esa-sceva/satcom-chunk-collection huggingface dataset:
# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 4 Discussion  
Our study demonstrates the efficacy of using convolutional neural networks (CNNs) for classifying cognitive states based on EEG data in the 30-40 Hz gamma band range. The model's high accuracy (91.42% on the test set) and balanced precision (71.41%) and recall (72.51%) indicate its potential as a reliable tool for EEG analysis in both research and clinical settings. These results not only align with recent advancements in deep learning applications for EEG data analysis but also surpass the performance of other machine learning methods such as support vector machines and random forests, which typically achieve accuracies between 70-85% for similar EEG classification tasks [21,22]. A key advantage of our CNN-based approach is the significant reduction in processing time compared to traditional manual methods, as shown in Table 1, with time savings of 68% in preprocessing and classification completed in under a second regardless of sample size. This efficiency gain enables increased research productivity, potential real-time cognitive state monitoring, and improved scalability across various study sizes. The model's ability to detect specific brain activity patterns associated with high-level cognitive functions opens up new possibilities for understanding cognitive processes and their neural correlates. However, limitations such as the need for larger, more diverse datasets and potential overfitting issues should be addressed in future research. Additionally, expanding the analysis to other frequency bands and exploring multi-class classification could further enhance the model's applicability. This study represents a significant step towards real-time cognitive state detection, with potential impacts spanning from basic neuroscience research to practical applications in education, brain-computer interfaces, and clinical diagnostics. Our approach has significantly enhanced and accelerated the exploration of brain activity and neural network behavior. This advancement paves the way for more efficient replication of brain activity patterns in artificial systems, potentially revolutionizing the development of optical chip technologies and sophisticated artificial intelligence applications.
## 4 Simulation Results
### Performance Comparison with the Algorithm of Chakraborty (Energies, 2019)
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**PopSize** & **Gens** & \multicolumn{4}{c}{**Best-of-Run Fitness**} & \multicolumn{4}{c}{**FirstHiteFvals**} \\ \cline{2-13}  & **Best** & **Mean** & **Std Dev** & **Success** & **Best** & **Mean** & **Std Dev** \\ \hline
20 & 10 & 13.6162 & 13.6847 & 0.0677 & 6 & 127 & 184.0 & 29.2461 \\
15 & 20 & 13.6159 & 14.6855 & 10.5138 & 64 & 116 & 249.6094 & 84.8868 \\
20 & 20 & 13.6159 & 13.6276 & 0.0336 & 69 & 127 & 322.4203 & 62.6424 \\
20 & 25 & 13.6158 & 13.6225 & 0.0278 & 86 & 127 & 348.9767 & 78.3951 \\
25 & 40 & 13.6157 & 13.6176 & 0.0136 & 97 & 89 & 486.6598 & 149.3530 \\
40 & 25 & 13.6157 & 13.6203 & 0.0143 & 81 & 267 & 693.6543 & 189.3033 \\
20 & 100 & 13.6157 & 13.6159 & 0.0005 & 100 & 127 & 422.63 & 236.1126 \\
100 & 20 & 13.6159 & 13.6203 & 0.0049 & 61 & 365 & 1422.6230 & 434.6554 \\
30 & 100 & 13.6157 & 13.6159 & 0.0006 & 99 & 175 & 581.8589 & 200.6175 \\
100 & 30 & 13.6157 & 13.6177 & 0.0022 & 86 & 365 & 1723.3721 & 616.9152 \\
40 & 100 & 13.6157 & 13.6159 & 0.0004 & 100 & 267 & 829.13 & 383.5640 \\
100 & 40 & 13.6157 & 13.6170 & 0.0019 & 92 & 365 & 1893.6522 & 743.6137 \\
100 & 100 & 13.6157 & 13.6162 & 0.0010 & 99 & 365 & 2136.2727 & 1335.2097 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of [PERSON] on the fuel cell problem (each row corresponds to 100 independent runs). Most numbers are shown with rounding at the fourth place after the decimal. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Metric** & **\#zero Diff.** & \(n\) & \(W+\) & \(W-\) & \(W\) & \(\alpha\) & **Critical \(W\)** & **Mean of \(W\)** & **Std. Dev. of \(W\)** & **\(z\)-Statistic** & **Left Tail \(p\)** \\ \hline Mean of Best-of-Run Fitnesses & 1 & 12 & 42 & 36 & 36 & 0.05 & 17 & 39 & 12.7476 & \(-\)0.2353 & 0.4070 \\ Mean of FirstHiteFvals & 0 & 13 & 83 & 8 & 8 & 0.05 & 21 & 45.5 & 14.3091 & \(-\)2.6207 & 0.0044 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: [PERSON] signed rank tests: Algorithm of [24] vs. Saya on the fuel cell problem.
# Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective
## 5 Evaluation and Experiments
### Rq1: Gamma Correction Reduces the Energy Consumption of the Mobile App  
Traditionally, gamma correction is used to manipulate the pixels of a digital camera image, due to which the human eye can view a fully illuminated image [18]. However, in this work, we use correction on mobile ads to reduce the size and adjust the pixels of the image to reduce the quality of mobile ads. Moreover, the quality is maintained up to the point where the content can read easily, hence, it is a \"win-win\" situation for the developer, end user, and third party Google Store. Figure 3 shows that the energy consumption of gamma-corrected applications is low compared to other non-corrected versions of the applications.  
Rq2: Gamma Correction Efficiently Increases the Battery Lifetime and Performance of the Mobile Device  
According to existing research on the mobile ecosystem [3; 4; 5; 6; 7], we can say that the energy consumption of the mobile device has a nonlinear relationship with the lifetime of battery and performance of the mobile device. Similarly, Figure 4 illustrates, after gamma correction on mobile applications, that it consumed less mobile battery. Hence, the gamma correction increases the lifetime of mobile battery and the performance of the mobile device. Thus, our technique provides a way for a developer to reduce the hidden energy cost of the mobile application.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Purpose** \\ \hline Android profiler & Recording energy consumption \\ Trapen profiler & Recording energy consumption \\ Android studio & For Code \\ Device for experiment & Q mobile, Samsung core prime, Huawei mate 10 lite \\ Matlab & Pictorial representation of findings \\ RERAN tool & Recording the manual generated workload \\ Android applications for experimentation & Cam Scan, Nature Photo Editor, Blind traveler app, Karvan Card \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tools and subjected apps for experiment.  
Figure 3: Mobile ad energy consumption.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Performance Metrics  
The considered performance metrics are as defined below.  
1. Latency: Measures the time (in seconds) it takes to send a message from source to sink. This term is a function of the number of hops, the packet transverse length of transmission queues, random MAC delays and routing delays (which are based on the strategy a protocol uses to avoid collision).
2. Throughput: Measures the time performance for the entire network. This term is defined as the number of messages the sink receives per second (Kbits/s).
3. Success Rate: Measures the overall success of the network as a percentage (%). This term is computed as the total number of packets received at all of the destinations versus the total number of packets sent from all of the sources.
4. Loss Rate: The number of lost packets against the total expected number of packets for the sink, measured as a percentage (%). This term measures the quality of the protocol.
5. Energy Consumption: The sum of used energy (in Joules) of all of the nodes in the network for transmitting, receiving, spectrum sensing and idling.  
Some of the simulation parameters presented in Table 1 require some explanation. Flood Temp is a temperature variable used to control the flood. The higher the value of T, the higher is the chance that a packet is broadcasted. The MCBR learning rate is described in Equation (1). Resend represents the number of times the node resends a packet in the instance of loss. ForwardDelta sets the forwarding status of all nodes. MaxDelay is the maximum delay granted to a packet before it is declared as a timeout (simulation equivalent of 1 s, _i.e._, 40,000 = 1 s, this conversion is the same for all scenarios).  
\begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline Distance (X,Y) & (1,1) \\ Number of nodes & 50 \\ Application source, center type, radius, rate, random rate & Static, random, 1, 4, 0 \\ Application destination type, center type, radius, rate, & Static, random, 1, 0.5, 0 \\ random rate & \\ Packets generated & Infinity \\ Data traffic & Constant bit rate (CBR) \\ Data rate & 250 kbps \\ Initial node energy & 30 Joules \\ Simulation time & 100 s \\ Routing protocol & RTLD, AODV, MCBR, SC, FF, FP \\ Window size, C1, Z & 10, 0.7, 1 \\ MCBR learning rate, Resend, ForwardDelta, MaxDelay, 1, 1, infinity, 4,000, 5 \\ Flood Temp & 120,000, 2, 0.3, 1.2 \\ Transmission Timeout, Retries, MaxHops & 3500, 8, Infinity \\ AODV RQcache, Rtable, RREP Retries, RREP delay, & 10, 10, 3, 60,000, 400,000 \\ RREQ Timeout & \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
AntStart is the time that elapses before the search ant is sent. Ratio is the ratio of control packets to data sent. RewardScale is a simulation parameter used to scale the reward function. DataGain is an initialization parameter used to calculate the total number of packets sent. Window Size is the memory reserved for queue management. The C1 and Z initialization simulation values are used to compute the probability of initial search ants being broadcasted.
## 3 PNCC Method Based on Wavelet Packet Transform
### Wavelet Packet Basis Function Selection  
Common wavelet basis functions include the Haar wavelet system, Daubechies (dbN) wavelet system, bioorthogonal (biorNr.Nd) wavelet system, Symlets (symN) wavelet system and dimey wavelet system. Generally speaking, the Haar wavelet system is often used in theoretical research, Daubechies wavelet system is often used for signal decomposition and reconstruction, as a filter, and the biorthogonal wavelet system is commonly used in the field of image processing. In order to determine the most suitable wavelet system for the audio data of dry-boring pests, a set of experiments was carried out. In this experiment, we used different wavelet systems to decompose the experimental signals and drew the frequency spectrum before and after the decomposition, as shown in Figure 6.  
Figure 5: Three-level WPT tree structure.  
Through observation, it is found that the third and fourth components of db8, sym11, and bio3.9 wavelets have frequency components centered at 2400 Hz. However, the amplitude of the original audio frequency spectrum is about 0 when it is greater than 2300 Hz, which indicates that there are false frequency components. In the 500-1000 Hz frequency band, the bior3.9 wavelet system is more chaotic than other wavelet systems. In fact, we tried multiple sets of clear trunk boring pest audio signals, and the results showed similar properties. Therefore, we believe that, in this study, the dmey wavelet system performs best, followed by the sym11 and db8 wavelet systems, and finally the bior3.9 wavelet system. Therefore, the dmey wavelet system is selected as the wavelet packet basis function.
# Provision of Energy- and Wavelength-Efficient Traffic
## 3 Traffic Grooming Algorithm Design
### Property Analysis  
The TPTG_MA is composed of the TAASA algorithm and the SG algorithm. In this subsection, the properties of the proposed TPTG_MA are discussed, including computational complexity, convergence, and stability.  
(1) Computational complexity: For the TAASA algorithm, it is assumed that \(N\) connection requests are aggregated into \(M\) sub-wavelength units. In the phase of proposing and rejecting, each request will choose a sub-wavelength to match with. The computational complexity can be calculated as \(\mathcal{O}(MN)\). Let \(\omega\) be the number of iterations in each of which every connection request needs to visit \(M-1\) sub-wavelength unit due to the compare-and-swap operations. Consequently, the total computational complexity of the TAASA algorithm can be expressed as \(\mathcal{O}(MN+\omega M(N-1))\). For the SG algorithm, there are \(NM\Phi\) combinations of matching pairs. In the initialization phase, we need to construct the sub-wavelength unit utilizing the TAASA algorithm. Assuming that the outcome of the SG algorithm remains unchanged after \(\kappa\) iterations, the computational complexity of the SG algorithm can be calculated as \(\mathcal{O}(\kappa NM\Phi(M\Phi-1))\). Therefore, the computational complexity of the proposed TPTG_MA can be expressed as  
\[\begin{array}{l}\mathcal{O}(MN+\omega M(N-1))+\mathcal{O}(\kappa NM\Phi(M \Phi-1))\\ =\mathcal{O}((\omega+1)MN+MN(\kappa M\Phi^{2}-\kappa\Phi))\\ =\mathcal{O}(MN(\kappa M\Phi^{2}+\omega-\kappa\Phi+1))\end{array} \tag{19}\]  
(2) Convergence and stability: In the TAASA algorithm, we allocate all connection requests to groups according to the path affiliation, which implies that the number of sub-wavelengths available for a request is limited. During the compare-and-swap phase, the moving of a single connection request from one group to another will influence the total energy consumption. When the minimum energy consumption is achieved, the connection request will not move anymore, and the matching structure remains stable. Therefore, the convergence and stability of the TAASA algorithm can be guaranteed. In the SG algorithm, the constraints, i.e., (i) \(\Re(\mathcal{W}_{\varphi})\subseteq\mathcal{U}^{*}\cup\varnothing\) and \(|\Re(\mathcal{W}_{\varphi})|\leq\mathcal{I}\); (ii) \(\Re(\mathcal{U}^{*}_{i})\in\mathcal{W}\) and\(\left|\Re(\mathcal{U}_{i}^{*})\right|\leq 1\) ; (iii) the \(\Re_{(\mathcal{W}_{\varphi}(\mathcal{S}_{n}\mathcal{U}_{i}^{*}))}^{(\mathcal{W}_ {\varphi}(\mathcal{S}_{n}\mathcal{U}^{*}))}\) is feasible, and are not violated after the swap operation. Hence, a stable matching will be obtained after several iterations.
## 2 Materials and Methods
### Statistical Methods and Trends Fitting  
To extract useful trends from the dataset created, the least squares methodology is implemented in the current work. Ordinary least squares (OLS) is a well-established statistical technique for finding the best-fitting line or curve to represent a set of data points. By calculating the sum of squared errors between predicted and actual values, OLS [36; 37; 38] minimizes this value to identify the best-fitting line or curve (Equations (2)-(6)). This widely used method is presented in several textbooks [31; 32; 33] and is helpful for better understanding the relationships between variables and making more accurate predictions. OLS has been applied in similar research papers regarding UAV databases and sizing correlations [24].  
\[Linear:y=Ax+B \tag{2}\]  
\[Polynomial:y=Ax^{2}+Bx+C \tag{3}\]  
\[Power:y=Ax^{B} \tag{4}\]Two approaches are used with the dataset, depending on whether the variable to be fitted shows great dispersion or not. In case this statement is true (e.g., wingspan versus gross takeoff weight (GTOW)), a logarithmic transformation is conducted to the data for the fitting process to be smoother. If not, then the fitting process continues uninterrupted. To determine the most suitable correlation between UAV parameters, the aforementioned OLS method is used, with the parameters either logarithmically transformed or not, onto the 4 determined curve types. In the case of the transformed data, the best-fitted curve has to be transformed back to the physical plane and thus changes its form (e.g., a linear curve in the logarithmic plane, should be a power curve in the physical). Assisting the fitting process, the Python(r) machine learning library Scikit-Learn [39] was implemented. As criteria to determine the most suitable curve, the R\({}^{2}\) term is utilized, by splitting the available data into fitting and testing ones with a 75-25% distribution, using the fitting ones for the fit process and then checking the curve fitted with the testing ones.  
Box plots, bar charts and pie charts are utilized as additional tools to further explore the nature of the dataset parameters. Bar charts and pie charts are created to identify the market trends or gaps through the years for qualitative parameters, such as low observability and satellite communication system (SatComm). Moreover, box plots are used as a statistical means to show the dispersion of the data [36], which is also a very important metric to evaluate the fitting in addition to the R\({}^{2}\). The main attributes of a box plot are the box itself, the median line, the whiskers and the possible outliers.
## 2 System Model and Previous Works
Particularly, the authors of [14] had already proved that any differential encoding techniques can be transform equivalently to a differential encoding via a look-up table. Using the algorithm in [14; 15] to create look-up table for encoding and decoding 16-DQAM signal, a brief explanation of a look-up table is as follows. The readers should refer to [14; 15] for the details of the algorithm. * Sort all possible codeword pair \(\mathbf{c}_{x}\) and \(\mathbf{c}_{y}\) in descending order based on their proposed distance. * Arrange all groups: Take codeword pair one by one from the sorted list \(L\). Put two codewords of a pair into the same group if possible; Otherwise, put them into two different groups and the two groups are written into \(L\), which is called the list of near group pairs (near groups means the distance between members in two groups are small, which means there is a big chance that these two groups may be wrongly estimated as each other). Two codewords cannot be in the same group if their first symbols are identical. * Assign modulated symbol to each group \(G_{1},G_{2}\ldots,G16\) based on list \(L\): Take group pairs one by one in order from list \(L\). The number of different bits assigned for the considered two groups taken from \(L\) should be as small as possible (since two near groups have big change to be wrongly estimated as each other, this step's goal is to minimize the bit error). Some properties of the look-up table are listed up below. * The number of rows in the table are numbers of groups of codewords. * Codewords belong to the same group will encode for the same information. * Number of codewords in each group are the same. * The important metric to generalize the look-up table is the non-coherent distance between any codewords, which is one of the new propose in this paper. * General rules of generating the look-up table is that two codewords with small non-coherent distance should be placed in the same group. Otherwise, they should be putted into different groups. Moreover, this look-up table can be optimized by using algorithms proposed in [14; 15]. In this paper, we propose new non-coherent detector, new non-coherent distance and apply the algorithm in [14; 15] to generate the look-up table. We compare the performance of the proposed detector and distance to the existing detectors and distances proposed in [12; 13]. Due to the limitation of length, we would like to skip the detail of the look-up table algorithm and refer interested readers to [14; 15]. **3.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 5 Experimental Results
### Real-World Outdoor Experiments  
Five common real scenes (including turf, roof parking lot, and cottage area) were selected, as shown in Figure 11. The UAV was set up to fly into the experimental area at a height of 30 m, and the point cloud and image were collected and processed. At the beginning of the experiment, multiple locations were calculated and identified via a scoring selection of the point cloud data by the algorithm. In addition, the UAV was tested  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **GPS** & **Optical Flow [16]** & **Hybrid Optical Flow [17]** & **Ours** \\ \hline \(E_{tra}\) (m) & 1.0112 & 2.0538 & 0.5193 & **0.3671** \\ \(\sigma_{tra}^{2}\) (m\({}^{2}\)) & 0.5083 & 1.5819 & 0.1828 & **0.1568** \\ \hline \hline \end{tabular} The bold values indicate the best performance among the compared methods.  
\end{table}
Table  
regarding the regional identification of multiple landing sites via autonomous location. The algorithm first evaluated whether the landing area where the target landing site was located met the landing conditions. The suitable accurate landing site was calculated and compared with the physical environment information to assess the rationality of the selection.
## 2 System Model and Transmission Process
### Transmission Process
#### 2.2.1 Direct Transmission Stage of the First Time Slot
Note that U\({}_{1}\) sends the signaling to relays instead of BS [30; 31; 32]. Then, we can obtain the signaling sent by [PERSON]\({}_{1}\) at the end of the first slot. Let \(\Theta\) (\(\Theta\in\{11,10,01,00\}\)) denote the signaling sent by [PERSON]\({}_{1}\). As shown in Table 1, \(\Theta\) = '11' indicates that [PERSON]\({}_{1}\) successfully decodes \(x_{2}\) and \(x_{1}\) and does not need the help of relays; \(\Theta\) = '10' means [PERSON]\({}_{1}\) correctly decodes \(x_{2}\) but cannot detect \(x_{1}\), so it needs the help of relays; \(\Theta\) = '01' indicates that [PERSON]\({}_{1}\) fails to decode \(x_{2}\) but successfully detects \(x_{1}\), so if U\({}_{1}\) can receive \(x_{2}\) in the next time slot, it can successfully extract \(x_{1}\) from the superimposed signal obtained in the first slot; \(\Theta\) = '00' is that U\({}_{1}\) fails to decode \(x_{2}\) and \(x_{1}\) even if \(x_{2}\) is obtained in the next slot. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\(\Theta\)} & \multicolumn{2}{c}{**The First Time Slot**} & \multicolumn{2}{c}{**The Second Time Slot**} \\ \cline{2-4}  & **Descriptions** & **Candidate Relay Sets** & \(x_{R_{n}}^{\Theta}\) & **Decoding Order** \\ \hline
11 & \(R_{SU_{1}}^{x_{2}}\geq R_{2},R_{SU_{1}}^{x_{1}}\geq R_{1}\) & \(S_{1}=\left\{n\big{|}n\in N,R_{SR_{n}}^{x_{2}}\geq R_{2}\right\}\) and U\({}_{1}\) & \(x_{2}\) & \(x_{2}\to x_{1}\) \\
10 & \(R_{SU_{1}}^{x_{2}}\geq R_{2},R_{SU_{1}}^{x_{1}}<R_{1}\) & \(S_{2}=\left\{n\big{|}n\in N,R_{SR_{n}}^{x_{2}}\geq R_{2},R_{SR_{n}}^{x_{1}}\geq R _{1}\right\}\) & \(\sqrt{\theta_{n1}^{10}}x_{1}+\sqrt{\beta_{n2}^{10}}x_{2}\) & \(x_{1}\to x_{2}\) \\
01 & \(R_{SU_{1}}^{x_{2}}<R_{2},R_{SU_{1}}^{x_{1}}\geq R_{1}\) & \(S_{1}=\left\{n\big{|}n\in N,R_{SR_{n}}^{x_{2}}\geq R_{2}\right\}\) & \(x_{2}\to x_{1}\) \\
00 & \(R_{SU_{1}}^{x_{2}}<R_{2},R_{SU_{1}}^{x_{1}}<R_{1}\) & \(S_{2}=\left\{n\big{|}n\big{|}n\in N,R_{SR_{n}}^{x_{2}}\geq R_{2},R_{SR_{n}}^{x_{1}} \geq R_{1}\right\}\) & \(\sqrt{\theta_{n1}^{00}}x_{1}+\sqrt{\beta_{n2}^{00}}x_{2}\) & \(x_{1}\to x_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The signaling sent by U\({}_{1}\).