{\text{CD}}\) shows the average angular distance between predictions and references of the same class, whereas \(LR_{\text{CD}}\) calculates the true positive rate of the localization predictions in a class out of all class instances [8].  
These metrics can be integrated into a joint measurement \(D_{\text{SELD}}\) as given in [17]:  
\[D_{\text{SELD}}=\frac{1}{4}[ER_{\leq_{\text{T}}}+(1-F_{\leq_{\text{T}}})+\frac{ LE_{\text{CD}}}{180^{\circ}}+(1-LR_{\text{CD}})]. \tag{13}\]  
The higher \(F_{\leq_{\text{T}}}\) and \(LR_{\text{CD}}\) and the lower \(ER_{\leq_{\text{T}}}\), \(LE_{\text{CD}}\), and \(D_{\text{SELD}}\) means better performance.
# COVID-19 Diagnosis from Crowdsourced Cough Sound Data
## 3 Proposed Method
### Model  
The model in this study used a combination of ResNet-50 [25] and a deep neural network (DNN) to distinguish between the cough sounds of COVID-19 positive people and the cough sounds of healthy people. This model was constructed based on the model proposed in [13]. The ResNet-50 was trained with spectrogram images (224, 224, 3) extracted from the audio chunk file. The ResNet-50 model was divided into a Global Average Pooling layer and a Global Max Pooling layer, and was reconnected after performing batch normalization and dropout. The DNN was trained with the 36-dimensional feature set configured in this study as an input. It was divided into two layers of 256 node layers and two layers of 64 node layers, respectively, and was connected after dropout was performed on each layer. GlorotUniform was used for the kernal initializer of each layer and Relu was used for the active function. In all models, the dropout was 0.5. The output values from ResNet-50 and DNN were connected to each other (Figure 7).  
The values output by the ResNet-50 and DNN were connected. Then, an output value was calculated using the sigmoid function after passing through the dense layer, batch normalization layer, and dropout layer. Using this value, the cough sound of COVID-19-positive people was distinguished from the cough sound of healthy people.  
Figure 6: Spectrogram images extracted from the segmented cough sound data of male and female participants who were positive and negative for COVID-19.
## 3 Results and Discussion
### Calculated Core-Level Binding Energies, XP Spectra, and Work Function Changes
#### 3.3.1 Energetic Shifts Due to Variations in the Interface Dipole
To test that, we plotted \(\Delta\Phi\) as a function of \(\Delta\)E\({}_{\text{CIs}}\) for all investigated SAMs in Figure 4. Indeed, the data for all SAMs without polar tail group substituents align perfectly along a straight line with a slope of 1 passing through the origin. This supports the assessment that in these systems core level shifts and work function changes have the same origin (namely the interface dipole). In passing we note that the dipole due to the terminal C-H bond of the biphenyl cancels out for the evolution of \(\Delta\Phi\) vs. \(\Delta\)E\({}_{\text{CIs}}\) (at least for similar coverages and tilt angles), as the non-interacting biphenyl monolayer is used as the reference for both quantities. Figure 4: Correlation between the change in work function, \(\Delta\Phi\), and the shift in binding energy, \(\Delta\)E\({}_{\text{C1s}}\), for all systems relative to system VIa (as non-interacting reference system in which no interface dipole occurs). The black line indicates a linear curve with a slope of 1 through the origin. The shaded area represents the range over which the values for \(\Delta\)E\({}_{\text{C1s}}\) vary between systems I, VII, and VIII. Figure 3: (**a**) Simulated shifts of carbon 1s orbital energies, \(\Delta\)E\({}_{\text{C1s}}\), as a function of the escape depth of the electrons for the prototypical systems I, III, IV, and VIa and (**b**) the corresponding XP spectra generated from the calculated energy levels. The energy scale is chosen relative to the position of the peak in the XP spectrum for the vacuum-level aligned system VIa. The escape depth defined as the distance from the topmost carbon atom is chosen as the parameter on the \(x\)-axis considering that the highest atoms primarily determine the XP spectra. As the extent of the SAMs between the bottom ring and the metal substrate varies due to the different docking groups, this choice of the \(x\)-axis also largely aligns biphenyls in the different systems. System IV is shown in the background as an example of the structure of one of the SAMs considered in the plot. Due to the different SAM thicknesses, we included vertical lines in panel (**a**), which designate the positions of the centers of the atoms of the topmost Au layer. The large number of data points for each system is a consequence of the two inequivalent molecules per unit cell.
## 4 Simulation Results and Performance Analysis
### Discussions  
The higher stability of the clusters formed by our CDS_SC algorithm ensures the reduction in re-clustering process. Since cluster formation process requires few additional messages like Candidate Cluster Head Message, Candidate Gateway Message, Gateway Selection Message etc., to be exchanged among the neighborhood vehicles, the reduction in re-clustering process significantly reduced the message overhead due to such additional messages. When compared to DTA and DWCM protocols, our CDS_SC had lesser cluster head and gateway vehicles which yielded a predominant decrease in the number of rebroadcasts, thereby alleviating the broadcast storm problem. Ultimately our proposed scheme achieves 99% to 100% message reachability in average traffic scenario with only 6% to 10% of rebroadcasting vehicles.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 1 Introduction
Thirdly, to achieve obstacle avoidance control for the inspection robot in a roadway environment, the sensing and signal processing layer, decision control layer, and motion execution control layer were established. The virtual prototype of the tracked robot and the simulation environment of the obstacle environment were established in ADAMS software, and the rationality of the control strategies of the tracked robot obstacle-avoiding steering were simulated and analyzed. Finally, the theoretical analysis results were used to guide the tracked robot to complete a performance test.
## 4 Results and Discussion
### Cost Assessment  
The cost assessment was evaluated in this section. In this virtual simulation process, 200 and 400 tasks with varying deadlines were considered. The iteration was between 10 to 50. Figures 6 and 7 show the results that were obtained. Figure 6 considers 200 task cost values, while Figure 7 considers 400 task cost values. Cost assessment of the proposed algorithm HWACO was compared with other algorithms, including QANA, MTF-BPSO, ACO, MIN-MIN, and FCFS. From the experimental results obtained, they suggest that the introduced algorithm HWACO achieved a low cost for both cases when number of tasks were 200 and 400.  
Figure 5: The convergence plot for makespan of 400 tasks.  
Figure 6: The task cost values of 200.
## 5 Experimental Analysis
### Performance Comparison  
To illustrate the effectiveness of our proposed model, we first compared ECKEI to the baseline methods mentioned above. Figures 7-9 demonstrate the comparative results of different approaches when detecting stance, and the overall performances of the different approaches on the two datasets are shown in Table 4. The results on the two datasets show that the ECKEI model is more competitive. From Figures 7-9, we can observe that user stance detection methods are more inclined to large datasets and can play a important role. Regarding machine learning and neural network-based models, TAAT has better performance than other methods; this is because more features are extracted. However, the TAAT model is obviously a bit laborious when processing topic information and neighbor information.  
As show in Table 4, our proposed model significantly outperforms all baseline methods by achieving the highest average micro-F1, average accuracy and average recall on two datasets. From the results, we can see the SVM-ngram model has better performance than the NB model when comparing traditional machine learning models. Regardless of the dataset, we find that the average accuracy, average micro-F1 and average recall scores of SVM n-gram are better than NB by 1.42-6.70%, which indicates that the SVM model has a great advantage in stance classification tasks. Regarding the Aff-feature model, we can find that after adding the information in the context of the tweet, it demonstrates a performance improvement of 4.88-6.41% compared to the previous deep neural network model. Then, after the historical tweets are input, they are compared with the TAAT model. The adjacent tweets of each tweet are regarded as local context. The attention mechanism is used to obtain the influence of adjacent tweets, and we can find that the model has obvious advantages when compared with other models. Finally, we find that MITRE (RNN) is the closest model to our model's average accuracy, average micro-F1 and average recall scores. The first reason to explain the better performance of the ECKEI model is that it uses the CK-BiLSTM network as an encoder to process external commonsense knowledge in order to better supplement the original information. Secondly, our proposed model considers the influence of neighbors' emotions on users' viewpoints states, which helps to explore changes in stance between user interactions.
# Storing Set Families More Compactly with Top ZDDs
## 2 Preliminaries  
Here we explain notations and basic data structures.  
Let \(C=\{1,\ldots,c\}\) be the universal set. Any set in this paper is a subset of \(C\). The empty set is denoted by \(\emptyset\). For a set \(S=\big{\{}a_{1},\ldots,a_{s}\big{\}}\subseteq C\ (s\geq 1)\), its size is denoted by \(|S|=s\). The size of the empty set is \(|\emptyset|=0\). A subset of the power set of \(C\) is called a set family. A set family is also called a family of sets, a set of sets, or a combinatorial set. If a set family \(\mathcal{F}\) satisfies either \(S\in\mathcal{F}\Rightarrow\forall k\in S,S\backslash\{k\}\in\mathcal{F}\) or \(S\in\mathcal{F}\Rightarrow\forall k\in C,S\cup\{k\}\in\mathcal{F}\), \(\mathcal{F}\) is said to be monotone. If the former is satisfied, \(\mathcal{F}\) is monotone decreasing and the latter monotone increasing.
## Acronyms/Abbreviations  
Ground sample distance (GSD), artificial intelligence (AI), deep convolutional neural network (DCNN), feature pyramid network (FPN), feature refinement module (FRM), normalized correlation coefficient (NCC), commercial off-the-shelf (COTS), KNN (K-nearest neighbor).
# Using a GPU to Accelerate a Longwave Radiative Transfer Model with Efficient CUDA-Based Methods
## 3 Model Description and Experiment Platform
### RRTMG Radiation Scheme
Under some assumptions, Equation (1) becomes  
\[I_{g}(\mu,\varphi)=\int_{0}^{1}dg\left\{Beff(g,T_{g})+[I_{0}(g)-Beff(g,T_{g})] exp[-k(g,P,\theta)\frac{\rho\Delta z}{cos\varphi}]\right\}, \tag{2}\]  
where \(g\) is the fraction of the absorption coefficient, \(P\) is layer pressure, \(\rho\) is the absorber density in the layer, \(\Delta z\) is the vertical thickness of the layer, \(\varphi\) is the angle of the optical path in the azimuthal direction, \(k(g,P,\theta)\) is the absorption coefficient at \(P\) and \(\theta\), and \(Beff(g,T_{g})\) is an effective Planck function for the layer. The monochromatic net flux is expressed as  
\[F_{v}=F_{v}^{+}-F_{v}^{-}, \tag{3}\]  
where \(F_{v}^{+}=2\pi\int_{0}^{1}I_{v}(\mu)\mu du\) and \(F_{v}^{-}=2\pi\int_{0}^{-1}I_{v}(\mu)\mu d\mu\). The total net flux is obtained by integrating over \(v\)  
\[F_{net}=F_{net}^{+}-F_{net}^{-}. \tag{4}\]  
The radiative heating (or cooling) rate is expressed as  
\[\frac{d\theta}{dt}=-\frac{1}{c_{p}\rho}\frac{dF_{net}}{dz}=\frac{g}{c_{p}} \frac{dF_{net}}{dP}, \tag{5}\]  
where \(c_{p}\) is the specific heat at constant pressure, \(P\) is pressure, \(g\) is the gravitational acceleration, and \(\rho\) is the air density in a given layer [37].
## 3 Results
### Comparison of Flow Pattern Derived by the 4D Flow MRI with CFD  
Flow conditions for all subjects were analyzed with both the 4D flow MRI and CFD. Visual comparison between the 2 techniques of the 3D velocity streamline is provided in Figure 4. These visualizations show that the major flow structures and the secondary flow directions of the carotid arteries bifurcation observed in the 4D flow MRI moderately coincide with the CFD simulation (Figure 5). However, several differences were identified (Figure 6): (1) Secondary flows are of poorer quality when measured through the 4D flow MRI technique compared to CFD, making the flow pattern more parallel or laminar; (2) 3D velocity streamlines from the 4D flow MRI results stop suddenly at the segment of an arterial wall within the carotid bifurcation domain; (3) recirculation regions at the carotid sinus and carotid bifurcation, which is characteristic of transitional flow, were not well captured by the 4D flow MRI-performed flow patterns; (4) in the CFD models observed at the CCA and ICA, the maximum velocity magnitude of blood flow tended to be higher than in the 4D flow MRI simulation. By contrast, greater flow velocity in the CCA was measured by the 4D flow MRI simulation in almost all cases.  
Figure 3: (**A**) Comparison of the velocity waveform at the bifurcation plane derived from 4D flow MRI (red line) and from CFD (dashed line); (**B**) Bland–Altman plot analysis of velocity data at the carotid bifurcation plane comparing the results of CFD and 4D flow MRI of maximum and minimum velocity. The green lines indicate the mean of the difference between 4D flow MRI and CFD \(\pm\) 1.96 standard deviation; the red line shows mean difference value.  
Figure 4: The flow patterns and velocity fields distribution for 12 carotid arteries at peak systole of 4D flow MRI (**left**) and CFD (**right**).  
Figure 5: Visualization of velocity streamlines in the carotid artery bifurcation at peak systole using 4D flow MRI and CFD. 4D flow MR images of secondary flow direction and location (arrows) moderately coincide with the CFD visualization. (**A**) Lateral view; (**B**) posteroanterior view.
## 2 Related Works  
### Image Super-Resolution  
Image super-resolution is a traditional task in computer vision involving the use of algorithms to enhance low-resolution images to high-resolution ones. This technique finds applications in various fields, such as surveillance, medical imaging, and media. Its advantage lies in the ability to effectively improve the outcomes of subsequent tasks, such as face recognition and semantic segmentation, without increasing sensor hardware costs. In past research, various techniques have been developed, encompassing regularization techniques [12], neighborhood embedding-based algorithms [13], and the utilization of similar patches that exhibit redundancy in low-resolution images when compared to their higher-resolution counterparts. Nonetheless, these methods are not optimal and encounter challenges when applied to tasks involving higher-scale images.  
Simultaneously, with the continuous development of deep learning technology, [14] were the first to apply convolutional neural networks (CNNs) to this task. They employed a lightweight deep CNN for end-to-end learning on the data, achieving state-of-the-art results in both quality and speed. The observation that deeper networks for super-resolution (SR) are challenging to train, coupled with the underutilization of abundant information in feature maps across various layers, led to the proposition of residual channel attention networks (RCANs) [15]. The residual-in-residual (RIR) structure facilitates the bypassing of copious low-frequency information through multiple skip connections, enabling the primary network to concentrate on learning high-frequency information. Moreover, attention mechanisms [16; 17] targeting spatial or channel dimensions have been incorporated to enhance the model's capacity for improved representation. Recently, the Transformer [18] has shown remarkable performance in high-level vision tasks. It reveals that both spatial and channel information are important for performance. Pre-trained Transformer models [19; 20] have also been proposed, and their effectiveness has been validated in the context of SR tasks. ESSAformer [21] was proposed as a Transformer network embedded with ESSA attention for single hyperspectral image super-resolution, featuring an iterative refining structure. Remarkably, ESSA achieved significant improvements in both visual quality and quantitative results in experiments, all without the necessity of pretraining on large datasets.
## 3 Results and Discussion
### BCF of Metal(loid)s in Corn and Wheat
The BCF value for Cd in wheat was 0.195, which is higher than for corn (0.026). [PERSON] et al. (1999) showed that the  
Figure 4: Box plot of metal(loid) accumulation in corn and wheat samples (CBCF and WBCF represent the bioconcentration factors (BCF) values for corn and wheat, respectively). absorption of Cd in corn was much lower than that in wheat [71]. [PERSON] et al. (2011a) found that Cd had little enrichment capacity in corn seeds and was more easily absorbed by crops in a reduction environment [23]. Additionally, long-term fertilization gradually reduced the effectiveness of Cr and Ni, with the unstable form (exchangeable state and carbonate binding state) being transformed into a more stable form (Fe-Mn oxide binding and organic binding fractionation), resulting in reduced Cr migrating into crops [70]. Comparing the BCF values for each element shows that the metal(loid)s content in corn is small. First of all, this is due to the biomass of the non-seed parts of corn plants grown in dryland soil being large. Because the biological dilution effect is strong, metal(loid)s in crops are generally distributed as follows: Roots \(>\) stem leaves \(>\) seeds. Plant absorption of metal(loid) content is approximately 20% to 40% of the total crop uptake, with corn seeds' uptake being the lowest [23; 72]. Secondly, this is due to variation in species, e.g. cabbage was determined to have a higher bioconcentration than garlic and Leping radish for metal(loid)s [12], [PERSON] et al. (2018) also reported a higher and more sensitive bioconcentration of wheat than corn [8], which indicated that strains of each type accumulate very differently for metal(loid)s.  
In a word, the BCF value of metal(loid)s showed that some differences between wheat and corn in the study area depend not only on the soil properties, but also on the biological characteristics of a species and variation between species.
# Dimensional Tolerances in Mechanical Assemblies: A Cost-Based Optimization Approach
## 2 Applicable Concepts and References Using Similar Proposal Approaches
### The Cost Approach
#### 2.3.1 Manufacturing Costs  
The manufacturing cost of a product is influenced by various factors, including material, facilities, labor, and parts design specifications [6]. Dimensional tolerances play an important role in this regard, as the cost of manufacturing increases with a tighter specified precision [7]. It is widely accepted that tighter tolerances lead to higher production costs [8; 9; 10]. This cost increase is particularly evident below a certain tolerance value, as illustrated in Figure 2.  
Manufacturing costs associated with tolerances vary depending on the production site and are typically treated as proprietary information that is not publicly disclosed. In practical applications, the cost-tolerance relationship is often represented by multiple linear functions, as exemplified in Figure 2, derived from discrete tabular values.  
Several references have been chosen to illustrate the various ways in which the cost of dimensional tolerances can impact a product, encompassing aspects such as design, manufacturing, and application. For example, [PERSON] et al. proposed considering components as a function of the product's operating costs. They demonstrated the impact of widening manufacturing tolerances on the aerodynamic behavior of an aircraft nacelle to reduce costs [11]. [PERSON] et al. introduced the concept of activity-based tolerance allocation, which involves evaluating tolerance costs based on design and process selection activities [12]. [PERSON] et al. explored the use of the skew normal distribution optimization strategy (SNDOS) as an approach to address tolerance costs. They provided an example that highlights the reduction in car seat rework rates. It is worth noting that normal distributions are commonly assumed to be symmetric [13].  
[PERSON] conducted a comprehensive review of the tolerance cost functions employed in tolerance synthesis processes. The author emphasized the importance of appropriately selecting parameters within cost functions to avoid inconsistent results [14]. On a related note, [PERSON] et al. proposed a cost optimization approach that considers interrelated key characteristics (KCs) [15].