esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 7 Conclusions  
The major challenges identified in real-time patient monitoring WBANs are the higher response time, lower reliability, and higher energy consumption. These shortcomings can be addressed in MAC layer using dynamic time slot allocation instead of fixed slot allocations. In this paper, a fog-assisted network is utilized for a real-time patient monitoring setup. The fog layer (central coordinator) is deployed at the edge of the network to reduce the response time and transmission errors. This makes it suitable for emergency medical applications which carries bursty data. An energy-efficient, cost-based objective function and an MCPS algorithm is designed for routing the data packets to the coordinator node. A new dynamic time slot allocation method called DTS has been proposed for allocating dynamic slots to the sensor nodes. It minimizes the unnecessary slot wastage and waiting time of packets in the queue. The slot allocation is designed based on the fuzzy logic with input variables as energy ratio, buffer memory ratio, and packet arrival rate. The chance value for the number of slots allocated is determined with the help of fuzzy inference rules. The results reveal that the DTS is capable of achieving a relevant enhancement in packet delivery ratio (12% and 15%), a significant reduction in average end to end delay (47% and 59%) and average energy consumption (22% and 31%) in comparison with TMP and IEEE 802.15.4 respectively. Future work will include the enhanced version of the proposed model for a specific disease prediction which is based on different data rate patient vitals. Also, the fog-assisted network can be made more secure by implementing new data encryption and authentication methods.  
Conceptualization, B.V. and S.P.; methodology, B.V.; software, S.P.; validation, B.V. and S.P.; formal analysis, B.V.; investigation, S.P.; resources, B.V. and S.P.; data curation, B.V.; writing--original draft preparation, S.P.; writing--review and editing, B.V.; visualization, B.V. and S.P.; supervision, B.V.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 12: Average energy consumption under different packet intervals.
# An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction
## 3 Proposed Method
### Encoder-Decoder  
As shown in Figure 3, an encoder-decoder structure is adopted in the model that uses a CNN as an encoder and an LSTM as a decoder. The encoder is responsible for converting input into features and the decoder is responsible for converting features into targets.  
The encoder is formed by a CNN, such as Resnet [15], which adopts a residual structure to avoid over-fitting. Firstly, since the input contains the time dimension and the feature dimension, two continuous \(3\times 3\) convolution layers are applied to receive the input and capture feature. After that, it will come through a BatchNormalization (BN) layer, which is widely used to normalize the data to the same quantity and accelerate convergence; then, a Dense layer is used to accept the characteristics and reduce the dimensions, and at the same time, a \(1\times 1\) convolution layer is used to deliver useful information and concatenate the output of the Dense layer. The concatenated feature is flattened and passes through a Dense layer to reduce the dimensions. The output of the CNN module will be passed to a standard LSTM module as the input. The output of the LSTM module will predict the effluent wastewater quality through the attention mechanism.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 3 Proposed Speech Enhancement Algorithm  
Figure 3 shows the block diagram of the proposed SEA. Firstly, \(y(n)\) is converted into frames \(y(n,k)\) with the same setup as used in Section 2.3.  
To carry out the tuning of \(K_{0}(n)\) in real-life noise conditions, unlike biased \(J_{2}(n)\) and \(J_{1}(n)\) metrics (Figure 2f), they should achieve similar characteristics that occur in the WGN condition (Figure 1c). It can be achieved through improving the estimates of \((\{\hat{a}_{i}\},\hat{\sigma}_{w}^{2})\) and \(\hat{\sigma}_{w}^{2}\) as described in Section 3.1.  
Figure 2: Biasing effect of \(K_{0}(n)\): (**a,b**) spectrograms of the clean speech and the noisy speech (corrupt sp05 with 5 dB _bubble_ noise), (**c**) \(K_{0}(n)\) computed in oracle and non-oracle cases, (**d**,e**) \([\alpha^{2}(n)+\sigma_{w}^{2}]\) and \(\sigma_{w}^{2}\) computed in oracle and non-oracle cases, (**f**) \(J_{2}(n)\) and \(J_{1}(n)\) computed from the noisy speech in (**b**), spectrogram of enhanced speech produced by: (**g**) KF-oracle method and (**h**) KF-non-oracle method.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 5 Experimental Results
### Single-Subject Model Experiment  
In the first part of the experiment, models for the single-subject models were created, 70% of the data was used for training, 15% for the model validation, and 15% for the test.  
As can be seen in Table 1, machine learning learned to detect speech from the EEG signal for a single subject with an average accuracy of 84%. This accuracy can be considered relatively high.  
The best result was recorded for the subject 3. Figure 3 shows the part of the input labels compared to the output data obtained from model based on data set 3.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 1 Introduction
During the sea trials, these sensors measure the ship's motion status data and control system status data in real time. These time series sensor data are stored. Therefore, this approach provides a new strategy for ship motion modeling and prediction. We can model the ship motion by mining the hidden information of sensor data. In this paper, we propose a coupled convolutional neural network (CNN) [38] and long short-term memory (LSTM) [37] model for USV roll motion prediction. The data measured by sensors installed on the USV are adopted as the data set of the proposed model. These sensor data contain six-degree-of-freedom motion status data and control status data, and constitute a multidimensional space of the USV movement. The USV roll motion of the next moment is influenced not only by the other five degrees of freedom and control states, but also by the movement of the previous period. CNNs have been proven to be powerful for processing spatial data, and have been widely used in computer vision [38; 39; 40]. LSTM is a type of recurrent neural network (RNN) [41] designed for the time-series problem. Therefore, in our proposed model, a CNN is used to extract spatial features and local time-series features of the USV sensor data. The output of the CNN layer are higher-dimensional feature maps, which are the input of the LSTM layer. The wind, waves, and sea current are natural phenomena that usually change continuously over a period of time. They act on the USV, causing a series of changes in the USV's motion status. So, the current USV roll motion is affected by the motion status in the past. Therefore, the LSTM layer is exploited to model the long-term movement process of the USV and predict the roll motion in the next moment. After that, the fully connected layer is utilized to decode the LSTM output and calculate the final prediction results. The proposed model is able to extract features in both spatial and temporal dimensions to obtain better prediction results for USV roll motion. To prove the effectiveness of the proposed model, it was applied to \"JingHai-VI\" and \"JingHai-III\" USVs of Shanghai University. The paper is organized as follows: Section 2 presents the proposed coupled CNN and LSTM model for USV roll motion prediction in detail. Section 3 describes the source of the data set and the data preprocessing process. The experimental results and discussion of two case studies are shown in Section 4. Finally, the paper is concluded in Section 5.
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 3 Test of the Improved Algorithm Based on Numerical Simulation
### Frontal Wind Field  
The front is the weather system formed when cold and warm air masses meet. Since the density of cold air is greater than that of warm air, the warm air will rise along the cold air to form an inclined junction area when the two air masses meet, forming wind-direction-shear and wind-speed-shear at the interface. The constructed simulated frontal wind field is depicted in Figure 6. The red straight line in Figure 6 is the simulated aircraft's slide path and laser beam in slide path scanning. The simulated wind field comprises two air masses with two opposite motion directions, with the simulated wind speed being 10 m/s. The wind direction of the lower air mass tilts 45 to the positive direction of the \(Z\)-axis, while the wind direction of the upper air mass is 45 to the negative direction of the \(Z\)-axis. Wind-direction-shear and wind-speed-shear are formed at the junction of the two air masses.  
The results of wind shear detection under the background of the frontal wind field are illustrated in Figure 7, where the black dotted line represents the headwind profile data in the slide path obtained by the simulated lidar. The results reveal that wind shear existed at the data acquisition point within 8500-9000 m of the aircraft landing point. Here, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Strength Grade} & \multicolumn{2}{c}{**The Standard of Windshear Intensity**} & \multirow{2}{*}{**Effect on Flight Safety**} \\ \cline{2-2} \cline{4-4}  & **Windshear Intensity (S 1)** & & \\ \hline Mild & 0–0.07 & 0–0.27 & The track and airspeed of aircraft changes slightly \\ Moderate & 0.08–0.13 & 0.28–2.13 & May make the operation of aircraft difficult \\ Strong & 0.14–0.20 & 2.14–7.20 & May lead to loss of control of aircraft \\ Severe & \textgreater{}0.20 & \textgreater{}7.20 & May cause serious harm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The standard of wind shear intensity and its corresponding wind shear intensity factor value recommended by ICAO.  
Figure 6: The left figure is the simulated frontal wind field in the 3-D coordinate system; the right figure is the profile view of the simulated frontal wind field.  
wind speed changes by 2.25 m/s within a 500 m distance and the shear intensity factor value is 0.02, which is assumed as mild wind shear. At this time, both algorithms WSDSF and WSDHP can detect this weak wind shear, and the calculated wind shear intensity and position are consistent with the actual case. Concurrently, the double ramps detection algorithm also realizes the warning of wind shear. However, the calculated position of wind shear is different from the actual case, and the calculated wind shear intensity is higher than the actual wind shear intensity. In this experiment, WSDSF and WSDHP perform better than the double ramps detection algorithm.
## 3 About Geographic Names and Gazetteers
### Existing Systems
#### 3.4.1 GeoNames  
The GeoNames [22] database contains over 10,000,000 geographical names corresponding to over 7,500,000 unique features. All features are categorized into one out of nine feature classes and further subcategorized into one out of 645 feature codes. Beyond names of places in various languages, the data stored include latitude, longitude, elevation, population, administrative subdivisions and postal codes. Among spatial relationships, GeoNames utilizes a special way to model hierarchical tessellations:  
* Children, _i.e._, the list of administrative divisions (first relative sublevel);
* Hierarchy, _i.e._, the list of toponyms higher up in the hierarchy of a place name;
* Neighbors, _i.e._, the list of all neighbors for a country or administrative division;
* Contains, _i.e._, the list of all features within the feature;
* Siblings, _i.e._, the list of all siblings of a toponym at the same level.  
For instance, here is an excerpt of the description of Sicily in which the tag \(<\)ToponymName\(>\) corresponds to endonym and \(<\)name\(>\) to exonym):  
\(<\)geoname\(>\)  
\(<\)toponymName\(>\)Sicilia\(<\)/toponymName\(>\)  
\(<\)name\(>\)Sicily\(<\)/name\(>\)  
\(<\)lat\(>\)37.75\(<\)/lat\(>\)\(<\)lng\(>\)14.25\(<\)/lng\(>\)  
\(<\)geonameld\(>\)2523119\(<\)/geonameld\(>\)  
\(<\)countryCode\(>\)IT\(<\)/countryCode\(>\)  
\(<\)countryName\(>\)Italy\(<\)/countryName\(>\)  
\(<\)numberOfChildren\(>\)9\(<\)/numberOfChildren\(>\)  
\(<\)/geoname\(>\)
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Regional Regulations
#### 2.2.3 Japan  
The Association of Radio Industries and Businesses defines the use of the 922.4-928 MHz band [25], using carrier sense under these premises:  
* Minimum listening time during CCA of 128 us; maximum: 5 ms.
* Maximum single TX time: 400 ms.
* Duty cycle \(\leq\) 10%.
* If the previous TX time is >200 ms, the device shall wait for at least 10 times the TX time before the next TX.
* If the previous TX time is \(\leq\)200 ms and more than 6 ms, it shall wait for 2 ms before consecutive TX.
* Using two radio channels at the same time (i.e., signal is 400 kHz wide), the maximum single TX time has to be less than 200 ms.
* Using up to 5 radio channels at the same time, maximum single TX time has to be less than 100 ms.
* Maximum TX power is 20 mW.  
If no carrier sense is used:  
* Maximum TX power is 1 mW.
* Maximum single TX time: 100 ms.
* Duty cycle \(\leq\) 0.1%.
## 1 Introduction
[41] demonstrated that epoxy and polyurethane coatings performed better than acrylic, polymer, and chlorinated rubber coatings using the methods of water absorption, chloride permeability, and chloride diffusion. At present, research on improving the impermeability of substrates with superhydrophobic coatings mainly focuses on the impermeability of coatings. There are few impermeability tests for coatings under high water pressure. It is worth noting that coatings exhibit varying impermeability in different high-pressure environments, which even lead to coating detachment or damage. On the one hand, microcracks are easily distributed on concrete surfaces under long-term high pressure [42]. This process requires the coating to have excellent mechanical properties. On the other hand, low interfacial strength causes coating blistering or delamination at the interface. Therefore, excellent interfacial strength is the foundation for coatings that provide effective protection for concrete [33; 43; 44]. The objectives of this study were to apply a coating to improve the impermeability of concrete in high-pressure water environments, and to study the seepage regulation of coated concrete and the failure mechanism of the SSS coating by conducting high-pressure impermeability tests. An SCU-SD-SP-II (SSS) coating was applied to the surface of concrete. The SSS coating formed a composite protective layer with the concrete interface through an epoxy-modified silicone (EMS) coating. We conducted high-pressure impermeability tests to study the seepage regulation of concrete and coated concrete in high-pressure environments. Subsequently, we designed impermeability tests with SSS coatings on the bottom and top surfaces of concrete specimens, simulating two working conditions of high-pressure water conveyance tunnels: internal water pressure during operation and external water pressure during the emptying period. Considering the cracks on concrete surfaces in high-pressure environments, we also prepared natural cracks by splitting the concrete. We designed impermeability tests for SSS coatings on crack surfaces and analyzed the failure mechanism of SSS coatings in cracks. The experiments of this study considered high water pressure and concrete cracks, providing reference for impermeable tests in high-pressure water environments. The research results can solve the high-pressure seepage of concrete, which is significant for engineering economics and safety in global hydraulic engineering and ocean engineering.
## Appendix A Summary of the Equivalent Beam Models  
The main equivalent beam models reported in the literature are summarized in Table 1. Refer to Section 4 for the list of abbreviations.  
The first column presents the main authors and the years when models were proposed. The second column lists the bracing system that can be described by the model. The third column reports the equivalent beam model used: Euleoro Bernulli beam, EBB (i.e., bending stiffness only); shear stiffness only beam, SHB; Timoshenko beam, TB (i.e., shear and bending stiffness in series); Timoshenko beam with axial strain, TBA, coupled two beams, CTB (i.e., bending beam coupled with shear a beam); sandwich beam, SB (i.e., bending beam coupled with a Timoshenko beam). When bending and torsion coupling is considered(i.e., asymmetric buildings) a check mark is placed in the fourth column and the torsional stiffness used is described in the third column between parenthesis: \(GJ_{t}\) and \(EI_{\omega}\) are the De Saint-Venant and secondary stiffnesses, respectively. When a symmetry plane is needed it is indicated in the note column. A check mark is placed in the columns 5 to 9 when the model describes, respectively, stiffness vertical variations, axial strain, natural frequencies, mode shapes and seismic loads. Additional features are summarized in the note column.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
# Reducing LUT Count for FPGA-Based Mealy FSMs
## 6 Experimental Results
Four other methods were taken to compare with our approach. They are: (1) Auto of Vivado; (2) One-hot of Vivado; (3) JEDI-based FSMs and (4) DEMAIN-based FSMs. The results of experiments are shown in Table 5 (the number of LUTs) and Table 6 (the operating frequency). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Benchmark** & **Auto** & **One-Hot** & **JEDI** & **DEMAIN** & **Our Approach** & **Category** \\ \hline bbara & 17 & 17 & 10 & 9 & 10 & 1 \\ bbsse & 33 & 37 & 24 & 26 & 26 & 1 \\ bbtas & 5 & 5 & 5 & 5 & 8 & 0 \\ beecount & 19 & 19 & 14 & 16 & 14 & 1 \\ cse & 40 & 66 & 36 & 38 & 33 & 1 \\ dk14 & 10 & 27 & 10 & 12 & 12 & 1 \\ dk15 & 5 & 16 & 5 & 6 & 6 & 1 \\ dk16 & 15 & 34 & 12 & 14 & 11 & 1 \\ dk17 & 5 & 12 & 5 & 6 & 8 & 0 \\ dk27 & 3 & 5 & 4 & 4 & 7 & 0 \\ dk512 & 10 & 10 & 9 & 10 & 12 & 0 \\ donfile & 31 & 31 & 22 & 26 & 21 & 1 \\ ex1 & 70 & 74 & 53 & 57 & 40 & 2 \\ ex2 & 9 & 9 & 8 & 9 & 8 & 1 \\ ex3 & 9 & 9 & 9 & 9 & 11 & 0 \\ ex4 & 15 & 13 & 12 & 13 & 11 & 1 \\ ex5 & 9 & 9 & 9 & 9 & 10 & 0 \\ ex6 & 24 & 36 & 22 & 23 & 21 & 1 \\ ex7 & 4 & 5 & 4 & 4 & 6 & 1 \\ keyb & 43 & 61 & 40 & 42 & 37 & 1 \\ kirkman & 42 & 58 & 39 & 41 & 33 & 2 \\ lion & 2 & 5 & 2 & 2 & 6 & 0 \\ lion9 & 6 & 11 & 5 & 5 & 8 & 0 \\ mark1 & 23 & 23 & 20 & 21 & 19 & 1 \\ mc & 4 & 7 & 4 & 5 & 6 & 0 \\ modulo12 & 7 & 7 & 7 & 7 & 9 & 0 \\ opus & 28 & 28 & 22 & 26 & 21 & 1 \\ planet & 131 & 131 & 88 & 94 & 78 & 2 \\ planet1 & 131 & 131 & 88 & 94 & 78 & 2 \\ pma & 94 & 94 & 86 & 91 & 72 & 2 \\ s1 & 65 & 99 & 61 & 64 & 54 & 2 \\ s1488 & 124 & 131 & 108 & 112 & 89 & 2 \\ s1494 & 126 & 132 & 110 & 117 & 90 & 2 \\ s1a & 49 & 81 & 43 & 54 & 38 & 2 \\ s208 & 12 & 31 & 10 & 11 & 9 & 2 \\ s27 & 6 & 18 & 6 & 6 & 6 & 1 \\ s386 & 26 & 39 & 22 & 25 & 20 & 1 \\ s420 & 10 & 31 & 9 & 10 & 8 & 4 \\ s510 & 48 & 48 & 32 & 39 & 22 & 4 \\ s8 & 9 & 9 & 9 & 9 & 9 & 1 \\ s820 & 88 & 82 & 68 & 76 & 52 & 4 \\ s832 & 80 & 79 & 62 & 70 & 50 & 4 \\ sand & 132 & 132 & 114 & 121 & 99 & 3 \\ shiftreg & 2 & 6 & 2 & 2 & 4 & 0 \\ sse & 33 & 37 & 30 & 32 & 26 & 1 \\ styr & 93 & 120 & 81 & 88 & 70 & 2 \\ tma & 45 & 39 & 39 & 41 & 30 & 2 \\ \hline Total & 1792 & 2104 & 1480 & 1601 & 1318 & \\ \hline Percentage,\% & 135.96 & 159.63 & 112.29 & 121.47 & 100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results (the number of LUTs). Tables 5 and 6 are organized in the same order. The rows are marked by the names of benchmarks, the columns by design methods. The rows \"Total\" include results of summation for corresponding values. The summarized characteristics of our approach (\(U_{4}\)--based FSMs) are taken as 100%. The rows \"Percentage\" show the percentage of summarized characteristics of FSM circuits implemented by other methods respectively to benchmarks based on our approach.
# FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
## 4 Experiments
### Experimental Results Analysis
#### 4.3.2 FSM-BC-BSP Algorithm Performance Evaluation  
This section evaluates the performance of the FSM-BC-BSP algorithm through a comparative analysis with two other frequent subgraph mining algorithms, namely FSM-H based on MapReduce and GRAMI based on a single machine.  
(1) Different Datasets' Comparison at the Same Support Level  
The initial part of the test involves comparing the runtime performance of FSM-BC-BSP, FSM-H, and GRAMI across different datasets, while maintaining the same support level of 4. Datasets such as Ast.txt, credit1, credit2, and DBLP are used. The results consistently indicate that the FSM-BC-BSP algorithm outperforms FSM-H and GRAMI on  
Figure 6: The times of frequent subgraphs with different partitions under different datasets.  
Figure 7: The times of frequent subgraphs with different partitions and different support  
all datasets. Figure 8 provides a visual representation of the runtime comparison on the diverse datasets.
## 4 Results and Discussion
### Outcomes of Rayleigh Number (Ra)  
Figure 13 illustrates the variety of the (left) streamlines, (middle) isotherms, and (right) nanoparticle concentration with Rayleigh number (\(Ra\)) for \(N=4\), \(A=0.1\), and \(\phi=0.02\) in the \(X,Y\) plane. Rayleigh number holds a strong impact in the flow field. The vortices become stronger and more concentrated near the upper surface with the increasing \(Ra\) values. At \(Ra=10^{7}\), some lower strength vortices appear close the cold bottom surface in addition to the strong vortices at the upper portion. The higher Rayleigh number indicates the higher temperature contrast within the hot and cold surfaces, which causes a stronger flow circulation inside the domain. Additionally, Rayleigh number associated with the buoyancy-driven flow. Increasing the Rayleigh number means the increase in the buoyancy forces and overcome the viscous forces. Therefore, convection currents dominate at high Rayleigh numbers. At high Rayleigh number, the temperature difference causes a higher density difference between the fluids near the cold and hot walls, leading to a strong convection current that improves the convective heat transfer rate accordingly.  
Looking at the middle column of Figure 13, it is observed that the isotherms are uniformly stratified throughout the whole collector. The isothermal lines move away from the upper wavy wall  
Figure 12: Variation of the average Nusselt number with \(\phi\) for different (**a**) \(N\) and (**b**) \(A\) at \(Ra=10^{6}\).  
Figure 13: Variation of the streamlines (**left**), isotherms (**middle**), and nanoparticle distribution (**right**) evolution by Rayleigh number (\(Ra\)) for \(N=4\), \(A=0.1\), and \(\phi=0.02\) in the plane (\(X,Y\)).  
as the value of \(Ra\) goes up. Due to increasing Rayleigh number, temperature gradient becomes greater, leading to more thermal energy transfer of the hot toward the cold surface into strong convection current. The locative concentration of nanoparticles from the upper and lower surfaces to the fluid for Rayleigh number variation is of great importance and is depicted in the right column of Figure 13. Nanoparticle distribution is more nonuniform for large Rayleigh numbers. Nevertheless, it remains further uniform toward low Rayleigh numbers. The concentration gradient near the top and bottom surface becomes more important for a higher values of \(Ra\).