esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation
### Network Model  
A network-based FlexE interface can be represented via a directed graph \(G=(V,E,L,B)\), where \(V=\left\{v_{1},v_{2},\cdots,v_{N_{V}}\right\}\) represents the set of nodes, \(|V|=N_{V}\) indicates that the number of the nodes is \(N_{V}\), and \(E=\left\{e_{ij}|v_{i},v_{j}\in V\right\}\) represents the set of links in the network. Also, the adjacent nodes are connected by two links in opposite directions; for example, \(e_{ij}\) denotes the link from node \(i\) to node \(j\), and \(e_{ij}\) denotes the link from node \(j\) to node \(i\). The link length sets are defined as \(L=\left\{l_{e_{ij}}\middle|v_{i},v_{j}\in V\right\}\), and \(B=\left\{b_{e_{ij}}\middle|v_{i},v_{j}\in V\right\}\) represents the link bandwidth sets in the FlexE.
# Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics
## 3 Parameter Design and Optimization of Basic Nozzle Unit  
### Evaluation of Outlet Velocity Uniformity  
The uniformity of the flow field velocity distribution can be evaluated by various methods. To ensure accurate assessment, this paper employs both the relative standard deviation and the uniformity index to determine the uniformity of the outlet velocity [26].  
(1) Coefficient of variation  
The coefficient of variation (CV) characterizes the measure of relative variability, and it is a dimensionless value. It can be utilized to compare the dispersion of populations with significantly different means and to assess the improvement in uniformity of a flow field. Comparing \(CV\) values under different conditions allows for assessment of the uniformity of the flow field on the cross-section. A smaller \(CV\) value indicates higher uniformity in the flow field.  
(2) Uniformity Index  
The uniformity index, established by [PERSON] et al. [27] in the early 1990s, is a flow field velocity uniformity evaluation metric. It is based on the definition of statistical deviation and comprehensively reflects the characteristics of fluid velocity distribution across the entire flow section. This index is notable for its strong comparability and wide applicability.  
\[\gamma_{v}=1-\frac{1}{2n}\sum_{j=1}^{n}\frac{\sqrt{\left(v_{j}-\overline{v} \right)^{2}}}{\overline{v}}, \tag{1}\]  
where \(\gamma_{v}\) takes values between \([0,1]\). A larger value of \(\gamma_{v}\) indicates better flow uniformity, 1 represents an ideal state of uniform flow, and 0 represents a hypothetical condition where the fluid flows only from a single measurement point. Both extreme cases are assumed conditions and do not occur. \(v_{j}\) is the velocity at a measurement point, and \(\overline{v}\) is the average velocity on the measurement cross-section.
## 3 Research Process
### Research Methodology
#### 3.3.1 Topic Recognition  
The purpose of topic recognition is to lay the foundation for the subsequent mining of association relationships based on topics. However, traditional text topic extraction methods such as TF-IDF and Text Rank have certain limitations. They cannot effectively distinguish the strength of the relationships between topics, resulting in difficulties in accurately revealing the associations between topics. Therefore, this paper adopts the Latent Dirichlet Allocation (LDA) model [27] to conduct topic extraction on the text corpus.  
The accuracy of LDA topic extraction is largely dependent on the number of topics [10]. Consequently, it is essential to determine an appropriate number of topic clusters prior to training the LDA model, as this can enhance the accuracy of the LDA topic model. To find the optimal number of topics, this paper adopts the perplexity evaluation method to determine the model parameters. Perplexity is a measure of the ability of a topic model to predict samples, representing the uncertainty of the topics to which a specific document belongs in the trained model [34]. Generally, when the changing trend of perplexity no longer decreases or reaches an inflection point, the current perplexity value is the optimal number of topics.
## 4 Energy-Aware Routing Protocol  
Since cluster heads consume more energy than cluster members in receiving sensed data from their member nodes, performing signal processing functions on the data (e.g., data aggregation), and sending the aggregated data to the next hop node or base station, the role of the cluster head must be  
Figure 1: Example for cluster head election.  
rotated among all sensor nodes. Therefore, the operation of EAP is divided into rounds as LEACH. Each round begins with a set-up phase while clusters are organized and the routing tree is constructed, followed by a working phase when data are sent to the sink node. For easy reference, we describe the states of nodes and control message in Table 1.
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 6 Experimental Results
### Experiment I: Fingerprint Dynamic Texture  
This experiment aims to investigate the dynamic fingerprint texture, specifically caused by pressure, as PAD features. The experiment is conducted following Table 7.  
The classification accuracy of the proposed PAD mechanism, using all feature extractors and different parameters, is reported as BPCER at APCER = 5% and APCER = 2.5% in Figure 5. Moreover, The PAD testing scores are expressed as functions of the decision threshold, i.e., DET curves, considering all feature extraction algorithms in Figures 6 and 7.  
Figure 6: Detection Error Trade-offs (DET) curves for the five feature extractors (optical sensor).  
Figure 7: DET curves for the five feature extractors (optical sensor).  
The most desirable result to emerge from the figures is achieving low values for the pair (APCER | BPCER). For instance, at 5% APCER, BPCER values are 0% for the optical sensor using \(LBP\)-\(TOP_{1,8,1}\), and 1.66% for the thermal sensor using \(GIST3\)-\(D\). While at 2.5% APCER, BPCER values are: 1.11% for the optical sensor using \(LBP\)-\(TOP_{2,8,1}\) and 2.22% for the thermal sensor using \(GIST3\)-\(D\).  
In order to examine the PAD subsystem capability of detecting different PAI species, \(APCER_{PAI}\) for the seven attack species is reported at \(APCER_{total}\) = 5% in Tables 8 and 9. The tables list the distribution of the 5% \(APCER_{total}\) on the seven PAI species. It is interesting to note that despite the similarity in DET curves, the \(APCER_{PAI}\) distribution might differ notably. For example, considering the optical sensor, the spray rubber species has an APCER = 2.22% when \(VLPQ_{3\times 3}\) is used, however, APCER raises to 18.89% using \(VLPQ_{9\times 9}\).  
The \(APCER_{PAI}\) distribution confirms a significant difference in the PAD subsystem vulnerability to the different attack species. Meaning that the PAD subsystem, at a certain threshold, might have the capacity of eliminating some PAI species, as shown in Tables 8 and 9, nevertheless, the results demonstrate its vulnerability to other species.  
For both sensing technologies, the PAD mechanism had been able to obtain BPCER values lower than 2.5% at APCER = 2.5%. This implies that both security and ease of use aspects have been achieved without the need to compromise one over the other. The following experiments further investigate the effectiveness and generalizability of spatio-temporal features.
## 3 Results
The total numbers of LUTs are 53,200, FFs are 106,400, and BRAMs are 140 in the ZYNQ-7020 chip. The TDC core requires 522 LUTs, 1080 FFs, and 4 BRAMs. This TDC core needs many resources because it works at 350 MHz. Table 5 shows the power of the TDC system. These data are obtained from the implementation power report. The total dynamic power is 1.601 W and the main dynamic power comes from the PS; therefore, the dynamic power of the TDC is 0.202 W. The total static power is 0.138 W, which comes from the TDC. In other words, the total power consumption of the TDC is 0.340 W. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **LUTs (53200)** & **FFs (106400)** & **BRAMs (140)** \\ \hline TDC core & 522 & 1080 & 4 \\ One channel TDC system & 1551 & 2313 & 4 \\ Two channel TDC system & 2513 & 4095 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The resource utilization of the TDC system. Figure 14: RMS precision of difference frequencies. \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Worst Negative Slack** \\ \hline Setup & 0.348 ns \\ Hold & 0.068 ns \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The timing of the TDC system. Table 6 provides a comparison with other TDCs based on similar principles. In terms of the precision, this paper is better than reference 22 and 25. For the sample rate, this paper is better than all references: a 19 ps precision and 170 M samples/s TDC implemented and evaluated in Zynq-7000 (programmable logic is equivalent to a 28 nm Artix-7 FPGA). The proposed TDC offers higher precision and a higher sample rate than that TDC from reference 25. In addition, the sample rate is verified by the experiment.
# A Study of Forest Phenology Prediction Based on GRU Models
###### Abstract  
Investigating forest phenology prediction is a key parameter for assessing the relationship between climate and environmental changes. Traditional machine learning models are not good at capturing long-term dependencies due to the problem of vanishing gradients. In contrast, the Gated Recurrent Unit (GRU) can effectively address the problem of vanishing gradients and allow the neural network to capture longer-range dependencies. In this study, an optical camera was used as experimental equipment to obtain forest images. The absolute greenness index (GEI) data of the region of interest (ROI) in the images were calculated to fit the seasonal variation curve of forest phenology. The GRU neural network model was introduced to train and analyze the GEI data, and the performance of the GRU neural network was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) methods. Finally, the model was used to predict the trend of GEI data for the next 60 days. The results showed that: (1) In terms of training and predicting forest phenology, the GRU model was validated using histograms and autocorrelation graphs, which indicated that the distribution of predicted data was consistent with the trend of actual data, the GRU model data was feasible, and the model was stable. (2) The MSE values of the GRU model at rain-fed-CK (preset point 1), sufficient drip irrigation-DIFI (preset point 3), and sufficient furrow irrigation-BIFI (preset point 5) were 9.055 \(\times\)\(10^{-5}\), 12.91 \(\times\)\(10^{-5}\), and 8.241 \(\times\)\(10^{-5}\), respectively. The RMSE values were 9.516 \(\times\)\(10^{-3}\), 11.36 \(\times\)\(10^{-3}\), and 7.313 \(\times\)\(10^{-3}\), respectively. The MAE values were 7.174 \(\times\)\(10^{-3}\), 8.241 \(\times\)\(10^{-3}\), and 5.351 \(\times\)\(10^{-3}\), respectively. These results indicate that the overall performance of the GRU model was good. (3) The predicted GEI data for the next 60 days showed a trend consistent with actual changes in GEI data, as demonstrated by the GRU model. The GRU model has become the preferred method for phenological prediction due to its simple internal structure and relatively short training time. Results show that the GRU model can achieve forest phenological change prediction and can reveal in-depth insights into future forest growth and climate change, providing a theoretical basis for the application of forest phenological prediction.  
green excess index; Gated Recurrent Unit model; prediction; forest phenology 2023
## 2 The Measurement Partitioning Problem  
An integral part of the update equation requires the partitions of the measurement set, i.e., the set of measurements needs to be partitioned into different partitions at each time step and each partition contains several subsets with the measurements understood to be originated from the same target or clutter. For MCETT in noise and clutter environments, the result of measurement partitioning would greatly affect the stability of subsequent tracking algorithm and the estimation accuracy of both the target state and number.  
In the framework of the ET-PHD filter, all possible partitions of the measurement set are needed in the measurement pseudo-likelihood, which makes the whole filtering computationally challenging with the increase of the partitions' number [27]. However, there are many unsuitable subsets in a large part of the partitions, contributing little to the tracking performance. Only several partitions contain the most likely subsets. Thus, these partitions can be used to approximate the whole partitions.
## 1 Introduction  
Noise pollution in urban areas has become a growing concern due to rising population density, urban expansion, and increased vehicular traffic. Numerous studies have confirmed that prolonged exposure to high noise levels can have significant adverse effects on human health, impacting multiple bodily systems. For example, constant exposure to noise--especially at night--can lead to sleep disorders, increasing the risks of cardiovascular disease, hypertension, and stroke [1, 2].  
Moreover, it has been demonstrated that long-term noise exposure can cause chronic stress, negatively affecting both physical and mental health. Recent studies indicate that continuous exposure to urban noise is associated with psychological disorders such as anxiety and depression, due to the sustained activation of biological mechanisms related to stress. Additionally, disruptions to circadian rhythms caused by nighttime noise exacerbate these negative effects [3].  
Against this backdrop, mitigating urban noise becomes a key priority for public health policies and urban planning. The accurate prediction of acoustic levels would allow for the implementation of more effective strategies, such as sound barriers, dynamic traffic regulation, or improvements in urban design, thereby reducing population exposure and improving well-being [4].  
Despite the recognized importance of this issue, there is a clear lack of research leveraging recent deep learning techniques for the spatiotemporal prediction of urban noise. In particular, the use of Graph Convolutional Networks (GCNs) to simultaneously model the spatial and temporal relationships present in urban noise remains limited.  
In this context, the present study addresses this gap through the development and comparative evaluation of deep-learning-based models--CNN, LSTM, and particularly GCN--to predict noise pollution in a densely populated city such as Madrid, Spain. The specific choice of this analysis period allows for the evaluation of exceptional events such as the COVID-19 pandemic, as well as recurring patterns associated with holidays and weekends, and their impact on urban noise levels. The goal is not only to anticipate acoustic patterns but also to provide a deeper understanding of how these levels vary under different temporal and social conditions.  
This study contributes to current knowledge by explicitly evaluating the effectiveness of different deep learning models and proposing an integrated methodology that can be adapted to other cities facing similar noise-related challenges.
# Comparison of \({}^{210}\)Pb Age Models of Peat Cores Derived from the Arkhangelsk Region
## 3 Results and Discussion
### Chronology \({}^{210}\)Pb and Rate of Peat Accumulation
#### 3.3.2 Application of the CF/CS Model  
The values used in the constant flux/constant sedimentation model were the cut depth (\(z_{i}\)), the average mass depth (\(m_{i}\), g\(\cdot\)cm\({}^{-2}\)), and the logarithm \({}^{210}\)Pb\({}_{\text{ex}}\) (In C\({}_{i}\)). We executed the expression as \(y=a+bx\), where \(y=\ln\) C\({}_{i}\) and \(x=m_{i}\). The equation \(lnC_{i}=lnC_{0}-\frac{\lambda}{r}m_{i}\) shows that \(r=-\frac{\lambda}{b}\), with uncertainty defined as \(u(r)=r\sqrt{\left(\frac{u(\lambda)}{\lambda}\right)^{2}+\left(\frac{u(b)}{b} \right)^{2}}\).  
Our linear equation (R = 0.88) gives b = \(-5.129\pm 0.613\), meaning that \(r\) = 0.006 \(\pm\) 0.001 g\(\cdot\)cm\({}^{-2}\).year\({}^{-1}\) At this point and later in the text, the mass accumulation rate (\(r\), g\(\cdot\)cm\({}^{-2}\).year\({}^{-1}\) was denoted as MAR, and the sediment accumulation rate (\(s\), cm\(\cdot\)g\({}^{-1}\)) was denoted as SAR. Also, SAR could be estimated via a linear equation using the cut depth (\(z_{i}\)) instead of the depth of mass (\(m_{i}\)). Then, \(=-\frac{\lambda}{b}\), and its uncertainty was defined as \(u(s)=s\sqrt{\left(\frac{u(\lambda)}{\lambda}\right)^{2}+\left(\frac{u(b)}{b} \right)^{2}}\).  
The linear regression (R = 0.91) gives \(b\) = \(-0.222\pm 0.023\), meaning that \(s\) = 0.140 \(\pm\) 0.015 cm/year. As can be seen in Figure 5 and Table 3, while the correlation coefficient is good, a simple regression line does not show the variations in the parameters in the profile, suggesting that the graph should be broken down into linear segments. Figure 5 shows one mass accumulation rate of the profile (Figure 5a) and three mass accumulation rates of individual line segments (Figure 5b). Figure 5b shows for the CF/CS model, activity values below 37 cm have been omitted, as at this level, they reach a value of \({}^{210}\)Pb\({}_{\text{sup}}\). The fitting of a simple regression to the natural logarithm plot of the activity concentration of \({}^{210}\)Pb\({}_{\text{uns}}\) ln (\({}^{210}\)Pb\({}_{\text{uns}}\)) as a function of the depth mass m\({}_{i}\) is shown in Figure 5a. Even if the value of determination (COD) for the whole data set was quite significant, i.e., 0.78, as can be seen from Figure 5, we characterized our model as being of good quality (\(COD=0.8\)). One trend line cannot explain all of the variability in the core.  
As a result, we identified three linear segments in the peat record. The linear regression was fitted three times, resulting in different groups of fitted parameters. These are represented as \"1, 2, 3\" in Figure 5b. All points have been involved in the operations. Figure 5 presents the alternative interpretation by breaking the graph into three regression lines.  
Figure 5: Graphs showing the one (**a**) and three-fold approximations (**b**) performed using the linear regression method for the data set ln (\({}^{210}\)Pb\({}_{\text{uns}}\)) depending on the mass depth \(m_{i}\).
# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
## 3 Model and Optimization
### Backbone Improvement  
Weld seam defects typically vary in size, shape, and texture. EfficientViT, through cascading grouped attention and parameter reallocation, can more effectively capture and integrate features from different scales, enabling the model to better represent and identify subtle variations in defects [31].  
Figure 4: Extended sample sketch.  
EfficientViT's architecture features an efficient sandwich layout, cascaded group attention modules, and parameter redistribution, enhancing memory use, computational efficiency, and parameter allocation.  
The sandwich layout is shown in Figure 5; input features pass through \(\mathcal{N}\) Feed-Forward Networks (FFNs), followed by cascaded group attention, and are then processed by \(\mathcal{N}\) additional FFN layers to produce the output features. This design aims to improve the model's memory efficiency while enhancing computational and parameter efficiency. The calculation formula is as follows:  
\[X_{i+1}=\prod^{\mathcal{N}}\Phi^{\mathrm{F}}_{i}(\Phi^{\Lambda}_{i}(\prod^{ \mathcal{N}}\Phi^{\mathrm{F}}_{i}(X_{i}))) \tag{1}\]  
where \(X_{i}\) represents the full input features of the \(i\)-th block. This block uses \(\mathcal{N}\) FFNs before and after a single self-attention layer \(\Phi^{\mathrm{F}}_{i}\) to transform \(X_{i}\) into \(X_{i+1}\). \(X_{i+1}\) is the result of \(X_{i}\) after being processed by a single self-attention layer.  
The cascaded attention mechanism is the middle module in Figure 5 that improves computational efficiency and increases the diversity of attention by decomposing the full features into multiple subspaces and projecting and accumulating these subspaces. The calculation formulas are as follows:  
\[\widetilde{X}_{ij}=Attn(X_{ij}W^{\mathrm{Q}}_{ij},X_{ij}W^{\mathrm{K}}_{ij},X_ {ij}W^{\mathrm{V}}_{ij}) \tag{2}\]  
\[\widetilde{X}_{i+1}=Concat[\widetilde{X}_{ii}]_{j=1:h}W^{\mathrm{P}}_{i} \tag{3}\]  
\[X^{\prime}_{ij}=X_{ij}+\widetilde{X}_{i(j-1)},\ 1<j\leq h \tag{4}\]  
where in the \(j\)-th attention head, self-attention is computed on \(X_{ij}\), the \(j\)-th partition of the input feature \(X_{i}\), where \(X_{i}\)= \([X_{i1},X_{i2},...,X_{ii}]\) and \(1\leq j\leq h\). The projection layers \(W^{\mathrm{Q}}_{ij}\), \(W^{\mathrm{K}}_{ij}\), and \(W^{\mathrm{V}}_{ij}\) map each partition into different subspaces, while \(W^{\mathrm{P}}_{i}\) projects the concatenated outputs back to the original input dimension. \(X^{\prime}_{ij}\), formed by adding \(X^{\prime}_{ij}\) to the output of the \((\ j-1\ )\)-th head, \(\widetilde{X}_{i(j-1)}\) replaces \(X_{ij}\) as input for the \(j\)-th head's self-attention.  
Parameter reallocation optimizes model efficiency by expanding key network component channels and reducing less critical ones. This redistribution minimizes redundancy, refining summation and projection at each stage based on importance analysis. The strategy improves parameter utilization and enhances overall model performance.  
Figure 5: A memory-efficient sandwich layout.