esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
## 3 Configuration and Programming
When the process of collecting the data from the stations and the trajectory of the UAV has finished, it is necessary to upload the information to a database in order to be able to visualize it in a web application. For this purpose, a connection is made to a database hosted on Google's Firebase service. This program is based on generating a connection with the Firebase database and uploading the data collected by each of the stations, which is performed when the data collection system has an Internet connection. Algorithm 5 describes the logic used to satisfy the abovementioned requirements. ```
1:Define Libraries
2:Define Variables
3:Define Transceiver configuration
4:for Each of the buoys do
5:while Not connectedToBuoy do
6: Send HELLO message
7: Receive Buffer data in rcv
8:if rcv with valid format then
9: Send Confirmation message
10:endif
11:endwhile
12:whileInvalid Package Name do
13: Receive Buffer data in rcv
14:if rcv with valid Package Name format then
15: Send Confirmation message
16:endif
17:endwhile
18:while Receive Package Data do
19: Identify Transmitting Buoy ID
20: Generate Data Package to store information
21:while receive data do
22: Receive Buffer data in rcv
23:if rcv with valid Data Record format then
24: Send Confirmation message
25:endif
26:if End of package in rcv then
27: Send Confirmation message
28: End up Data reception cycle
29:endif
30:endif
31: Close data package file
32:to register data package in.log file
33:endwhile
34:endfor
```  
**Algorithm 4** Receiver  
```
1:Define Libraries
2:Define Variables
3:Define Database credential configuration
4:Establish Database connection
5:while True do
6: Define buoy id
7: Define.log file directory
8: Get Log of packages received
9: Get Package position with READY status
10:if Status == \"READY\" then
11: Define Data package directory
12: Open Data file
13: Read File data
14: Define Variables to classify information
15: Get File data
16: Upload Data to database
17:endif
18: Define package status to \"DONE\"
19:endwhile
```  
**Algorithm 5** Uploader  
Finally, the algorithms described above were developed in.Py codes; the location of the files is indicated in Table 1.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 3 Performance of HWLNets
### Energy Efficiency  
With the increase of energy prices, this metric has become one of the key factions in determining the profitability of investments in HWLNets. The consumption of energy rises with the number of APs, so there is a trade-off between energy and spectral efficiency. One of the key factors that can impact energy efficiency in these systems is the choice of modulation technique. Orthogonal frequency division multiplexing (OFDM) is a popular modulation technique used in both RF and VLC systems, and its energy efficiency can be improved by the use of specific OFDM variants.  
One OFDM variant that can improve energy efficiency in hybrid LiFi-WiFi networks is asymmetrically clipped optical OFDM (ACO-OFDM). ACO-OFDM [33] uses asymmetric clipping to reduce the peak-to-average power ratio of the signal, which reduces the amount of energy required to transmit the signal. This technique can be particularly useful in VLC systems, where high peak-to-average power ratios can lead to significant energy losses.  
Another OFDM variant that can improve energy efficiency in hybrid LiFi-WiFi networks is DC-biased optical OFDM (DCO-OFDM) [33]. DCO-OFDM uses differential carrier-offset modulation to achieve high spectral efficiency, allowing multiple data streams to be transmitted simultaneously over a single optical carrier frequency. This increases the amount of data that can be transmitted per unit of bandwidth, which can reduce the overall energy consumption required to transmit the data.  
OFDM-based systems such as ACO-OFDM have more energy efficiency than for example DCO-OFDM for low spectral efficiency (2 bit/s/Hz) [32].  
In addition to these specific OFDM variants, there are other techniques that can be used to improve energy efficiency in hybrid LiFi-WiFi networks. One approach is to use adaptive modulation and coding, which adjusts the modulation scheme and coding rate in real-time based on channel conditions. This can help to ensure that the system is using the most energy-efficient modulation technique for the current channel conditions, reducing energy consumption.  
Another approach is to use power allocation techniques, which adjust the power level of each subcarrier in the OFDM signal based on the channel conditions. This can help to ensure that the system is using the minimum amount of energy required to transmit the signal while still maintaining the required quality of service.  
HWLNet system can optimize energy efficiency by granting bandwidth and power allocation to certain parts of the system [34] where more power is needed, thus restricting power to other parts and having efficiency improve by about 75% in contrast to the traditional WiFi system while minimizing energy consumption and reducing environmental impact.
## 5 Results
### Interpretability Analysis
#### 5.4.2 Representation Validation on UCI HAR  
An analysis on the UCI HAR dataset further validated the semantic integrity of the disentangled components.  
As shown in Figure 8, each latent space captures distinct aspects of the data. In the Static Latent Space, dynamic activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS) are largely separated from static activities (SITTING, STANDING, LAYING), though some overlap exists. The Trend Latent Space shows clearer separation between activity groups, suggesting it effectively captures the overall motion patterns. The Event Latent Space also demonstrates good clustering, particularly in separating LAYING from all other activities. This visualization confirms that the model learns representations that reflect the inherent structure of the activities.  
To provide quantitative evidence of the learned features' utility, we analyzed their importance for the downstream classification task. Figure 9 illustrates the feature importance ranking from the Random Forest classifier. The results show that both 'Event' and 'Trend' features are highly influential. This indicates that the classifier leverages both the gradual, overall patterns (Trend) and the sparse, specific moments (Event) to distinguish between activities. This analysis confirms that the disentanglement is not merely a structural exercise; the separated components are semantically meaningful and directly contribute to the model's predictive power.  
Figure 8: t-SNE visualization of the Static, Trend, and Event latent spaces on the UCI HAR test set. CoTD-VAE demonstrates a clear ability to cluster activities based on their dynamic or static nature, supporting the interpretability of the disentangled representations.  
Figure 9: Feature importance ranking of the disentangled latent variables for the classification task on the UCI HAR dataset. The plot shows the top 20 features as determined by the Random Forest classifier. Both event and trend features are shown to be highly predictive.
## 3 Materials and Methods
### Encoder
#### 3.1.3 Decoder  
In the decoder section, we have decided to employ a decoder focused on graph structure reconstruction. This decision is based on two core considerations. Firstly, by optimizing the error loss before and after graph structure reconstruction, we can facilitate encoder self-feedback, enhancing the model's adaptability. Secondly, and more importantly, we aim to build a joint loss function by integrating the graph structure reconstruction loss and the spectral clustering loss, thus enhancing the overall performance of the model.  
Considering the latent embeddings have effectively integrated the key information of node content and graph structure, we selected a decoder based on the inner product to predict the connection probabilities between nodes. This decoder was chosen for its efficiency and flexibility:  
\[\hat{A}_{ij}=sigmoid\left(z_{i}^{T}z_{j}\right) \tag{9}\]  
where \(\hat{A}_{ij}\) is a reconstructed adjacency matrix, whose elements predict the probability of links existing between nodes. To minimize reconstruction error, we utilize the mean squared error formula to calculate the difference between the actual adjacency matrix \(A_{ij}\) and \(\hat{A}_{ij}\) to minimize the reconstruction error.  
\[L_{recon}=\frac{1}{n^{2}}{\sum_{i=1}^{n}\sum_{j=1}^{n}\left(A_{ij},\ \hat{A}_{ij} \right)^{2}} \tag{10}\]  
This loss function ensures that the model, during its learning process, is capable not only of reconstructing the graph's structure but also of accurately capturing and simulating the complex interrelations among nodes within the graph.
# Investigating a Toolchain from Trajectory Recording to Resimulation
## 2 Material and Methods
### Traffic Scenarios  
In total, we defined three different scenarios, which can be divided into two categories based on the road users involved, i.e., (i) cars to cars:  
1. Two cars drive one behind the other. The car in front brakes until its velocity is zero (\(v=0\) km/h, m/s). The car behind reacts to this maneuver.  
Figure 1: Flowchart of the toolchain presented in this paper.  
2. One car drives straight, while another car turns left and crosses the path of the first one, a so-called left-turn-across-path (LTAP) maneuver. and (ii) cars to pedestrians: 3. Pedestrians are crossing the street in front of a car, which responds by braking to ensure their safety. The three scenarios are presented visually in Figure 2. Car drivers were following a screenplay, whereas pedestrians were crossing the street randomly following traffic rules. Each scenario has been recorded once and resimulated also once.
## 9 Conclusions and Future Work  
The system presented in this article offers many opportunities for development and is adapted to further research in terms of its use by people with disabilities. The verification of the EMG signal made it possible to check how the algorithm would work in the case of mental commands without additional muscle stimulation, as would be the case with paralyzed people. This is very important as such participants cannot move their limbs.  
It should also be noted that the level research work with the use of the Emotiv EPOC Flex Gel (EEG) headset in this type of project is very small. It is a simple device that provides satisfactory accuracy. In addition to this, regarding the use of MyoWare Muscle Sensor (EMG), it turns out that their combination is an original solution, which further confirms the uniqueness of the project. Further research may provide a better understanding of other uses for one or both of the devices. Therefore, it is possible to emphasize the originality of the research carried out in the context of the lack of cooperation between the Emotiv and MyoWare devices. The literature review confirms this state of affairs.  
Summarizing the results obtained in the experiment, it is possible to conclude that the tasks set for the system classifying integrated EEG signals and verifying muscle movements have been fulfilled. An undoubted advantage is the possibility of further research leading to the development of the system, especially in terms of deep learning. It would be good to test the system on people with disabilities. This would probably make it possible to better adapt it to their needs in such a way that in the future it could serve them and improve their functioning. The further development of artificial intelligence will certainly make it possible to classify biomedical signals even more effectively. The authors hope that the proposed approaches to the problem of classification and verification of mental commands will be developed, improved, and resistant to various disturbances occurring in the signal waveforms.  
Conceptualization, D.P. and S.P.; methodology, D.P.; software, D.P.; validation, S.P. and D.P.; formal analysis, S.P.; investigation, D.P.; resources, D.P. and S.P.; data curation, D.P.; writing--original draft preparation, D.P.; writing--review and editing, S.P.; visualization, D.P.; supervision, D.P. and S.P.; project administration, S.P. and D.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Center, Grant number: 2021/05/X/ST7/34, MINIATURA 5.  
The authors declare no conflict of interest.
## 4 Evaluation
### Query Processing Time  
We randomly selected 1000 TRUE (reachable) and FALSE (non-reachable) node pairs from each dataset. The query processing time was measured to be the total time required to determine the reachability of 1000 node pairs.  
For TRUE pairs (Figure 4), CRT showed the worst performance, as it requires prime factorization and a comparison of all remainders. The other approaches showed a similar performance. However, for the larger datasets, such as SwissProt and psd7003, REP+ showed the best performance. The reason is that, as already shown in the previous section, the space requirements of REP+ are the smallest, which renders the node labels smaller than those of other labeling schemes.  
For FALSE pairs (Figure 5), REP showed the worst performance. The prime factorization and prefix matching of prime number sequences required by REP reduces its speed. Note that there is a  
Figure 4: Query processing time for TRUE pairs (X-axis indicates datasets in Table 1).  
Figure 3: Space requirements (X-axis indicates datasets in Table 1).  
big performance difference between REP and REP+, which employ the same labeling scheme. Consider line 2 in Algorithm 2. In REP+, if a label is not divisible by the other label, this signifies a FALSE pair. No additional operations are required for FALSE pairs, which allows REP+ to be much faster than REP. That is, REP+ immediately terminates when the remainder is zero. It makes REP+ to be more efficient than REP in terms of CPU time. Meanwhile, CRT is faster than REP because CRT stores parent labels and self labels separately, whereas REP stores only one composite number. Thus, CRT often has smaller number labels than REP, which reduces query processing time.  
Considering both TRUE and FALSE pairs, PRM is the only method that is comparable to REP+. PRM requires a single division operation for both TRUE and FALSE pairs, and is the fastest of the prime number labeling approaches thus far. Nevertheless, it was slower than REP+ for the larger datasets. This is because REP+ has smaller labels than PRM, which can be seen in its space requirements. This leads us to regard REP+ as a scalable approach. Moreover, since REP+ also outperforms PRM in terms of labeling time and space requirements, REP+ should be considered the most efficient approach overall.
# Dynamic Response Analysis of Asphalt Pavement under Pavement-Unevenness Excitation
## 3 Solution of Multilayer Pavement under Pavement-Unevenness Excitation
### Boundary and Interlayer Bonding Conditions  
When a circular harmonic load is applied on the surface of a multilayer elastic body (\(z=z_{0}=0\)), the amplitude of which is \(P_{l}\), the boundary conditions at the surface \(z=0\) can be expressed as follows:  
\[\begin{array}{l}\sigma_{zz}|_{z=0}=\left\{\begin{array}{l}-q\ r\leq R\\ 0\ r>R\end{array}\right.\\ \sigma_{rz}|_{z=0}=\sigma_{\theta z}|_{z=0}=0\ r\geq 0\end{array} \tag{29}\]  
where \(R\) is the radius of the circular load and \(q=P_{l}/\pi R^{2}\).  
When the layer is not completely continuous at the interface \(z=z_{j}\), the interlayer interface conditions are simplified by using the Goldman model, and the transfer relation expression at \(z=z_{j}\) is as follows:  
\[\begin{array}{l}\sigma_{rz}(r,\theta,z_{j-})=\sigma_{rz}(r,\theta,z_{j+})\\ \sigma_{\theta z}(r,\theta,z_{j-})=\sigma_{\theta z}(r,\theta,z_{j+})\\ \sigma_{zz}(r,\theta,z_{j-})=\sigma_{zz}(r,\theta,z_{j+})\\ u_{z}(r,\theta,z_{j-})-u_{z}(r,\theta,z_{j+})=(1/k_{r})\sigma_{rz}(r,\theta,z _{j-})\\ u_{\theta}(r,\theta,z_{j-})-u_{\theta}(r,\theta,z_{j+})=(1/k_{r})\sigma_{ \theta z}(r,\theta,z_{j-})\\ u_{z}(r,\theta,z_{j-})-u_{z}(r,\theta,z_{j+})=(1/k_{z})\sigma_{zz}(r,\theta,z _{j-})\end{array} \tag{30}\]  
In the above formula, \(k_{z}\) and \(k_{r}\) are the interface association coefficients in the vertical and horizontal directions, respectively. When the interface association coefficients in the vertical and horizontal directions are zero, it means that the interface is completely separated from each other; when they approach infinity, it means that the interface is completely continuous.  
In the practical application of the sliding interface model, only the horizontal interface bonding coefficient is considered; that is, the vertical interface bonding coefficient \(k_{z}\) is infinite, and the interlayer contact is characterized by changing the horizontal interface bonding coefficient \(\mathrm{k_{r}}\).
# Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments
## 3 The Proposed Dynamic Task Scheduling Scheme
### Dynamic Task Scheduling  
Based on the load information collected previously, the state of a worker node and the task information are used to determine the load type of the worker node. Figure 3 shows the decision tree of task scheduling. \(n\) indicates a worker node where a load is determined to be present since the CPU and memory usage exceed the threshold value (\(\theta\)). Task scheduling is performed based on the load type by checking the state of each worker node. First, the load table of Nimbus is examined, and the lowest memory usage of the worker nodes is determined. When the lowest value exceeds the threshold, the memory usage of all worker nodes is higher than the threshold, which is subsequently determined as the memory load, and memory load-based scheduling is performed accordingly. Alternatively, it is examined whether the CPU and memory usage of a particular worker node exceed the threshold. If a load occurs on at least one worker node, it is determined as the CPU and memory load, and CPU and memory load-based scheduling is performed. Finally, if the CPU usage of a particular worker node exceeds the threshold, it is determined as the CPU load, and CPU load-based scheduling is performed. When all three load types are not satisfied, R-Storm-based static scheduling is performed.
# Experimental Research into the Ultrasonic P-Wave Velocity of Coal Slime Based Backfill Material
## 4 Correlation between Strength and P-Wave Velocity  
### Correlation between Compressive Strength and P-Wave Velocity  
Variations in the ultrasonic p-wave velocity in the filling materials are correlated with the strength of the filling materials. Generally, the strength of the filling materials is related to their density and texture. The ultrasonic propagation speed in different materials is related to the material density, elastic modulus, and internal defects.  
Therefore, variations in the ultrasonic p-wave velocity and strength of coal slime based backfill materials were subjected to correlation analysis. In correlation analysis, the correlation coefficient is commonly used to measure how closely variables are correlated. The most common correlation coefficient is the Pearson correlation coefficient, which measures the linear correlation between two variables. The Pearson correlation coefficient lies within the range from \(-1\) to \(1\). A Pearson correlation coefficient approaching \(1\) or \(-1\) indicates positive or negative correlations, while it means that there is no correlation if the coefficient approximates to \(0\).  
As shown in Figure 14, the ultrasonic p-wave velocity varies in a manner similar to the strength of the filling materials. In the first several groups of tests, the ultrasonic p-wave velocity increases with the strength. From the third to the sixth group, the strength and ultrasonic p-wave velocity begin to decline simultaneously, and they decrease at the same time after small increments in the seventh group of tests. After correlation analysis on the ultrasonic p-wave velocity and compressive strength, a correlation heat map was drawn (Figure 15). In the figure, the color of ellipses indicates the symbol of correlation coefficients: red indicates that two factors are positively correlated, and the more oblate the ellipse is, the higher the correlation. The correlation coefficient between the ultrasonic p-wave velocity and compressive strength is \(0.87\), which means that they have a strong positive correlation. These results suggest that in these tests, specimens with a high (low) ultrasonic p-wave velocity generally show a high (low) compressive strength.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.2 Simulated Annealing Based  
Simulated annealing was proposed by Metropolis in 1953 [17]. In 1983, simulated annealing was introduced into combinatorial optimization [25]. Simulated annealing is a kind of stochastic searching optimization algorithm based on a Monte-Carlo iterative solving strategy. The idea of simulated annealing is inspired by the similarity between the annealing process and combinatorial optimization. Simulated annealing starts with a high initial temperature; with the decrease of the temperature, the simulated annealing algorithm uses probabilistic jumping to find the optimal solution in solution space randomly, _i.e._, the algorithm can jump between the locally optimal solutions probabilistically and finally get a globally optimal solution. The simulated annealing algorithm is a universal optimization algorithm which can theoretically achieve global optimization performance. The simulated annealing algorithm has been widely used in control engineering, machine learning, neural network, signal processing, _etc._  
Many researchers have used simulated annealing algorithms to improve the performance of LEACH, such as [79; 80; 81; 82]. The algorithm shown in [79] uses a simulated annealing algorithm to form clusters. In low-energy adaptive clustering-centralized (LEACH-C) [79], the NP-hard problem of finding \(k\) optimal clusters has been solved. Based on LEACH-C, in [80], the authors propose an energy efficiency optimized LEACH-C algorithm for WSNs. To improve the energy performance, energy efficiency optimized LEACH-C takes packet retransmission and acknowledgement into consideration to create a cluster head energy consumption model. The algorithm can calculate the quadratic sum of the distances from each cluster head to its member nodes in the optimal solution. Based on LEACH, in [82], the authors use the member nodes' information in steady phase to choose the vice cluster heads which will take over the role of cluster heads in the later steady phase period to avoid the early death of cluster heads. The work in [81] addresses the energy efficient sensor deployment problem. In this algorithm, a simulated annealing algorithm will be used to calculate the global optimization result for the whole network. In [83], the authors design a simulated annealing-based algorithm for large scale wireless sensor networks. In this algorithm, the Wiener Index is used as the cost of the solution. The algorithm randomly selects a neighborhood as the current solution and calculates the Wiener Index. If the Wiener Index is greater than or equal to the current solution, then the current solution will be replaced by the neighborhood. The algorithm has better energy efficiency and network latency performance.