esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DenseNet-LSTM Model for Epileptic Seizure Prediction
## 3 Proposed Method: DenseNet-LSTM
### Deep Learning Architecture  
#### 3.3.1 DenseNet  
As the network deepens, there is a problem that input or gradient information may vanish when it reaches the end of the network. Various studies are being conducted to solve this problem, and all of them have the feature of making a shortcut from the early layer to the later layer. A densely connected convolutional network, which was introduced at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in 2017 [5], proposed architecture with great advantages in terms of vanishing gradient, reduced computation, and reduced number of parameters through a new concept of dense connectivity that extends this feature. As shown in Figure 5, dense connectivity is a method of continuously connecting the feature map of the previous layer with the input of the later layer to reinforce the information flow between layers.  
DenseNet is composed of dense block and transition layer. The dense block consists of a bottleneck layer and a growth rate. Since the feature maps of different layers in DenseNet are connected using channel-wise concatenation, but it can lead to oversized parameters of the network, which will affect the efficiency of the computation. To avoid oversized parameters' problem, the DenseNet author used the growth rate (=k) as a hyperparameter, also apply the Batch Normalization (BN) -? Rectified Linear Unit (ReLU) -? Conv(\(1\times 1\)) -? Batch Normalization (BN) -? ReLU -? Conv(\(3\times 3\)) nonlinear transformation to the DenseNet structure in order to solve the problem we mentioned before. The bottleneck layer is shown in the Figure 5(a). Additionally, as before, it is used to reduce the number of input feature maps and improve calculation efficiency.  
Figure 4: The process of converting raw EEG signals into time-frequency images using DWT.  
Figure 5: An example of dense connectivity. The square means the input feature maps with several channels. The curve connects the previous feature map with next layer feature map using channel-wise concatenation.  
As shown in Figure 5(b), the transition layer has the role of reducing the width and height size of feature maps and reducing the number of feature maps. It is connected behind the dense block and consists of BN -> ReLU -> Conv(\(1\times 1\)) -> Avg pool(\(2\times 2\)). At this time, it is determined how much to reduce the feature map through the hyperparameter value between 0 and 1 called the compression factor. If this value is 1, the number of feature maps does not change. In addition, DenseNet applied the composite function consisting of the order of BN -> ReLU -> Conv to the layer, citing the efficiency results according to the order of BN, ReLU, and Conv tested in [28].
# Optimizing Retransmission Threshold in Wireless Sensor Networks
## 8 Conclusions  
The retransmission threshold in wireless sensor networks is critical to the latency of data transmitting in the networks. The problem of finding optimal retransmission thresholds for each node along a delivery path is defined and is formalized as an integer optimization problem. A dynamic programming-based distributed algorithm for finding the optimal retransmission threshold is proposed. The correctness of the algorithm is proven, and its time and space complexity are analyzed. When the delivery delay \(\Delta\) is greater than polynomial, a linear programming-based \((1+p_{min})\)-approximation algorithm is proposed. Furthermore, in the case of the ranges of the upper and lower bounds of the retransmission thresholds being big enough, a Lagrange multiplier-based distributed \(O(1)\)-approximation algorithm with time complexity \(O(1)\) is proposed. Simulation results show that the proposed algorithms have better performance for real-time data delivery.
## 3 Conclusions
It can also be concluded that the high DVB content (1:5 DVB/St ratio) does not prevent either the incorporation of the MA-PDMS-MA macrocrosslinker or the nanophase separation between the sytrenic and PDMS components in the hypercrosslinked coneworks. The swelling in selective solvents, that is, 1-nitropropane (1-PrNO\({}_{2}\)) for PSt and hexane for PDMS, led to uniform swelling in both solvents, and this resulted in the corresponding organogels. This swelling behavior is in line with the bicontinuous nanophasic morphology. In other words, this finding provides additional evidence for the bicontinuous nanophase-separated structure of the PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB networks. The equilibrium swelling degrees (\(Q_{\mathrm{e}}\)) correlate well with the composition, i.e., \(Q_{\mathrm{e}}\) increases with increasing PSt content in 1-PrNO\({}_{2}\), and with increasing PDMS content in hexane for all conetworks. The addition of DVB decreases \(Q_{\mathrm{e}}\), due to the increased crosslinking density. The diffusion exponents, determined from the swelling degree versus time curves by the Korsmeyer-Peppas relationship [113; 114; 115; 116], fall below 0.45 indicating hindered Fickian diffusion swelling mechanism of the PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB networks in both 1-PrNO\({}_{2}\) and hexane. In sum, it can be concluded that radical copolymerization of styrene and methacrylate-telechelic PDMS, both in the absence and in the presence of DVB, results in conetworks with a bicontinuous disordered nanophasic morphology with domain sizes in the range of \(\sim\)3-30 nm in a broad composition window with 30-80% \((m/m)\) PDMS content. These conetworks are selectively swellable in appropriate solvents, resulting in special gels (organogels, hydrophobic gels, and oleogels) in which only one of the nanophases is swollen. This property can surpass organogels of homopolymer networks (see e.g., Refs. [117; 118; 119; 120; 121; 122; 123; 124] and references therein) by the opportunity to have a much broader choice of organic solvents as swelling agents. Both the cocontinuous nanophasic morphology and the selective swelling behavior of the PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB networks can be utilized in various applications, ranging from optics to environmental protection, selective solvents, catalyst supports, nanohybrids, specialty membranes, etc.
# Experimental Flow Performance Investigation of Francis Turbines from Model to Prototype
## 2 Methodology and Experimental Measurements Techniques  
### Experimental Test Rig  
The turbine model used in the hydroelectric power station was tested at the Hydro Power Fluid Machinery Development and Testing Center in Tianjin, China. The test apparatus, shown in Figure 1, included a unit generator with a power range of 540 kW and a speed range of 0-2600 rpm. The power consumed by the two double-suction pumps was 724 kW. The flow measurement tank had a capacity of 180 m\({}^{3}\), and the storage tank had a volume of 3000 m\({}^{3}\). The unit's maximum head was 150 m, with flow rates ranging from 0 to 2.2 m\({}^{3}/s\), and the model runner testing range was between 250 and 500 mm. The following parameters were measured with the installed equipment: discharge, rotational speed, pressure, generator torque, frequency amplitude, spiral case inlet pressure, and draft tube outlet pressure, shown in Table 1.  
The discharge was measured by an electromagnetic flow meter, which employed the pulsed DC (direct current) magnetic field technique. The flow meter equipment was installed in the inlet pipeline before the spiral case, measuring unit discharge under various load conditions. The total model torque, \(T_{m}\), is the sum of the motor-generator torque, \(T_{1}\), and the friction torque, \(T_{2}\). The torque transducer was mounted on  
Figure 1: Experimental test rig of the Francis turbine model.  
the runner to the motor generator. The performance equations with unit parameters of the model turbine were:  
\[\text{Unit speed (rpm) : }n_{11}=\frac{n_{m}D_{m}}{\sqrt{H_{m}}} \tag{1}\]  
\[\text{Unit discharge (m}^{3}/\text{s) : }Q_{11}\ =\ \frac{Q_{m}}{D_{m}^{2}\sqrt{H_{m}}} \tag{2}\]  
\[\text{Unit power (W) : }P_{11}=\frac{P_{m}}{D_{m}^{2}H_{m}^{3/2}} \tag{3}\]  
where \(n_{m}\), \(D_{m}\), \(Q_{m}\), \(H_{m}\), and \(P_{m}\) are the rotational speed, runner diameter, flow rates, prototype head, and power output for the model turbine, respectively.  
Pressure transducers, which are electromagnetic devices, were utilized for pressure measurements in various locations of the model Francis turbine. Figure 2 shows the locations of four selected measurement points: \(P_{1}\) at the spiral inlet, \(P_{2}\) at the head cover, \(P_{3}\) in the draft cone, and \(P_{4}\) at the draft tube elbow. According to IEC-60193 standard [27], the amplitude of a signal is defined as the extent of variation that encompasses a specific percentage of the sample. This definition was used in conjunction with the probability density function to determine the peak-to-peak values of the pressure signals. The magnitude of pressure pulsations in the draft tube was analyzed by obtaining peak-to-peak measurements with a 97% confidence interval.
## 2 Materials and Methods
### Environmental Data  
Abiotic and vegetation variables were recorded at the beginning of each transect. Abiotic variables included the following: turbidity (NTU); conductivity (\(\mu\)S cm); total dissolved solids (mg/L); salinity (g/kg); dissolved oxygen (mg L\({}^{-1}\)); water depth (measured to nearest cm using meter stick or weighted string if over 1 m); water temperature (\({}^{\circ}\)C); dominant substrate (estimation based on grain size); air temperature (\({}^{\circ}\)C); wind strength (estimated using Beaufort wind strength scale (0-12); wind direction (azimuthal direction); and cloud cover (estimated based on tenths scale). Turbidity was measured using an HACH 2100Q portable turbidimeter (HACH Company, Loveland, CO, USA). Conductivity, total dissolved solids, salinity, dissolved oxygen, and water temperature were measured using an YSI ProQuatro multiparameter meter (Cole-Parmer, Rue Jean-Perrin, QC, Canada). Vegetation was estimated along the entire length of the transect, visually covering 2 m on either side, as the proportion of submerged, emergent, and floating vegetation cover. In addition, vegetation types were identified by species, and percent cover and vegetation height for each species were estimated. For each wetland, abiotic and vegetation data were averaged across all transects, separately for each year. Water-quality analysis was completed in July, which included total phosphorus (TP, mg/L) and turbidity, water temperature, conductivity, and pH that were used to calculate a Water Quality Index (WQI) value [3]. In July and August, additional aquatic vegetation sampling was undertaken. At 20 randomly selected 1 m\({}^{2}\) quadrats per wetland, all submerged aquatic vegetation (SAV) was identified, and percent cover and vegetation height were estimated. This information is used in a Submerged Aquatic Vegetation (SAV) IBI [20; 21].  
Figure 1: Coastal wetlands monitored by the Durham Region Coastal Wetland Monitoring Program and the Bay of Quinte Remedial Action Plan, Beneficial Use Impairment monitoring. The star represents Oshawa Second Marsh; the open circles represent sites included as Excellent fish IBI; the black circles are sites not included in the analysis. Table 1 provides a summary of the wetlands.
## 3 Results
### Test and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Station**} & \begin{tabular}{c} **EPN** \\ **Class** \\ \end{tabular} & \
\begin{tabular}{c} **Coordinate** \\ **Components** \\ \end{tabular} &
\begin{tabular}{c} **Overall Conclusion** \\ \end{tabular} \\ \hline \multirow{3}{*}{WTZR} & \multirow{3}{*}{C0} & N & There is an effect of weak systematic errors that were \\  & & E & not excluded when processing GNSS observations. \\ \cline{3-4}  & & U & Evaluation by NETM methods is required. \\ \hline \multirow{3}{*}{HELG} & \multirow{3}{*}{C1} & N & Significant data pathology. Evaluation is not possible. \\ \cline{3-4}  & & U & \\ \hline \multirow{3}{*}{PTBB} & \multirow{3}{*}{C5} & N & There is an effect of weak systematic errors that were \\  & & E & not excluded when processing GNSS observations. \\ \cline{3-4}  & & U & Evaluation by NETM methods is required. \\ \hline \multirow{3}{*}{WROC} & \multirow{3}{*}{C6} & N & There is an effect of weak systematic errors that were \\  & & E & not excluded when processing GNSS observations. \\ \cline{3-4}  & & U & Evaluation by NETM methods is required. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PS-NETM conclusion about chosen stations. It should be noted that the overall conclusion is based on the \"worst case\" scenario for any of the coordinate components.
## 3 Results and Discussion  
### Nanoparticles Characterization  
Standard characterization procedures were used for the synthesized WS\({}_{2}\) particles. The microscale/nanoscale 2H-WS\({}_{2}\) powder was characterized through X-ray diffraction (XRD) and SEM to obtain its physical and chemical properties. Figure 2 shows the XRD images of the synthesized WS\({}_{2}\) particles. The diffraction peaks in Figure 2 correspond to the PDF#08-0237 in the powder diffraction file, with lattice parameters of a = 3.154 A, b = 3.154 A, and c = 12.362 A. Because of no impurity peaks in the figure, it can be determined that the purity of synthesized WS\({}_{2}\) is relatively high. The SEM images of synthesized WS\({}_{2}\) particles at different magnifications are shown in Figures 3 and 4. The thickness of WS\({}_{2}\) particles in Figure 3 is about 500 nm, and the lateral dimension is between 1 and 5 um. The thickness of WS\({}_{2}\) particles in Figure 4 is about 2 um, and the lateral dimension is between 5 and 10 um. As can be seen from the figures, the WS\({}_{2}\) particles are mostly lamellar structure with different orientations, disordered arrangement, and irregular shapes. In addition, it can be clearly seen that the large WS\({}_{2}\) particles are formed by the close packing and agglomeration of some lamellas. Among them, most of these stacked particles have obvious delaminations on the edges, and the other parts have blurred edges and have a tendency to fuse. This is because the high-temperature calcination is conducive to the conversion of stacked multilayer lamellas into disordered single lamellas; this conversion is limited by the size of the particles and therefore cannot be fully conducted. Therefore, the lamellar-structure WS\({}_{2}\) particles with sizes of 2 um and 500 nm were successfully synthesized by solid-phase reaction method.  
Figure 3: SEM images of nano-WS\({}_{2}\) at different magnifications, **(a)** 5000\(\times\)**; **(b)** 10,000\(\times\).  
Figure 2: XRD images of the synthesized WS\({}_{2}\) particles.
## 4 Conclusions  
This study developed an SSVEP-based BCI with MDFs to enable people to communicate with others or devices. The multi-domain features can precisely represent the characteristics of the SSVEP signal. For multi-channel SSVEP-based BCIs, a convolutional operation across the channel dimension can effectively fuse information from different channels. Additionally, the element-wise addition operation successfully fuses time- and frequency-domain features. The sequence of convolutional neural networks effectively extracts discriminative embedding features, and MTL is used to make correct final decisions. The experimental results show that the proposed SSVEP-based BCI with MDFs and MTL outperforms EEGNet and MTL. Moreover, compared to statistical approaches, the proposed approach achieves higher accuracy when the duration of the SSVEP signal is 1 s or 2 s. When the duration of the input SSVEP signal is 3 s or 4 s, the performance  
Figure 3: The accuracy of the SSVEP-BCI for different approaches. The proposed neural network outperforms other neural network-based approaches. When the duration of the input SSVEP signal is short, the proposed approach also outperforms the statistical approaches.  
of the proposed approach is similar to that of statistical approaches. A limitation of the proposed approach is that the SSVEP signals for some subjects may not be effectively evoked. In the future, multi-task learning with non-classification tasks can be adapted to extract meaningful embedding features, thereby improving BCI system performance. Therefore, the proposed approach can be used to develop a practical SSVEP-based BCI.  
Conceptualization, Y.-J.C. and C.-M.W.; data curation, Y.-J.C. and C.-M.W.; formal analysis, Y.-J.C. and C.-M.W.; investigation, Y.-J.C., S.-C.C. and C.-M.W.; methodology, [PERSON]-J.C. and C.-M.W.; project administration, C.-M.W.; resources, [PERSON]-J.C. and C.-M.W.; software, Y.-J.C.; supervision, C.-M.W.; validation, [PERSON]-J.C., [PERSON]-C.C. and C.-M.W.; visualization, C.-M.W.; writing--original draft, [PERSON]-J.C., S.-C.C. and C.-M.W.; writing--review and editing, [PERSON]-J.C. and C.-M.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Higher Education Sprout of the Ministry of Education, Taiwan, and the other was funded by the National Science and Technology Council, Taiwan, grant numbers NSTC 113-2221-E-218-006, NSTC 113-2221-E-218-005, and NSTC 113-2221-E-167-005.  
National Review Board Statement: National Cheng Kung University Human Research Ethics Committee Approval No. NCKU HREC-F-113-510-2, date--19 September 2023, and No. NCKU HREC-F-113-512-2, date--23 September 2023.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent was obtained from the patient(s) to publish this paper.  
The original data presented in the study are openly available in [21].  
We are indebted to all study participants and members of the research team.  
The authors declare no conflicts of interest.
## 6 Conclusions  
Under conditions that ensure the health and comfort of passengers during the operation of the plateau train, the energy consumption changes of the ACS and OSS along the Qinghai-Tibet railway line were studied by adjusting the ASP. The main conclusions are as follows. 1. The FAV in different areas increased with increasing altitude, and the O\({}_{2}\) demand of trains in the O\({}_{2}\) supply areas was positively correlated with altitude. The FAV and OSV reached maximum values of 0.350 m\({}^{3}\)/s and 0.146 m\({}^{3}\)/s, respectively, in the Tanggula mountainous area, which has the highest altitude. 2. When the train was running in areas where the ambient air enthalpy was higher than the indoor air enthalpy, reducing the FAV when the CO\({}_{2}\) PP was less than 152 Pa helped reduce the ACS energy consumption. When running in no-oxygen areas, where the enthalpy of the ambient air was lower than that of the indoor air, reducing the FAV led to an increase in the energy consumption of the ACS. The ACS in these areas should work under an FAV recommended by the specifications. When the train was running in the O\({}_{2}\) supply areas, the energy consumption of the ACS accounted for a small proportion of the total energy consumption, and the OSS energy consumption was significantly affected by the FAV. When the CO\({}_{2}\) PP was lower than the limit value, the minimum FAV should be used to minimise the energy consumption of the OSS. The maximum energy-saving rates of the system in the three areas were 37.16%, 35.41%, and 75.61%. 3. The air pressure in the Tanggula mountainous area was extremely low and the O\({}_{2}\) content was insufficient. The average O\({}_{2}\) PP of the passenger breathing zone could not satisfy the minimum standard requirement of 13.5 kPa under combined working conditions of a minimum FAV and maximum OSV. In this area, the use of distributed O\({}_{2}\) supply equipment and stewarded round-trip diagnosis should be strengthened. 4. As 25T trains are widely used in rail transit, their body structures cannot be easily changed. Optimising the air distribution is the only way to change the ASP. To improve the air composition inside a train, train body structure optimisation should be considered in the future. In this study, the theoretical relationship among the FAV, OSV, and air parameters in a train running in different areas along the Qinghai-Tibet line was elucidated, and the dynamic operation modes of the ACS and OSS were proposed. Furthermore, the research results provide a healthy and comfortable interior environment for the plateau train and minimise its energy consumption.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 2 Background and Related Work
### InfiniBand  
InfiniBand [12; 13] is a high-performance network device that guarantees high bandwidth and low transmission latency compared to Ethernet. It is a standard switch fabric designed for interconnecting nodes in high-spec computing clusters such as HPC (High-Performance Computer). The representative feature of InfiniBand, RDMA, is a technique for transmitting data directly from the memory of the sending server to the memory of the receiving server without passing through the operating system. There are several advantages to using RDMA communication, such as (1) zero-copy, in which messages are transmitted directly to each server's memory without copying between network layers; (2) kernel bypass, which enables data transmission without kernel intervention; and (3) no CPU involvement, meaning that memory-based message transmission is used without wasting the CPU resources of remote systems [9]. As a result, the performance overheads of CPU-based operations such as buffer copying and context switching that occur during data transmission can be significantly reduced, leading to an overall improvement in data processing performance.  
InfiniBand also provides an IPoIB (IP over InfiniBand) layer that allows devices to be accessed via IP addresses. IPoIB enables better accessibility overall as it allows the use of InfiniBand with IP, but its overall performance is lower than that of RDMA due to the use of the existing TCP/IP network stack. In this paper, we propose an efficient method for improving the performance of Apache Storm using InfiniBand and RDMA as explained so far, and compare the processing efficiency in Ethernet- and IPoIB-based environments to evaluate the performance of the proposed SPinDP.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations
### IIR Approximations  
The Infinite Impulse Response (IIR) filters use current input and previous input and output samples to compute each output sample. In the case of approximating Gaussian convolution, IIR filters employ recursive expressions that allow for iterative computations.  
IIR filter recursively solves a sequence of difference equations like the following one:  
\[y[n]=b_{0}\,x[n] +b_{1}\,x[n-1]+\ldots+b_{P}\,x[n-P] \tag{30}\] \[-a_{1}\,y[n-1]-\ldots-a_{Q}\,y[n-Q],\]  
where the filter coefficients \(b_{i}\), \(i=0,\ldots,P\), and \(a_{j},j=1,\ldots,Q\) are some functions of the Gaussian half-width \(\sigma\). The cost of such an approximation is independent of \(\sigma\). By considering the \(z\)-transform of the IIR filter and combining causal and anti-causal parts, researchers constructed recursive systems with symmetric impulse responses that closely approximate the Gaussian filtering.  
Several state-of-the-art forms of IIR approximations are described below.