esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Protocol
### Constructing the Hierarchical Network Structure  
In order to construct a hierarchical network structure, we assume that each sensor node is layered and belongs to a specific node set according to the adjacency matrix \(M(\mathcal{G})\). Specifically, the BS \(v_{0}\) is the unique element of the first-layer subset \(\mathcal{V}_{1}\); the second-layer subset \(\mathcal{V}_{2}\) comprises the neighboring sensor nodes of the BS \(v_{0}\), and the neighboring sensor nodes that are adjacent to \(\mathcal{V}_{2}\) constitute the third-layer subset \(\mathcal{V}_{3}\). This process repeats itself until all sensor nodes are stratified. Finally, a hierarchical network structure \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\) for all nodes is built, where \(\mathcal{V}_{T}=\mathcal{V}\), \(\mathcal{E}_{T}\) is the edge set with the edge representing the neighboring communication link between sensor nodes of adjacent layers, and \(\mathcal{E}_{T}\subset\mathcal{E}\). The hierarchical network structure \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\) is denoted as  
\[\left\{\begin{array}{l}\mathcal{V}_{1}=\{v_{0}\}\\ \mathcal{V}_{2}=\{v_{p}\in\mathcal{V}|m_{p0}=1,\;e_{p0}\in\mathcal{E}_{T}\}\\ \cdots\cdots\\ \mathcal{V}_{k}=\{v_{p}\in\mathcal{V}|v_{q}\in\mathcal{V}_{k-1},m_{pq}=1,\;e_ {pq}\in\mathcal{E}_{T}\}\\ \cdots\cdots\\ \mathcal{V}_{K}=\{v_{p}\in\mathcal{V}|v_{q}\in\mathcal{V}_{K-1},m_{pq}=1,\;e_ {pq}\in\mathcal{E}_{T}\}\end{array}\right. \tag{6}\]  
where \(\mathcal{V}_{k}\) is the \(k\)-layer sensor node subset of \(\mathcal{T}(\mathcal{V}_{T},\mathcal{E}_{T})\). Meanwhile, \(\mathcal{V}_{T}=\mathcal{V}_{1}\cup\cdots\cup\mathcal{V}_{k}\cup\cdots\cup \mathcal{V}_{K}\), \(\mathcal{V}_{s}\cap\mathcal{V}_{t}=\emptyset\) for any \(s\
eq t\) and \(s,t\in\{1,\cdots,k,\cdots,K\}\).
## 2 Materials and Methods
### Antibacterial Activity of Levan
#### 2.9.2. Well Diffusion Assay  
Levan's antibacterial properties were tested in vitro against _Staphylococcus aureus_ (ATCC 33592) and _Escherichia coli_ (ATCC 25922) using agar well diffusion method [18,19]. Using sterile swabs, 100 \(\mu\)L of 24 h old bacterial culture of the two reference strains with a concentration equivalent to 0.5 McFarland (1.5 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\)) were streaked on the dry surface of the nutrient agar plate. Then, under aseptic conditions, a well of 0.8 mm was made on the nutrient agar surface by using a sterile cork borer and receiving 50 \(\mu\)L of levan solution at concentration 25 mg mL\({}^{-1}\) DMSO. As positive controls, both Oxoid Sulfamethoxazole (25 \(\mu\)g/disc) and Ofloxacin (5 \(\mu\)g/disc) were used, while a negative control was DMSO. Then, incubation was carried out at 37 \({}^{\circ}\)C for 24 h to allow the growth of bacteria. Zones of inhibition were measured in mm using a ruler. The experiment was carried out in triplicate and the means of inhibition zones were measured in millimeters \(\pm\) standard variation [20].
# A Review of Deep Learning Applications for Railway Safety
## 2 Overview of Deep Learning Approaches
### Data Types
#### 2.1.2 Time-Series Data  
Sequential data refer to data in which objects in the data set have a certain order. Sequential data include numerous kinds of time series data with temporary properties, such as language, stock quotes, electrocardiogram (ECG) signals, seismic waves, and DNA sequencing. In railway safety, sensor data (e.g., vibration) and video (e.g., CCTV) are prevalent time-series data types.  
The recurrent neural network (RNN) was developed to deliver information that occurred at the previous time step to the next time step through the recurrent edge, which is the edge connecting the hidden nodes. In other words, an RNN hidden layer can remember important things about input information that allows them to predict what will come next. The key operation in RNN can be described by the following formula:  
\[\mathbf{a}^{(t)}=\mathbf{b}+\mathbf{W}\mathbf{h}^{(t-1)}+\mathbf{U}\mathbf{x}^{(t)} \tag{5}\]  
\[\mathbf{h}^{(t)}=\tanh(\mathbf{a}^{(t)}) \tag{6}\]  
\[\mathbf{o}^{(t)}=\mathbf{c}+\mathbf{V}\mathbf{h}^{(t)} \tag{7}\]  
where \(\mathbf{U},\mathbf{V},\mathbf{W}\) is a weight matrix that is updated through training, for input-to-hidden, hidden-to-output and hidden-to-hidden. \(\mathbf{b}\), \(\mathbf{c}\) are bias vectors. \(\mathbf{x}^{(t)}\) is input vector. \(t\) is the time step. \(\mathbf{o}^{(t)}\) is output vector.  
Long short term memory (LSTM) improves an original RNN structure by adding gates that select inputs and outputs at a time step to properly understand the contextual dependence of sequential data (e.g., long-term dependence). A gated recurrent unit (GRU) is also a variant of RNN, like LSTM, but has fewer parameters.
## 4 Conclusions  
In this study, it was found that the initial inclination caused by the clearance is the one of the most important variables for improving the joint strength when joining CFRP and steel. The effect was analyzed through experimental and numerical analysis, and the main conclusions are summarized below.  
1. The clearance is inevitable when mechanically fastening dissimilar materials using rivets and bolts, and the clearance causes inclination of the fastener when an external force is applied.
2. When using structural adhesive, it is natural that the bonding strength increases due to the adhesion force, but in addition to this, the bonding strength improves due to the mechanism in which the fastener is prevented from tilting. In riveting, the clearance-filling effect showed an 33% improvement in LSS, even if the adhesion force caused by adhesive was excluded. In bolting, the bonding strength increased to twice the original value using the structural adhesive in the clearance, along with the washer to prevent the deformation of the steel. With the washer, the clearance-filling effect alone showed a 10% improvement in LSS.
3. The clearance-filling effect reduces the stress applied to the rivet and bolt when subjected to external force. So, this effect was significant in the case of the rivet that fractured in the fastener. Even in the case of the bolt, in which fracture did not occur in the fastener, the deformation of the bolt was reduced, resulting in less deformation of the steel where fracture occurred, leading to improved bonding strength.
4. Various cases were numerically analyzed to verify the above findings. In riveting, the maximum stress exerted on the rivet was reduced by clearance-filling, in accordance with the experimental results. Similar trends of numerical analysis results were obtained in bolting. One thing to note is that the use of washers in bolting had the same effect as the stress reduction on the bolt caused by the clearance-filling effect.
5. The results show the necessity of preventing the inclination of the fastener during mechanical fastening between dissimilar materials and reveal the mechanism that can help develop an efficient fastening method in the future.  
Conceptualization, H.-M.Y.; Validation, D.-J.K.; Formal analysis, J.P.; Investigation, J.P.; Data curation, D.-J.K.; Writing - original draft, H.-M.Y.; Visualization, D.-J.K.; Supervision, H.-M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the research grant of the Gyeongsang National University in 2022-2023, the Technology development Program (S3269454) funded by the Ministry of SMEs and Startups (MSS, Korea), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1G1A1006606).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Cytocompatibility and Cell Viability  
The cell viability on the scaffolds underwent quantification analysis by MTT after culturing for 3 days (Figure 9). The cells proliferated more actively in groups CS-B and CS-C than other groups and reached a maximum value in group CS-C, which indicated that this group was more advantageous for cell growth than others.  
The cell adhesion and spreading experiments on group CS-C indicated that the cell growth included three main stages (Figure 10). Firstly, attachment: at this stage, cells were still spherical morphology and no filopodia (Figure 10a); secondly, partly spreading: cells started to extend locally and connect by filopodia. Besides, a small quantity of the extracellular matrix was synthesized and secreted by cells (Figure 10b); lastly, spreading and flattening: the cells spread completely and connected with each other (Figure 10c). These results indicated that the cells attached on the scaffolds by means of filopodia and changed their morphologies to contact with the cell-scaffold interface.  
Figure 10: Cell attachment and spreading on the scaffolds: (**a**) attachment, (**b**) spreading and (**c**) flattening (left: graphical representation and right: micrographs).
## 4 Analysis of Calculation Results
### Deformation Analysis of Existing Bridge Piers  
In each pier on the two left and right observation points, close to the tunnel excavation side and away from the tunnel excavation measurement, respectively, the construction stage pier observation points in each process of the vertical change time curve are drawn, as shown in Figure 10.  
On the whole, the trend of deformation of piers 71#-74# in each construction stage is caused by settlement. The deformation law is analyzed. As the construction progresses, the deformation of each pier gradually increases. For construction stages one, two, ten, and eleven, the influence of tunnel excavation on the existing structure is small, and the deformation value of the existing pier changes little. The main deformation of each pier occurs from process three to process seven. The maximum deformation value occurs in piers 72# and 73#, that is, two piers adjacent to the new tunnel, which has no obvious effect on other piers; among them, the maximum deformation value of pier 72# is \(-1.73\) mm and the maximum deformation value of pier 73# is \(-1.70\) mm, with both occurring in process 11.  
In order to more intuitively see the change in the maximum deformation value of each pier in each construction process, the maximum deformation value of each pier in each process is extracted to draw the vertical deformation time history curve, as shown in Figure 11. The deformation of piers 71#, 72#, 73#, and 74# is analyzed with monitoring points.  
Figure 10: Vertical deformation curve of the existing pier.  
Figure 9: Construction process simulation.  
It can be seen that piers 72# and 73# adjacent to the new tunnel are most affected by the entire tunnel excavation. Because the left line of the new tunnel is excavated two processes later than the right line, the deformation of pier 74# has a certain lag compared to pier 73#. The fourth process is the excavation of the right line tunnel to the point of the underpass, so the right line tunnel excavation in the third to fifth stages of the process has the greatest impact on the deformation of pier 72#, and the deformation value accounts for 68.9% of the total. Process 6 is the excavation of the left line tunnel to the point of the underpass; from process 5 to process 7, the right line tunnel excavation has the greatest influence on the deformation of pier 74#, accounting for 77.8% of the total deformation value. In the sixth process, the settlement of pier 72# tends to be stable after the excavation face of the right line tunnel passes through the point of the underpass and advances forward about 16 m. Process eight is the excavation of the left line tunnel face through the point of the underpass and advances forward about 16 m. The differential settlement between piers occurs between 72# and 71#, and 73# and 74#, and the differential settlement is 1.73 mm and 1.70 mm, respectively.
## 4 Evaluation
### Setup  
To simulate the behavior of the vehicles in a simulation environment as accurately as possible, the actual road information should be reflected in the simulation environment. In particular, the slope of the road has to be considered because it significantly affects the fuel consumption of the vehicle. Therefore, we used survey equipment to survey the map data used for the simulation, including the altitude. The driving route (marked with the red line) along with the traffic lights are shown in Figure 6. Based on the map data surveyed, an urban intersection driving environment was implemented within the ASM ModelDesk.  
Figure 5: Flowchart of cooperative eco-driving speed guidance application.  
Figure 6: Driving route on map with traffic lights.  
To simulate the behavior of realistic V2X communication in a simulation environment, the channel in which the actual DUTs are tested should be applied to the simulation environment. We analyzed the performance of the V2X communication in an urban intersection driving environment through field tests using V2X communication devices. Based on the results of the analysis, the characteristics of the V2X communication channel were configured within QualNet. Figure 7 shows the packet error rate of the IEEE 802.11p channel configured within QualNet based on field test results. To model the IEEE 802.11p channel in the urban LOS environment, the reliable path loss and fading models are chosen. For the path loss model, the CORNER model is used as follows:  
\[\mathrm{PL}=\ 20\log\biggl{(}\frac{\lambda}{4\pi\mathrm{d}}\biggr{)} \tag{1}\]  
Where \(\lambda\) is the wavelength of the propagating signal, and \(\mathrm{d}\) is the distance between the HV and traffic light [28]. For the fading model, the Nakagami-m model is used. The received fading signal envelope probability density function of the Nakagami-m distribution is given as follows:  
\[\mathrm{f_{R}(r)}=\frac{2^{m}r^{2m-1}}{\Gamma(m)\Omega^{m}}\mathrm{e}^{-( \overline{R})r^{2}} \tag{2}\]  
where \(\Gamma(.)\) is the gamma function,  
\[\Omega=\mathrm{E}\bigl{[}\mathrm{R}^{2}\bigr{]}=\overline{\mathrm{R}^{2}} \tag{3}\]  
which is the average signal power, and  
\[m=\frac{\left(\overline{\mathrm{R}^{2}}\right)^{2}}{\left(\mathrm{R}^{2}- \overline{\mathrm{R}^{2}}\right)^{2}}\geq\frac{1}{2}, \tag{4}\]  
which is the Nakagami distribution shape factor that controls the severity or depth of the fading [29].  
The parameters related to the simulation, including the specifications of the HEV model to be used in the simulation, modeled using the ASM, are defined in Table 1.  
Figure 7: Packet error rate of V2X communication channel in the urban LOS environment.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 3 Results and Interpretation
### GNSS Buoy Track  
The buoy tracks during tidal cycles A and B (Figures 3 and 4, respectively) each display a rotational movement around the anchor point. There is a consistent northerly component during both flood tides (tidal cycle A 18:00-21:30, tidal cycle B 06:30-10:30) with a small increasing component towards the shore suggesting strong alongshore currents during the flood tide. During tidal cycle A, there was a consistent moderate breeze (11-15 knots) from the west; during tidal cycle B, there was an increasing fresh breeze (17-21 knots) from the south-west. The weather conditions also would have contributed to the dominant northerly orientation during the flood tide.  
Shortly after high water in tidal cycle A, the rotation increases clockwise towards the shoreline, with its closest position to the shore being around 22:00, around 90 min after high water. It then continues to rotate clockwise until 23:00 when it then begins an anti-clockwise rotation passing the same position around 00:30. A similar pattern can also be seen with tidal cycle B. This could indicate water recirculation as tidal currents decrease, and being constrained by the short length (\(\sim\)100 m) rock groynes both to the north and south of the site. It is interesting to note that the majority of good consistent solutions (Q1) occurred shortly after high water and during the ebb tide, while the poorer solutions (Q2) occurred during the flood tide.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fixed Q1\%** & **Float Q2\%** \\ \hline Tidal cycle A & 57.0 & 43.0 \\ Tidal cycle B & 48.4 & 51.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage of fixed and float solutions.  
Figure 4: Buoy track during tidal cycle B, 29 August 2019 06:00–14:30. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.  
Figure 3: Buoy track during tidal cycle A, 18:00 28 August–02:00 29 August. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.  
Figure 5: Buoy positional components during tidal cycle A; East–West (E-W), North–South (N-S) and Up–Down (U-D), 18:00 28 August–02:00 29 August. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.  
Figure 6: Buoy positional components during tidal cycle B; East–West (E-W), North–South (N-S) and Up–Down (U-D), August 29 2019 06:00–14:00. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.  
Figure 8: Buoy vertical accelerations during tidal cycle B; Up–Down (U-D), 29 August 06:00–14:30. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.  
Figure 7: Buoy vertical accelerations during tidal cycle A; 18:00 28 August–02:00 29 August. Fixed solutions (Q1) are shown in green, and float solutions (Q2) are shown in orange.
# On Fast Converging Data-Selective Adaptive Filtering
## 3 Data-Selective Adaptive Filtering Algorithms
### Simulation 2: Prediction  
The dataset used in this subsection is taken from anemometer readings provided by Google's RE < C Initiative [21]. The data consists of the wind's speed recorded by five sensors on 25 May 2011. The dataset is split into 40 sets of size 8192 in order to use the Monte Carlo method.  
The performance of the MSE is verified in Figure 4a using \(P_{\mathrm{up}}=0.4\). The parameters for the DS-CG algorithm is \(\lambda=0.98\) and \(\eta=0.48\). Both the DS-LMSN and DS-LMSQN algorithms employed \(\
u=0.1\) and LMSN utilized \(\theta=0.1\). All algorithms obtain a similar convergence with 40 independent runs, but the DS-LMSQN algorithm achieves better performance due to its faster convergence to the steady state. The employed adaptive filter order is \(N=7\).  
In Figure 4b, the prescribed and observed probabilities of update are compared. We can observe that all DS algorithms obtained an observed probability of update close to the prescribed one.  
The output of the prediction is illustrated in Figure 5a for \(P_{\mathrm{up}}=0.4\) and Figure 5b for \(P_{\mathrm{up}}=0.7\) between iterations 8000 and 8150. In both the cases, it was observed an acceptable performance in the prediction, leading us to conclude that even if we perform a reduced number of updates, data selection algorithms achieve an accurate prediction.
## 4 Results and Discussion
### Cross-Validation of Results for Accuracy
The accuracy was measured using a random forest algorithm, which is known for its high accuracy and robustness in handling missing values and outliers. The dataset consisted of 197 students enrolled in a Bachelor of Engineering program, and the data were split into training and testing sets with various test sizes, ranging from 0.1 to 0.5, to evaluate the model's performance on unseen data. Figure 19: Individual factors’ importance in relation to student attrition.
## 2 Materials and Methods  
### Experimental Design  
The purpose of the test was to compare the effectiveness of UGV performance using two different complex trials with three alternative camera placement configurations (Figure 1). In each configuration, we used a horizontal FOV of approximately 58\({}^{\circ}\) for all three cameras to ensure the maximum viewing angle, which, according to [PERSON] (1973) [28], allows for the recognition of objects or details. The number of cameras has been limited to 3 due to the limited bandwidth of the video link.  
The first of the camera configurations corresponds to the typical, standard camera setting used in light robots. The main camera is positioned in the front UGV, while the side additional cameras enable control over selected zones in the robot's immediate surroundings, particularly those near the running gear. Horizontal positioning of the main camera provides the opportunity to observe not only the road (ground) but also buildings and other high objects, which helps to improve situational awareness. Side cameras have been designed to improve the ability to avoid obstacles. They are directed to the front, which in a natural way widened the field of view of the main camera. The side cameras are tilted down by 20\({}^{\circ}\) to better align their field of view (FOV) with the observation area, especially when obstacles are avoided.  
The second configuration corresponds to the operator's field of view from within the vehicle. The cameras are arranged in a horizontal configuration, resulting in a panoramic image that has a field of view (FOV) of 174\({}^{\circ}\) horizontally and 45\({}^{\circ}\) vertically. This design enables lateral viewing and facilitates the construction of cognitive maps. The setup should allow for a comparison of the teleoperation efficiency of the regular method with a panoramic wide field of view (FOV) solution that incorporates a car hood, thereby greatly improving situational awareness.  
The third configuration has a main camera placed centrally in the driver's seat and two side cameras, similar to the first configuration, improving obstacle avoidance. This allows for a direct comparison of the effectiveness of UGV teleoperation with the camera in front of and centrally over the vehicle.  
The system with the camera in front of the UGV provides visibility up to 2 meters ahead of the vehicle, while the centrally placed camera provides visibility up to 3 meters ahead of the vehicle.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background
### Low Power Wide Area Network (LPWAN)
#### 2.1.3 NB-IoT  
When compared with other LPWANs that use unlicensed band, NB-IoT utilizes the same frequencies as long term evolution (LTE), and it satisfies the need of IoT communication in a cellular system with low-complexity and low-throughput. To reduce device cost and minimise the battery consumption, LTE features, such as handover, carrier aggregation, and channel quality measurements, are removed in NB-IoT to simplify and optimize the communication procedure [14].