esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experimental Results
#### 3.2.3 Comparison of Classification Models  
In this study, we compared multiple classification models. In order to represent the effectiveness of each model as much as possible, we did not use any pre-trained models but trained each model from scratch. We selected a variety of commonly used classification algorithms and used normalised parameter settings in our experiments to ensure fairness. We chose the parameters of the traditional machine learning algorithm considering the number of features of the data and the fact that the occurrence of overfitting should be avoided. The ggap in the GA algorithm indicates the survival ratio of the next generation and usually takes a value from 0.6 to 0.9. In this paper, the best experimental result is 0.7, which is chosen as the value of ggap. The selected parameters are shown in Table 2. The detailed results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Time** \\ \hline GA & 0.28353 s \\ SMA & 0.15394 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm runtime.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Parameters** \\ \hline \multirow{4}{*}{DT} & Node splitting evaluation criteria = gini \\  & Feature division point selection criteria = random \\  & Minimum samples for internal node splitting = 2 \\  & Minimum samples in leaf nodes = 1 \\  & Maximum leaf nodes = 2 \\  & Maximum depth of the tree = 14 \\ \hline \multirow{4}{*}{RF} & Node split evaluation criterion = gini \\  & Number of decision trees = 5 \\  & Minimum samples in leaf nodes = 1 \\  & Maximum depth of the tree = 14 \\  & Maximum leaf nodes = 2 \\ \hline \multirow{4}{*}{SVM} & kernel = \(r\)bf\({}^{\prime}\) \\  & C = 20 \\  & \(\gamma\) = 2.00 \\ \hline \multirow{4}{*}{GA} & ga\_option.maxgen = 50 \\  & ga\_option.sizepop = 5 \\  & ga\_option.ggap = 0.7 \\ \hline \multirow{4}{*}{SMA} & pop = 5 \\  & dim = 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected parameters of each algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **TrainAcc** & **TestAcc** & **Precision** & **Recall** & **F1 Score** \\ \hline DT & 0.793 & 0.800 & 0.816 & 0.793 & 0.788 \\ RF & 0.948 & 0.850 & 0.868 & 0.864 & 0.864 \\ SVM & 0.810 & 0.900 & 0.859 & 0.810 & 0.799 \\ GA-SVM & 1.000 & 0.925 & 0.928 & 0.931 & 0.930 \\ SMA-SVM & 1.000 & 0.975 & 0.974 & 0.977 & 0.975 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of model experiments.
## 1 Introduction
### Discussion  
#### 3.2.1 Single-Factor Analysis  
According to the analysis of variance in Tables 4 and 5, the results show that the four single factors A, B, C, and D have significant effects on the standard deviation of velocity mean and the standard deviation of lateral displacement (\(p<0.05\) is significant, \(p<0.001\) is extremely significant). Among them, the single factor that has the greatest influence on the standard deviation of the mean velocity is B height and D plant spacing: the factor that has the greatest influence on the standard deviation of the mean lateral displacement is the C-side distance, and the \(p\)-value is less than 0.001. The interaction items AB and BD have a significant effect on the standard deviation of the mean velocity, and the interaction item CD has a significant effect on the standard deviation of the mean lateral displacement.  
The degree of influence of the four factors on the standard deviation of speed, from least to greatest, is determined by roadside distance, color, plant spacing, and height. Utilizing the BBD response surface methodology, the individual impact curves of the four factors on speed were obtained, as shown in the figure. From Figure 8, it is evident that plant spacing and height have the greatest impact on speed. Through inquiries into drivers' experiences, it is known that when the plant spacing is too wide, drivers perceive an open field of vision and subconsciously accelerate. Conversely, when the plant spacing is too narrow, drivers perceive the road as too narrow, leading to a reduction in speed for safe passage. Additionally, excessive plant height creates a psychological burden for drivers, increasing their tension and affecting speed control. Conversely, inadequate plant height fails to integrate well with the surrounding environment, creating a sense of dissonance for the driver and consequently impacting speed control.  
The degree of influence of the four factors on the standard deviation of lateral displacement, from least to greatest, is determined by color, plant spacing, height, and roadside distance. Utilizing the BBD response surface methodology, the individual impact curves of the four factors on lateral displacement were obtained, as shown in Figure 9. From the graph, it is evident that height and roadside distance have the greatest impact on lateral displacement. Through inquiries into drivers' experiences, it is known that as the height increases and the roadside distance decreases, drivers tend to drive closer to the road midline, leading to increased driver tension and greater lateral vehicle displacement. Conversely, as the height decreases and the roadside distance increases, the influence of the plant landscape on the driver diminishes, failing to guide the road alignment, resulting in increased speed and subsequently increased lateral displacement.  
Figure 8: The impact curves of color, height, roadside distance, and spacing on the standard deviation of speed.  
Figure 9: The impact curves of color, height, roadside distance, and spacing on the standard deviation of lateral displacement.
## 3 Results
### Characterization of the Materials
#### 3.1.2 SEM Analysis  
Figure 2 shows SEM images obtained for raw luffa, LAC sample and TiLAC-x/y composites. Figure 2 shows that raw luffa presents a homogeneous appearance, with a rich layer of external lignin around the fibers, as already demonstrated in previous work [39]. In addition, the sponge is slightly fibrous with irregular structure and certain cracks and holes. Figure 2 shows the typical morphology of an activated carbon [40], with the pores created due to the carbonization and activation with phosphoric acid treatments. Figure 2,d display SEM micrographs for the two prepared composites, from which it can be noticed that both phases are in close contact and TiO\({}_{2}\) is highly dispersed, covering most of the activated carbon surface homogenously. Note that the white areas are assigned to Ti particles, while the black and gray parts correspond to carbon. Furthermore, no agglomeration of Ti particles on the LAC surface was observed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \(\mathbf{S_{BET}}\) & \(\mathbf{V_{DR}}\)\(\mathbf{N_{2}}\) & \(\mathbf{V_{MESO}}\) & \(\mathbf{V_{DR}}\)\(\mathbf{CO_{2}}\) & \(\mathbf{V_{Total}}\) \\  & **(m\({}^{2}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** & **(cm\({}^{3}\) g\({}^{-1}\))** \\ \hline Ti (P25) & 60 & 0.02 & 0.08 & 0.02 & 0.10 \\ LAC & 1172 & 0.54 & 0.64 & 0.21 & 1.18 \\ TiLAC-7/3 & 378 & 0.16 & 0.28 & 0.07 & 0.43 \\ TiLAC-9/1 & 161 & 0.07 & 0.20 & 0.03 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural parameters for P25 TiO\({}_{2}\), the prepared luffa activated carbon (LAC) and TiLAC composites, obtained by N\({}_{2}\) adsorption–desorption at \(-\)196 \({}^{\circ}\)C and CO\({}_{2}\) adsorption at 0 \({}^{\circ}\)C.  
Figure 1: (**a**) Adsorption–desorption isotherms of N\({}_{2}\) at \(-\)196 \({}^{\circ}\)C and; (**b**) pore size distribution of the prepared luffa activated carbon and TiLAC composites.
## 3 Materials and Methods
### Digital Mobile Navigation Apps Used  
Two digital navigational aids were used for the experiment: Google Maps was adopted as an outdoor-only navigation aid, and MazeMap was used as an indoor-outdoor navigation aid. Participants used a smartphone to run the application for navigation during the wayfinding task.  
Google Maps is one of the most popular navigation aids and enables people to navigate to their desired destination by providing the shortest and most convenient route. However, Google Maps is only limited to outdoor navigation. It has many helpful features, such as street view and the location of nearby cafes, hospitals and police-stations [41]. MazeMap is an indoor-outdoor positioning and navigation system. It can locate the position of the user on campus and provides room-level wayfinding and object search capabilities. Moreover, when the user is outdoors, MazeMap is also able to use standard GPS positioning, and the system is also able to combine WiFi and GPS signals if available. The system uses construction drawings on the user interface side to present the users with readable maps and interprets them to recognize different objects. The user can choose what to hide or show so the maps are not overloaded with unnecessary technical data [8].
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 5 Tabu-Search-Based Enhanced Rosenbrock Algorithm  
Tabu Search (TS) algorithm was proposed by [PERSON]. TS is characterized by the use of tabu tables to block access to recently searched solutions [23]. This feature solves the repeated search problem found in the previous section. Given that the mm Wave channel is sparse and clustered, the beam gain exhibits clustered peaks at distinct local positions in beam index space, which represents the directions of the corresponding rays. To make full use of the local search ability of RA when combined with TS algorithm, mapping from the beam index to search index for search need to be defined.
## 4 Discussion
\begin{table}
\begin{tabular}{c c} \hline
**Constituent (unit)** & **Description** \\ \hline Alkanility mg as CaCO3 & as is \\ BGA-PC-Cells/mL & BlueGreen Algae (Cyanobacteria) probe response as cell density \\ BGA-PC-RFU & BlueGreen Algae (Cyanobacteria) probe response as relative fluorescence unit \\ Chl-Hg/L & Chlorophyll probe response as concentration \\ Chl-RFU & Chlorophyll probe responses as relative fluorescence unit \\ Depth-m & as is \\ DNH4 (ugN/L) & Dissolved Ammonium \\ DNO2-3 (ugN/L) & Dissolved Nitrite-Nitrate \\ DOC (mg/L) & Dissolved organic Carbon \\ DRP (ugP/L) & Dissolved Reactive Phosphorous (or filtered Ortho-P) \\ hardness mg as CaCO3 & as is \\ HCO3-(est) (ppm) & Bicarbonate \\ MC-LR CV & Coefficient of variation of environmental sample run in triplicate \\ MC-LR Stdev (3 reps) & Standard deviation of environmental sample run in triplicate \\ Microcystin-LR equivalent & Microsytin IC equivalents measured by ELISA as concentration (PPB) \\ ODO-mg/L & Optical Dissolved Oxygen probe measurement as concentration \\ ODO Sat \% & Optical Dissolved Oxygen probe measurement as percent saturation \\ OM percent (\% OM) & \% organic matter of suspended solids \\ pH & as is \\ pH (@Alkalinity measurement) & as is \\ PN (ugN/L) & Particulate Nitrogen \\ POC (mg/L) & Particulate Organic Carbon \\ PP (ugP/L) & Particulate Phosphorous \\ PRP (ugP/L) & Particulate Reactive Phosphorous \\ RCe,a (ug/L) & Phaephyton Corrected Chlorophyll A measured as absorbance on filtered and \\ extracted samples \\ RCHLA (ug/L) & Uncorrected Chlorophyll A measured as absorbance on filtered and extracted samples \\ RCHLB (ug/L) & Uncorrected Chlorophyll B measured as absorbance on filtered and extracted samples \\ RCHLC (ug/L) & Uncorrected Chlorophyll C measured as absorbance on filtered and extracted samples \\ RPEa (ug/L) & Phaephyton measured as absorbance on filtered and extracted samples \\ Sonde Number & as is \\ SpCond-ud/S/cm & Specific Conductance in microsemens per cm \\ TDN (ugN/L) & Total Dissolved Nitrogen \\ TDP (ugP/L) & Total Dissolved Phosphorous \\ Temp-\({}^{\circ}\)C & as is \\ Time & as is \\ TN (ugN/L) & Total Nitrogen \\ TNH4 (ugN/L) & Total Ammonium \\ TOC (mg/L) & Total Organic Carbon \\ TP (ugP/L) & Total Phosphorous \\ TRP (ugP/L) & Total Reactive Phosphorous (or Unfiltered Ortho-P) \\ TSS (mg/L) & Total Suspended Solids \\ Turbidity-NTU & as is \\ VSS (mg/L) & Volatile Suspended Solids \\ \hline \end{tabular}
\end{table} 
Table 5: Water Quality Parameters Investigated for covariation with BGA. Sentinel-3/MERIS/OLCI (Table S4) or indirectly via the Chl-a signature with Sentinel-2 and to a lesser extent Landsat-8 [6].
## 4 Performance Evaluation
### Experimental Environment and Parameter Settings  
To evaluate the algorithm's performance, the _NS2_ simulation environment was selected, the computer operating system was Win 10, the CPU's main frequency was 5.5 GHz, and there were 16 GB of memory. The mobile network simulation scene was set to a square area of 100 m \(\times\) 100 m, and 20 mobile nodes were randomly set within the region. The motion of a node was based on the random waypoint motion model [25], with a maximum motion speed of 10 m/s. Each node had a communication radius of 30 m. Experimental simulation parameters are shown in Table 3. The values of the immune game model are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Attack Type**} & \multirow{2}{*}{**Attack Code**} & \multirow{2}{*}{**Attack Name**} & \multicolumn{2}{c}{**Size of**} & \multicolumn{1}{c}{**Size of**} \\  & & & & **Training Set** & **Testing Set** \\ \hline
0 & NOM-AL & 0 & / & 97,278 & 60,593 \\
1 & PRO-BING & & / & 4107 & 4166 \\  & & 1 & Ipsweep & 1247 & 306 \\  & & 2 & mscan & - & 1053 \\  & & 3 & nmap & 231 & 84 \\  & & 4 & Portsweep & 1040 & 354 \\  & & 5 & saint & - & 736 \\  & & 6 & satan & 1589 & 1633 \\
2 & DOS & & / & 391,458 & 229,853 \\  & & 7 & Apache2 & - & 794 \\  & & 8 & back & 2203 & 1098 \\  & & 9 & land & 21 & 9 \\  & & 10 & mailbomb & - & 5000 \\  & & 11 & neptune & 107,201 & 58,001 \\  & & 12 & pod & 264 & 87 \\  & & 13 & Processable & - & 759 \\  & & 14 & smurf & 280,790 & 164,091 \\  & & 15 & teardrop & 979 & 12 \\  & & 16 & UDPstorm & - & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partial samples in 10% KDDCup99 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Item**} & \multicolumn{1}{c}{**Value**} & \multicolumn{1}{c}{**Item**} & \multicolumn{1}{c}{**Value**} \\ \hline network simulation area & 100 m \(\times\) 100 m & size of data packet & 150 bytes \\ Initial energy of nodes & 50 J & transmission rate & 500 Kpps \\ Node distribution & random site & communication mode & TDMA \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter set of experiment.
## 3 SOH Estimation Methods
### Deep Learning Methods
#### 3.2.3 Construction of a Neural Network Model  
Figure 7 shows the SOH estimation process for the LIB using its discharge signals. The voltage and temperature data of the battery are preprocessed using wavelet transform and features are extracted. The extracted data is used as an input for training a neural network model for estimating the SOH. In this case, the CWT transforms the 1-D signal of the battery into 2-D image data using the Bump Wavelet function.  
Additionally, the DWT applies the Daubechies Wavelet 10 and extracts the Approximation level 1 signal. After that, data using CWT is put into the CNN model as an input, and data using DWT is put into the LSTM as an input to estimate the SOH.  
The first layer of the constructed CNN for estimating the SOH of the LIB had eight 3 \(\times\) 3 kernels, the second layer had 16 3 \(\times\) 3 kernels, and the third and fourth layers had 16 kernels each. Furthermore, additional features were extracted through the fully connected layer, and the SOH information of the LIB was estimated through the regression layer. After completing the convolutional operations in each layer, we applied max pooling, which is a type of pooling layer for batch normalization and computational speed improvement, to overcome the vanishing and exploding gradient problem. Then, we applied the drop-out to prevent the overfitting of the model [34]. The model construction of CNN is shown in Table 2.  
Figure 6: Structure of LSTM.  
The LSTM neural network for estimating the SOH of the LIB consisted of a total of 125 cells. The state activation function of the LSTM neural network cells was constructed using a hyperbolic tangent, and the gate activation function was constructed using a sigmoid function. Furthermore, additional features were extracted through the fully connected layer, and the SOH information of the LIB was estimated through the regression layer. The model construction of LSTM is shown in Table 3.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
###### Abstract  
The emergence of optical wireless networks (OWNs) is a potential solution to the quest for the increasing bandwidth demand. Existing bandwidth assignment strategies are not suitable for OWNs, considering factors such as differences between the physical properties of radio networks and OWNs. In order to eliminate collision, minimize delay and enhance system utilization and fairness, we propose the non-contention bandwidth assignment protocol called adaptive polling medium access control (APMAC) protocol for OWNs. The APMAC protocol involves association, data transmission and dissociation phases. Moreover, the APMAC protocol exploits features of the IEEE 802.15.7 visible light communication (VLC) standard. While assigning bandwidth to the visible light nodes (VLNs), the visible light access point (VLAP) establishes a polling table that contains the identity, buffer size and round-trip time of each VLN that issued bandwidth request. The contents of the polling table enable the computation of the maximum transmission unit and time-slot for each VLN that requests bandwidth assignment. In order to achieve convincing results, we simulate the protocol under varying network sizes ranging from 1 to 10 VLNs per access point, then we compare the results against the medium transparent medium access control (MT-MAC) protocol that is a non-contention MAC protocol. We demonstrate numerical results of our study considering average waiting time, packet collision, system utilization and fairness. Numerical results reveal that the APMAC protocol outperforms the MT-MAC protocol.  
Keywords:optical wireless network (OWN); visible light communication (VLC); medium access control (MAC); light emitting diode (LED); IEEE 802.15.7 Standard +
Footnote †: journal: Applied
## 4 Discussion
### Recommendations for Segments Design Criteria  
The simulation analysis revealed similar effects of tunnel depth, soil density, soil Poisson's ratio \(\upmu\), and lining thickness on the internal forces of both the segments and the initial support. The impact of burial depth on the internal forces of the initial support structure was concentrated in the range of 33-36%, while the influence of soil density was concentrated in the range of 44-45%. The soil Poisson's ratio \(\upmu\) also affected the internal forces of the initial support, as it fell within the declining stage. On the other hand, the effect of lining thickness on the segment's forces was weaker compared to the initial support.  
Overall, the changes in conditions within this project had a lesser impact on the internal forces of the segments compared to the initial support. Therefore, the design standards for the segments can complement those of the initial support. Although the influence of backfill thickness on the internal forces of the segments was relatively balanced and concentrated in the range of 4-7%, it is crucial to control the backfill void to be no less than approximately 300 mm. This is the point where the maximum axial force mutation occurs, and maintaining this threshold helps prevent structural stress mutations. It is evident that the time factor plays a crucial role in the assembly of pipe pieces into rings and gap backfill processes. This factor significantly impacts the forces and displacements experienced by the pipe pieces. Therefore, further investigation into the use of integrated assembled structural bodies in similar projects is recommended for future stages.
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Networking  
#### 4.1.1 Internet of Things (IoT)  
Internet of Things (IoT) is a new revolutionary technology that enables the connection of all the everyday physical objects to the internet. Thus, IoT can constitute a huge global network with a lot of uniquely connected things, as depicted in Figure 2, which communicate with each other for a specific operation (e.g., in a medical application), providing a high impact on a society, since it improves the humans' Quality of Life (QoL) and Quality of Experience (QoE). Generally, IoT has three components: smart devices, communication networks to provide connectivity to the devices, and computer systems.  
Remarkably, the IoT-based devices can be wearables and have application in healthcare, providing a full range of new capabilities thanks to pervasive connectivity. Such medical wearables include medical devices, sensors, smartphones, imaging devices, personal digital assistants (PDAs), and electronic health records (EHRs) that are able to have a great impact on medicine as providing significant potential to improve patients' lives and doctors' work globally. IoT-based wearables can be used to monitor patients remotely, and hence, enable early diagnosis of an epidemic disease or pandemic, such as the recent COVID-19 [30].  
Today, the industry is moving towards replacing human resources for measurements and research using electronic Patients Reported Outcomes (ePRO) and electronic Clinical Outcome Assessment (eCOA) [31]. In medicine and specifically in medical research labs, an IoT-based platform can provide higher quality but more affordable healthcare solution globally, reducing the workflow of medical staff [32].