esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation of the Hybrid Method
### Scattered Fields Generated by the Mutual Coupling between Two Regions  
In this paper, we suppose that the asymptotic region is located at the far field area of the DGTD region. The secondary scattered field includes two components. For the first term \(\overset{\rightarrow}{E}_{\text{DGTD}\rightarrow\text{TDPO}}\left( \overset{\rightarrow}{R},t\right)\), we defined \(\overset{\rightarrow}{H}_{\text{DGTD}}(r,t)\) as an incident field of the TDPOregion. We combine it with Equations (5), (9) and (11) and obtain the coupling term: \(\overset{\rightarrow\text{scat}}{E}_{\text{DGTD}\rightarrow\text{TDPO}}\left( \overset{\rightarrow}{R},t\right)\):  
\[\overset{\rightarrow\text{scat}}{E}_{\text{DGTD}\rightarrow\text{TDPO}} \left(\overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc} \iint_{S^{\prime}}\frac{\partial 2\alpha\times\overset{\rightarrow}{H}_{\text{DGTD}}^{\text{scat}} \left(\overset{\rightarrow}{R},t-\left(\left|\overset{\rightarrow}{R}_{\text{GP} }\right|-\left|\overset{\rightarrow}{R}_{\text{P}}\right|\right)/c\right)- \left(2\alpha\times\overset{\rightarrow}{H}_{\text{DGTD}}^{\text{scat}}\left( \overset{\rightarrow}{R},t-\left(\left|\overset{\rightarrow}{R}_{\text{GP}} \right|-\left|\overset{\rightarrow}{R}_{\text{P}}\right|\right)/c\right) \cdot\overset{\rightarrow}{\delta_{R}}\right)\overset{\rightarrow}{\delta_{R}} }{\partial t}ds \tag{15}\]  
where \(\overset{\rightarrow}{R}_{\text{GP}}\) is the distance between elements in the DGTD region and the TDPO region, as well as \(\overset{\rightarrow}{R}_{\text{P}}=\left|\overset{\rightarrow}{R}-\overset{ \rightarrow}{R}\right|\). Then, (16) represents the integral in (15) numerically.  
\[\overset{\rightarrow\text{scat}}{E}_{\text{DGTD}\rightarrow\text{TDPO}} \left(\overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc}. \tag{16}\]  
Similarly, \(\overset{\rightarrow\text{scat}}{E}_{\text{TDPO}\rightarrow\text{DGTD}} \left(\overset{\rightarrow}{R},t\right)\) is an inverse process of \(\overset{\rightarrow\text{scat}}{E}_{\text{DGTD}\rightarrow\text{TDPO}} \left(\overset{\rightarrow}{R},t\right)\).  
\[\overset{\rightarrow\text{scat}}{E}_{\text{TDPO}\rightarrow\text{DGTD}} \left(\overset{\rightarrow}{R},t\right)\approx-\frac{\eta_{0}}{4\pi Rc}. \tag{17}\]  
where \(\overset{\rightarrow}{R}_{\text{PG}}\) is the distance of elements between the TDPO region and DGTD region, and \(S_{G}\) is the NFFF boundary.  
According to the above equations, the DGTD region and the asymptotic region can be calculated separately, and then the scattering field generated by the coupling between the two regions can be calculated by using Equations (16) and (17), and finally the total scattering field of the whole solution region can be obtained through Equation (1).
# Resource-Aware ECG Classification with Heterogeneous Models in Federated Learning
## 3 Materials and Methods
### ECG Classification Model  
In this study, we utilized a 1D CNN ECG classification model [39], shown in Figure 2, as the classifier in our heterogeneous FL framework. CNNs, adapted with 1D convolutional layers for ECG time series data, are highly effective in extracting features such as peaks and valleys, achieving high accuracy in arrhythmia classification. In this model, each ECG segment is treated as a 1D signal with a length of 187, captured from raw ECG data. The CNN has three 1D convolutional layers that apply a set of learnable filters sliding across the data to extract local features, allowing the network to identify significant characteristics like peaks and troughs, which help differentiate between various heartbeats or arrhythmias. The first two convolutional layers are followed by a max pooling layer, which selects the maximum value within a specified window from the input feature map, enhancing feature selection by reducing dimensionality while retaining important information. The last convolutional layer is followed by an average pooling layer, which takes the average of the values within the window, providing a more generalized representation of the features. To learn complex patterns in the data, the ReLU activation function is applied to all convolutional layers, introducing non-linearity to improve model performance. Finally, a fully connected layer maps the extracted features to a probability distribution over the number of heartbeat classes, allowing the model to classify each ECG segment accordingly. Together, these layers enable the CNN to efficiently extract meaningful information from the ECG signal, reduce noise, and enhance its ability to classify different types of heartbeats accurately.
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 5 Conclusions  
Aimed at the time synchronization problem of multiple sensor nodes in the high-density seismic array exploration method, we propose an energy-efficient time synchronization system that uses GPS and a high-precision TCXO based on the GEIWSR-III system in this paper. For energy-efficient design, we used the way that GPS works intermittently, TCXO provides time service continuously. We can save 73% of the energy compared to continuous GPS timing. After time-stamping the seismic data, we record accurate time, which is based on a strict time series in the SD card so as to intercept seismic data when seismic data is collected.  
After analyzing the performance of the time synchronization method, it is found that the synchronization accuracy depends only on the timing accuracy of the GPS and the clock cycle of  
Figure 15: Field measurement with the sensor nodes in equal intervals. (**a**) The sensor nodes are deployed in second field experiment and (**b**) the waveform of the recorded seismic data.  
the acquisition system. When the clock synchronization is stable, the sensor node can achieve a synchronization accuracy of about 0.6 \(\upmu\)s. Synchronization stability tests and the field measurements also prove that the time synchronization method has strong practical application ability, which meets the requirements of efficient acquisition tasks for high-density seismic array survey. In future work, we will produce more sensor nodes and further verify the performance of this time synchronization method in a wide range of exploration work.  
[PERSON], H.Y., and L.Z.: conceptualization conceptualization: [PERSON], J.Z., and H.Y.: conceived and designed the instruments; [PERSON], S.Z., Y.C., L.W., and H.Y.: performed the experiments; [PERSON], J.Z., Y.C., and Y.J.: validation and formal analysis; [PERSON]: writing--original draft preparation; [PERSON], [PERSON], and H.Y.: writing--review and editing; [PERSON], H.Y., and L.Z.: project administration. All authors have read and agree to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2018 YFC0603204), the Ph.D. Interdisciplinary Research Funding Project of Jilin University (Grant No. 10183201836), National Natural Science Foundation of China (Grant No. 41204069), New Energy Special Project of Jilin Province-University Construction Special Project (Grant No. SXGJSF2017-5), and Research on Key Techniques of Micro Seismic Joint Electrical Monitoring for Improving Production of Oil and Gas in Hydraulic Fracturing of Jilin Provincial Department of Science and Technology, Key Scientific and Technological Project (Grant No. 20160204068 CX).  
Any opinions, findings, conclusions and recommendations expressed in this paper are those of the authors. Authors would also like to thank anonymous reviewers who have provided many helpful comments on the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
Background: Digital bite measurement systems such as the T-Scan III allow for the computerized measurement of occlusal force distribution. This study aimed to establish the relationship between dentofacial vertical pattern and bite force distribution among children in late mixed dention. Materials and Methods: In total, 86 children (45 male, 41 female) aged between 9 and 11 years with short (\(n=28\)), medium (\(n=28\)), and long (\(n=30\)) facial heights were included in this study. The height, weight, age, and gender were recorded. Occlusional bite force distribution and time of occlusal cycle were recorded using a T-Scan III device (Tekscan Corp. Boston, MA, USA). The bite force distribution was compared among facial types using a One-Way ANOVA and post hoc test, a linear regression model with time of occlusion as dependent variable was developed. Results: No significant differences were observed in occlusion time between genders. Children with long facial height had a significantly lower anterior bite force distribution (\(p<0.05\)) and significantly higher posterior bite force distribution (\(p<0.05\)) than those with average or short facial height. Age, gender, height, and weight had no significant association with time of the occlusal cycle. Conclusion: Children with an increased vertical facial height have a more posterior distribution of force than children with average or short facial heights in the late mixed dention.  
occlusion; vertical facial height; T-scan III; digital occlusal analysis +
Footnote †: journal: Applied Sciences
## 3 Experiments and Results
### The Interactive Performance of the Developed Approach  
The detection speed on the targets, which determines the harvesting efficiency of robots, is very crucial for robotic harvesting. To determine whether the developed method could be used in harvesting robots, the elapsed time of the whole algorithm was computed using the clock function in Matlab, and the experiments were conducted on the 100 images that were randomly chosen from the captured images. The elapsed time of these images was showed in Figure 12. The result indicated that the elapsed time of every image was less than 0.59 s, and the overall processing time varied approximately from 0.43 s to 0.59 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence Number of Grape Clusters** & **The RPA of Paper [18]\%** & **The RPA of the Proposed Method/\%** \\ \hline
1 & 91.27 & 93.43 \\
2 & 86.82 & 92.86 \\
3 & 88.76 & 93.73 \\
4 & 90.57 & 89.23 \\
5 & 91.75 & 89.75 \\
6 & 90.26 & 91.26 \\
7 & 92.27 & 95.23 \\
8 & 89.78 & 88.95 \\
9 & 86.53 & 89.26 \\
10 & 87.36 & 91.23 \\
11 & 89.26 & 93.69 \\
12 & 93.45 & 92.26 \\
13 & 88.36 & 87.63 \\
14 & 91.59 & 94.45 \\ \hline
**Average value** & **89.86** & **91.49** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The PFR comparison of the proposed method and the method [18].  
Figure 11: The comparison of the detection process between the method [18] and the proposed method, (**a**) Original image; (**b**) Initial segmentation image by the method [18]; (**c**) Initial segmentation image by the proposed method; (**d**) The detected contour of the grape clusters by the method [18]; (**e**) The detected contour of the grape clusters by the proposed method; (**f**) The detection result by using the method [18]; and (**g**) The detection result by using the proposed method.
## 3 Data Processing & Methodology
### Methodology  
This section describes the procedure that is used in deriving the receiver clock differences between the two receivers Septentrio and Ublox. Both the receivers record the differences with respect to the GPST. \(Refsys_{Sept}\) denotes the differences between the data from Septentrio and GPST, and similarly, \(Refsys_{Ubx}\) denotes the differences between the data from Ublox and GPST. Here, we see that the GPST is the common time reference between the two receivers. If this common GPST is subtracted, we can compute the differ  
Figure 5: Results (**a**) before and (**b**) after synchronization to the external reference. The legend in each figure represents the condition. The graph with red dots is for “before” and the graph with blue dots is for “after” the synchronization.  
ences between the Septentrio and the Ublox receiver. This can be done by following either of the two approaches: _common view method_ and _all-in-view method_[15; 16].  
Figure 4 shows that there are two satellites (GPS satellites A and B) that are viewed from both the receivers at that instant, whereas only GPS satellite C is viewed by the Ublox receiver. As the name suggests, if only common satellites (in this case A and B) are used, then it is termed as common view. If the data from all satellites are averaged first and then the difference is calculated, it is termed the all-in-view method. For this paper, the common view method will be implemented. However, there are not many differences between the two methods for the context of this paper as the distance between the two receiver locations is very short. The same satellites are visible almost all the time by both the stations.
## 2 Materials and Methods
### Hydrographic Measurements  
Searwater samples were collected using a rosette water collector. Temperature, salinity, and depth data were measured in situ with a Hydro-bios@MWS6 conductivity-temperature-depth (CTD) recorder. The data were recorded every three hours and monitored continuously for 24 hours. pH was measured with an ORION Ross-type combination electrode, which was calibrated on the NBS scale. The measurement precision was \(\pm\)0.01 pH units. Total alkalinity (TA) was calculated using the TA-salinity relationship (equation 1), which was acquired by averaging the slopes and intercepts of the TA-salinity relationships in Table 1. The partial pressures of CO\({}_{2}\) (\(p\)CO\({}_{2}\)) and dissolved inorganic carbon (DIC) were calculated from pH and TA using the program CO2 SYS [28].  
\[\text{TA ($\mu$mol kg${}^{-1}$) }=(13.38\pm 0.15)\text{S}+(1788.40\pm 32.63) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Date** & **Sampling Area** & **Correlation** & **Reference** \\ \hline
27 August 2013 & 31–31.5\({}^{\circ}\) N, 121.5–124\({}^{\circ}\) E (with a salinity of 5.17–34.26) & TA = 13.3507S + 1797.39 & [7] \\ August 2009 & 31\({}^{\circ}\) N, 122.5–125\({}^{\circ}\) E (Transect C) & TA = 13.2S + 1744.7 & [29] \\
8–27 April and 2–7 May 2007 & 30.0–31.8\({}^{\circ}\) N, 122.5–123.5\({}^{\circ}\) E (with a salinity of 13.00–34.49) & TA = 13.5875S + 1823.1 & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of correlation between total alkalinity (TA, \(\mu\)mol kg\({}^{-1}\)) and salinity.  
Figure 1: Map of Changjiang River estuary plume (**a**) and sampling stations (**b**): stations M4-1, M4-8, and M4-13 denote the near-shore, front, and offshore regions, respectively.
## 1 Introduction  
With the emergence of thousands of terminals, more communication resources are needed to demand users' applications, such as interactive gaming, VR, etc. Traditional radio frequency (RF) communication methods will not satisfy those new demands anymore. Free space optical (FSO) communication systems are commonly used as one of telecommunications' 'last mile' or next-generation mobile communication technology [1] because the characteristics of large capacity, fast network construction, license-free, and high security of free space optical (FSO) communication systems. However, the performance of ground FSO communication systems is greatly affected by atmospheric channels, such as the scattering effect of atmospheric particles (such as clouds, fog, rain, snow, aerosols, etc.) on the beam of light, causing attenuation of the light intensity on the receiver plane [2]. Atmospheric turbulence can cause scintillation, phase fluctuation, beam expansion, and beam wander on the receiver plane [3]. These all lead to limitations in the FSO communication, such as poor visibility and short transmission distance.
## 3 Results and Discussion
### Evaluation Metrics  
The performance of the model was evaluated using four metrics: precision (\(P\)), recall (\(R\)), and mean average precision (mAP). The formulas for calculating precision and recall are shown as follows:  
\[P=\frac{TP}{TP+FP},R=\frac{TP}{TP+FN} \tag{21}\]  
where \(TP\) represents the number of targets that the model correctly detected, \(FP\) represents the number of non-targets that the model incorrectly detected as targets, and \(FN\) represents the number of actual targets that the model failed to detect.  
The mAP is an indicator of the average AP (average precision) value across all categories, which is used to comprehensively evaluate the performance of the model in multi-class target detection tasks. The formula for calculating mAP can be expressed as follows:  
\[AP=\int_{0}^{1}P(R)dR;mAP=\frac{1}{m}{\sum_{i=1}^{m}AP_{i}} \tag{22}\]  
where \(m\) represents the number of classes, with \(m\) being set to three in this case. \(AP_{i}\) denotes the average precision for the \(i-th\) class. mAP\({}_{0.5}\) represents the total class average accuracy at an IoU threshold of 0.5, mAP\({}_{0.75}\) represents the total class average accuracy at an IoU threshold of 0.75, and MAP\({}_{0.50.95}\) represents the average accuracy at different IoU thresholds, which range from 0.5 to 0.95 in increments of 0.05.
## 2 Literature Review
### Background
#### 2.1.1 Sparse Matrix-Vector Product (SpMV)  
The SpMV kernel is one of the seven known categorized dwarfs of numerical methods, which have significant importance [15]. Sparse matrices are mostly populated by zero values. The SpMV kernel is formally denoted by:  
\[\text{y}=\text{Ax} \tag{1}\]  
where A is a multidimensional sparse matrix, x is the input dense vector, and Y is the resulting output dense vector (see Table 1). The dense and sparse matrix-vector products are shown in Figure 2.  
Unlike dense matrices, sparse matrices require an explicit representation of the coordinates of non-zero elements and their manipulation in the sparse matrix-vector product demand memory bandwidth. The loop structure of the sparse matrix-vector product is more irregular than that of the dense matrix, which may lead to a less optimized compiler-generated code. The different data structures resulted in many indirect memory accesses that add performance bottlenecks by having additional load operations and poor compiler optimization. An efficient matrix-vector product involves multiple aspects, that is, the matrix pattern, to determine if the number of elements in each row varies strongly from row to row, if the sparse matrix is near a dense matrix or not. Knowing the matrix pattern, it is easier to find the most suitable storage scheme. Another important factor is the effective utilization of cache. The data distribution strongly depends on the matrix pattern. The use of pointers to exploit the sparsity of the matrix is unavoidable, as they lack spatial locality  
\begin{table}
\begin{tabular}{l l l l} \hline \hline Name & Symbols & Name & Symbols \\ \hline Nnz & Number of nonzeros in the matrix. & A & M \(\times\) N input matrix \\ M & Number of Rows & N & Number of Columns \\ X & N \(\times\) 1 dense vector & Y & M \(\times\) 1 Output vector \\ N & Number of matrices & F & Sparse Matrix features \\ SpMV & Sparse matrix–vector multiplication & CSR & Compressed Sparse Row \\ nnz\({}_{b}\) & Number of non zeros per block & Nb & Dimension of each block \\ BCSR & Block Compressed Sparse Row & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol used in the paper.  
Figure 2: Dense vs. sparse matrix comparison.  
and lead to poor cache utilization. An irregular sparse structure leads to a number of cache misses and memory indirection, or the use of pointers requires extra load operations.
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 3 Network Model and Assumptions
### Signal Channel Model  
Due to the complex environment, there is dense occlusion in the wireless signal's propagation that will lead to a multi-path effect. This effect produces much uncertainty in the receiving signal's strength and the link quality. Due to the small-scale effect of multi-path propagation, it can be considered that the signal attenuation of the link between nodes conforms to the following channel model [28; 29]:  
\[PL=10n\lg d+\mathbf{X} \tag{5}\]  
where \(PL\) represents the signal attenuation of the link, \(n\) is the environmental attenuation coefficient, \(\lg\) is a base-10 logarithm, \(d\) is the distance between transceiver nodes, \(A\) is the constant in the model, and \(\mathbf{X}\) represents the random component of small-scale fading.  
A TDMA method is adopted in this paper in order to reduce the interference between nodes. So, the small-scale fading that is caused by environmental noise is considered. For any node \(\mathbf{N}_{i}\), assume that it has \(k\) neighbor nodes, \(\mathbf{NB}_{1}\), \(\mathbf{NB}_{2}\),... \(\mathbf{NB}_{K}\), and the link connectivity probabilities are \(Lqc_{1}\), \(Lqc_{2}\),... \(Lqc_{m}\), respectively [30].  
\[Lqc=\left\{\begin{array}{c}1,\;if\;(PER=0)\\ (1-0.5\cdot e^{-\frac{2}{2}}\frac{1}{\cosh})^{\mathbb{N}},\;if\;(0<PER<1)\\ 0,\;if\;(PER=1)\end{array}\right. \tag{6}\]  
where \(\gamma\) is the signal-to-noise ratio (SNR) at the receiving end, \(PER\) is the package error rate at the receiving end, and \(l\) is the length of the data packet.  
Then, the average link quality of node \(i\) could be obtained as:  
\[Lqc(i)=\frac{1}{m}\frac{1}{1}Lqc_{i} \tag{7}\]A retransmission scheme is adopted in the network. A packet will be retransmitted when the link quality is poor. In this case, the amount of network transmission data will increase in addition to the network power consumption when the link quality decreases.  
A hybrid routing algorithm based on Naive Bayes and improved particle swarm optimization algorithms (HRA-NP) is proposed in this paper. HRA-NP has the advantages of a simple topology and easy-to-maintain cluster routing. This would improve the energy efficiency of data collection. At the same time, the multi-hop between cluster heads method is adopted to eliminate the energy consumption gradient and energy consumption funnels of remote cluster heads, which makes it more suitable for medium and large-scale network application scenarios [31]. The network diagram of the hybrid routing algorithm is shown in Figure 1.