esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment
## 4 U-SPB Procedure
### Data Analysis  
The analysis procedure starts from the raw data obtained in the measurement phase by matching traffic counter flows with noise data (sensor acquisition phase). Measurement periods are filtered according to the national law requirements for weather conditions.  
The feature detection phase starts with downloading traffic counter data into a spreadsheet. Then, a time interval between consecutive passages has to be fixed to identify valid transits. The choice of the time interval depends on the source distance and source average speed. For the purpose of this study, a time interval between 3.5 and 4 s is applied since all the roads have 50 km/h as a speed limit. The candidate transits are highlighted by conditional formatting on the spreadsheet. Analogous conditional formatting is performed over vehicle lengths returned by the traffic counter to discriminate between vehicle categories. Then, single candidates are identified in the time history of sound pressure level, and events are selected if L\({}_{\text{Affmax}}\) exceeds the background noise by at least 10 dB(A); otherwise, the event is discarded.  
For valid events, the noise average spectrum is acquired in the L\({}_{\text{Affmax}}\)-10 dB(A) time interval. The analysis spreadsheet is designed to be able to count valid events per direction and categories so that it is possible to stop analysis as soon as the minimum requirements are fulfilled. A diagram of the event selection procedure is reported in Figure 3.  
The event extraction phase starts by collecting the average spectrum together with speed and direction and storing them in order to collect all the events. The average spectrum is then fitted according to Equation (1).  
Thus, the statistical sample of many single passages constitutes the dataset for the statistical data binning developed in SPB-L. In Figure 4, an example of statistical data binning is provided according to [PERSON] et al. (2018) and [PERSON] et al. (2018).  
The analysis with data binning provides regression of SEL values and each frequency band as a function of speed. An example is reported in Figure 4. In case of fits with fewer than 10 events, fits are carried out without statistical data binning.  
If enough events are acquired, at least 100 light and 30 heavy vehicles, the SPBI can be estimated according to Equation (3), taken from the ISO/DIS 11819-1 for low-speed roads:  
\[SPBI=\ 10\ \log\left[W_{1}10^{(\frac{L_{1}}{10})}+W_{2}\left(\frac{v_{1}}{v_{2}} \right)10^{(\frac{L_{2}}{10})}+W_{3}\left(\frac{v_{1}}{v_{3}}\right)10^{(\frac {L_{3}}{10})}\right] \tag{4}\]  
Figure 3: Features extraction phase: event selection process.  
where \(L_{i}\) is the vehicle sound levels for the \(i^{\text{th}}\) vehicle categories; \(W_{i}\) is the weighting factors determined by compositions of traffic flow and are established by the ISO.
# Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network
## 3 Results and Discussion
### Neural Network Architecture
The input image size used in this study is 200 \(\times\) 200 \(\times\) 1. Therefore, a relatively deep network is required to fully learn the data features. However, a contradiction arises as the dataset size is insufficient to support an excessively deep network structure, which could lead to overfitting and lower the model's generalization performance. To address this challenge, the study initially employed larger convolutional kernels and larger strides for feature extraction from the input layer. This approach allowed for rapid feature extraction while retaining image features. To mitigate overfitting, non-zero padding was used during pooling to discard certain features. Subsequent convolution was applied to the pooled feature maps using (7 \(\times\) 7) convolutional kernels, which are smaller than those used in the first convolutional layer, and the number of feature maps was doubled. The resulting feature maps from the second pooling layer were further flattened, and smaller convolutional kernels with smaller strides were applied to preserve more image features. Finally, another round of pooling yielded a 256 tensor. Figure 8: The principles of feature extraction: (**a**) convolutional layer, (**b**) pooling layer. Two fully connected layers with 1024 neurons each were introduced to effectively address non-linear image classification. The data were ultimately transformed into probability outputs in the output layer at the bottom of the neural network, and predictions for different categories were made using the SoftMax function. The whole 2D CNN structure is shown in Figure 9, and related parameter settings is listed in Table 2.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### Feature Extraction  
Features are extracted from the preprocessed data. Recent studies [7; 19; 37] showed that removing the information from data is more helpful for classifying stressed and relaxed conditions. In the DEAP dataset, EEG is recorded using the 32-channel headset, and four EEG bands are obtained from the EEG headset, including delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. For this purpose, EEG data from all channels (i.e., 32 channels) is collected along with all the videos labeled either stressed or relaxed. So, the dimension of our feature vector is (channels) \(*\) (features), i.e., 32 \(*\) 4 = 128.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Data Modality & EEG (Electroencephalogram), Peripheral physiological signals, Audio \\ Number of Participants & 32 \\ Age Range & 19–37 years old \\ Gender & 46.87\% Females, 53.12\%Males \\ Stimuli & 40 one-minute music video excerpts \\ Self-Assessment & Valence (1–9 scale), Arousal (1–9 scale), Dominance \\ (1–9 scale), Liking (1–9 scale), Familiarity (1–5 scale) \\ Physiological Signals EEG & 32 Channels, International 10–20 system \\ Original Sampling Frequency (EEG) & 512 Hz \\ Down Sampling Frequency & 128 Hz \\ Data Format & Preprocessed Python/NumPy files \\ \hline \hline \end{tabular}
\end{table}
Table 2: DEAP Dataset—Description and Demographics.  
Power Spectral Density (PSD) features are a common choice for analyzing EEG signals due to their ability to provide valuable insights into the different brain rhythms, such as delta, theta, alpha, beta, and gamma waves. These brain rhythms are associated with various cognitive states and mental processes, making PSD a valuable tool for studying stress and relaxation states. The statistical properties of EEG signals change over time. PSD features can be calculated over short windows to capture these dynamic changes. Deviations in PSD features have been linked to various neurological and psychiatric disorders, making them clinically relevant.
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide
### Exploiting the TiO\({}_{2}\) Properties for Hydrogen Storage  
For solid-state hydrogen storage, several materials such as MgH\({}_{2}\)[13; 54; 55; 56], NaAlH\({}_{4}\)[57], LiAlH\({}_{4}\)[58], and LiBH\({}_{4}\)[59; 60] have been considered due to their higher gravimetric and volumetric hydrogen densities [61]. However, their practical applications are limited due to their sluggish kinetic and high dehydrogenation temperature [54; 57; 58; 62]. Concerning MgH\({}_{2}\)[13; 54; 55; 56; 63; 64; 65; 66; 67; 68], NaAlH\({}_{4}\)[57; 69; 70], LiAlH\({}_{4}\)[71; 72] and LiBH\({}_{4}\)[60; 62], the use of TiO\({}_{2}\) as a catalyst has been investigated in order to improve the dehydrogenation/rehydrogenation kinetics of these materials, showing an improvement in performance by adding TiO\({}_{2}\). Moreover, the use of TiO\({}_{2}\) has also been investigated for MgH\({}_{2}\)-NaAlH\({}_{4}\) composite [61] and 4 MgH\({}_{2}\)-LiAlH\({}_{4}\) composite [73]. Both composites showed significant improvements in the dehydrogenation/rehydrogenation kinetics with the introduction of TiO\({}_{2}\). Figure 4: Schematic representation of a plausible mechanism for H\({}_{2}\) production by CuInS\({}_{2}\)/TiO\({}_{2}\)/MoS\({}_{2}\) photocatalyst under visible light. Application of TiO\({}_{2}\) to Enhance the Hydrogen Storage Performance of MgH\({}_{2}\)  
Several strategies have been adopted to improve the hydrogen storage performance of MgH\({}_{2}\) by exploiting TiO\({}_{2}\) as catalyst. A carbon-supported nanocrystalline TiO\({}_{2}\) was synthesized by [PERSON] et al. [63] as a catalyst precursor. The carbon-supported nanocrystalline TiO\({}_{2}\) (TiO\({}_{2}\)@C) exhibited good catalytic activity in the hydrogen storage reaction of MgH\({}_{2}\). It was observed that the TiO\({}_{2}\)@C-containing MgH\({}_{2}\) composite with a TiO\({}_{2}\)@C content of 10 wt% released 6.5 wt% hydrogen within 7 min at 300 \({}^{\circ}\)C. Moreover, the dehydrogenated sample took up approximately 6.6 wt% hydrogen within 10 min at 140 \({}^{\circ}\)C under 50 bar of H\({}_{2}\). [PERSON] et al. [54] explored the catalytic effect of synthesized Co/TiO\({}_{2}\) nanocomposite on the hydrogen desorption/absorption properties of MgH\({}_{2}\). It was observed that using Co/TiO\({}_{2}\) highly improved the hydrogen desorption/absorption kinetics of MgH\({}_{2}\)
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Result Representation of the Three Evaluated Classifiers Using the Confusion Matrix
#### 7.2.3 Comparative Analysis of the Two Test Scenarios for the MLP Classifier  
Figure 10 compares the multi-class classification results of two test scenarios using MLP as a classifier. Similar to the previously mentioned two classifiers, the overall accuracy of the MLP considerably declined from 91.2% to 72.9% when the test environments of the UWB were different from the training state. Repeatedly, this was caused by the false discovery rate of the MP conditions in  
Figure 9: Comparison of the multi-class classification results for the RF classifier using the confusion matrices in two scenarios: (**a**) the test dataset obtained from the same conditions as the training data; (**b**) the test dataset collected in a different condition from the training. In the evaluation, fifty decision trees were used in the forest of the applied RF classifier.  
the measured UWB data. Specifically, the predicted accuracy of MP condition in the MLP classifier decreased from 30% to 19.8% out of 100% for the three classes. This outcome showed that the MP condition was the challenging class to identify throughout our evaluation in all classifiers (Figures 8-10).  
The decrease of performance in a particular class causes an increase in the false discovery rate of other classes. Specifically, for our evaluation in MLP, the misclassification rate of MP as LOS increased from 2.2% to 7.7%, while MP as NLOS increased from 1.1% to 5.9% (Figure 10). In fact, the predicted accuracy of both LOS and NLOS also declined in the MLP classifier, i.e., from 30.9% to 26.7% in the LOS condition and from 30.2% to 26.4%.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 6 Experiments and Discussion of Results
### Memory Consumption  
The memory consumption of each database (while idle) is illustrated in Figure 11 and clearly shows the high cost of graph database technology with TigerGraph and JanusGraph using about 2 GiB and 10 GiB respectively. PostgreSQL only makes use of around 9 MiB which makes is extremely lighter than the graph databases.  
The technologies used for JanusGraph all run on the JVM and, given the 32 GB memory available, allowed the JVM's garbage collector to run infrequently. Most of the memory used by JanusGraph is the cache for ElasticSearch, Cassandra, and JanusGraph, each storing an independent cache. Most of the fluctuations in memory came from the JanusGraph container and this happens from the moment the container is run. This cache can be limited by configurations, but our results are from an unlimited cache setup.  
Memory for TigerGraph and PostgreSQL were held constant (as their lower and upper memory bounds were within MiB differences) as the majority of these two databases are written in C or C++,  
Figure 11: Database memory usage for each dataset. The medical response dataset is 1.5 Mb large.  
Figure 10: A subset of the graph produced by TigerGraph on the result of the query for the third kernel, _Ranking Las Vegas by Friends’ Sentiment_ but on the user [PERSON] from the first kernel. The topology of [PERSON]’s friend groups suggest a very cliquey circle of friends with influence not far from her own.  
which requires manual memory management. It is important to note that while JanusGraph uses a lot of memory, there are other graph database variants adhering to the Apache TinkerPop standard which claim to be more efficient in terms of memory such as Neo4j and OrientDB. Neo4j and OrientDB memory consumption is also configurable, but may still use multiple GiB of memory because of the JVM [34; 35].
# The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications +
###### Abstract  
In this paper, we propose and validate an artificial neural network-based equalizer for the constant power 4-level pulse amplitude modulation in an optical camera communications system. We introduce new terminology to measure the quality of the communications link in terms of the number of row pixels per symbol \(N_{\text{pps}}\), which allows a fair comparison considering the progress made in the development of the current image sensors in terms of the frame rates and the resolutions of each frame. Using the proposed equalizer, we experimentally demonstrate a non-flickering system using a single light-emitting diode (LED) with \(N_{\text{pps}}\) of 20 and 30 pixels/symbol for the unequalized and equalized systems, respectively. Potential transmission rates of up to 18.6 and 24.4 kbps are achieved with and without the equalization, respectively. The quality of the received signal is assessed using the eye-diagram opening and its linearity and the bit error rate performance. An acceptable bit error rate (below the forward error correction limit) and an improvement of \(\sim\)66% in the eye linearity are achieved using a single LED and a typical commercial camera with equalization.  
CP 4-PAM; optical camera communications; ANN equalizer +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Artificial Neural Network Equalizer for Constant Power +PAM in Optical Camera Communications. In Proceedings of the 2020 12 th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal, 20-22 July 2020.
## 4 Experiments and Results  
Corresponding to the architecture of the proposed teleoperation system, three experiments were designed to evaluate the effectiveness of the proposed method, namely robotic arm teleoperation, robotic gripper teleoperation, and the integrated teleoperation for bimanual tasks.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 7 Result Comparison and Performance Evaluation
### Protocol Evaluation
#### 7.2.2 Scenario 2  
Figure 10 shows that by increasing the receivers then the performance of overhead, PDF and throughput are improving in EERASCA as compared to PUMA and ERASCA. Figure 10a,b shows that the increase in PDF and throughput is due to the presence of many alternate paths between the receivers and the group members which minimizes the packet loss as compared to longer and fewer paths. Packet loss is caused due to data transmission error and congestion. Packet loss arises when one or more data packets are moving across a network fail to reach the desired destination. Hence, as the number of receiver's increases, then there will be richer connectivity between the nodes in the group and increases the PDF and throughput.  
Figure 10: _Cont._  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Protocol** & **Mean Interval** & **Confidence Interval** \\ \hline EERASCA PDF & 79.96\% & 89.10–70.82\% \\ \hline ERASCA PDF & 82.02\% & 85.05–78.99\% \\ \hline PUMA PDF & 77.39\% & 81.33–73.45\% \\ \hline EERASCA Overhead & 2.35 byte/s & 2.16–2.55 bit/s \\ \hline ERASCA Overhead & 2.47 byte/s & 2.34–2.60 bit/s \\ \hline PUMA Overhead & 2.75 byte/s & 2.65–2.85 bit/s \\ \hline EERASCA Throughput & 79.26\% & 89.71–68.82\% \\ \hline ERASCA Throughput & 79.20\% & 86.11–72.30\% \\ \hline PUMA Throughput & 77.25 & 83.10–71.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confidence Interval for PDF, Overhead and Throughput.  
In Figure 10c, an interesting situation arises in PUMA and ERASCA when the number of receivers increases. Due to the increase in the number of receivers, flooding increases, and the overhead increases. This increase in overhead increases the packet drop, link failure, congestion, delay, etc. On the other hand, in EERASCA when the number of nodes increases, the overhead decreases due to multicasting and unicasting. Hence, the richer connectivity among the receivers decreases the overhead, packet drop, delay, congestion and bandwidth, etc., showing performance improvement compared to ERASCA and PUMA by avoiding flooding. In Figure 10, with fewer receivers, multicasting and unicasting are ideal because of less congestion and successful packet transmission to the desired destination with less packet drop. EERASCA shows good improvement related to overhead, throughput, and PDF compared to PUMA and ERASCA where flooding is used. Hence, EERASCA outperform the ERASCA and PUMA in PDF and throughput as the number of receivers are increases. The confidence interval for PDF, overhead and Throughput is shown in Table 4 in the presence of receivers (10-40), which shows the mean value, highest and lowest value.
# A Comprehensive Review on Mine Tailings as a Raw Material in the Alkali Activation Process
## 2 Mineralogy of Mine Tailings
The reaction can be represented by Equation (2) as follows:  
\[\text{CaCO}_{3}+\text{H}_{2}\text{SO}_{4}\rightarrow\text{CaSO}_{4}+\text{ CO}_{2}+\text{H}_{2}\text{O} \tag{2}\]  
The above reaction represents the neutralization of sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) by calcium carbonate (CaCO\({}_{3}\)), resulting in the formation of gypsum (CaSO\({}_{4}\)), carbon dioxide (CO\({}_{2}\)), and water (H\({}_{2}\)O). Dolomite (calcium magnesium carbonate, CaMg(CO\({}_{3}\))\({}_{2}\)) is similar to calcite but contains calcium and magnesium ions, providing a buffering capacity [75]. Feldspars are aluminosilicate minerals that can react with acidic solutions. The aluminum (AI) and silicon (Si) in their structure can neutralize acidic conditions. Olivine(magnesium iron silicate, (Mg, Fe)\({}_{2}\)SiO\({}_{4}\)) is another silicate mineral that can neutralize acid; the reaction involves the release of magnesium and iron ions, which can help buffer against acid [76]. Carbonates can provide the short-term neutralization of acidic conditions by decreasing the acidity of water by raising the pH and decreasing the concentration of hydrogen ions (H\({}^{+}\)), while silicates are considered more long-lasting neutralization minerals [77; 78]. These minerals, when present in tailings or added as amendments, can help to reduce the risk of environmental damage associated with acid mine drainage [79].
## 3 Results
### Spatial and Angular Distribution
By observing Figure 4, it can be discovered that the power distribution of the electron radiation in the \(\theta\) and \(\Phi\) directions is highly uneven, which suggests that both have a rather obvious influence on the radiation power. Hence, we select the angular distribution diagram of the electron radiation power when the laser normalized amplitude \(a_{0}=5\) and draw the cross-sectional plots in the \(\theta\) and \(\Phi\) directions, respectively. Figure 4: The angular distribution diagram of the electron radiation power when the delay time is \(5\) fs. \(dP(t)/d\Omega\) represents the radiant power per unit solid angle. By observing the cross-sectional diagram of \(\Phi\) in Figure 5a, it can be observed that the electron radiation is basically concentrated within the range from \(-10^{\circ}\) to \(10^{\circ}\). Within the ranges from \(-180^{\circ}\) to \(-10^{\circ}\) and from \(10^{\circ}\) to \(180^{\circ}\), the electron radiation is extremely small and can be disregarded. This is due to the fact that the electric field vector of the linearly polarized laser vibrates along the x-axis, and the polarization plane is the XOZ plane. Consequently, the generated electron radiation is primarily concentrated in the XOZ plane. Upon observing the cross-sectional diagram of \(\theta\) in Figure 5b, it can be discerned that the electron radiation power is predominantly concentrated between \(66^{\circ}\) and \(128^{\circ}\). Within the ranges 0-\(66^{\circ}\) and \(128^{\circ}\), the electron radiation is extremely minute and can be disregarded. There exists a rather intriguing phenomenon: as \(\theta\) increases, the electron radiation power exhibits a sharp ascent followed by a gradual descent. This distribution regularity is associated with the angle of the electron's movement within the range of laser influence. The tangential direction of the electron's movement constitutes the direction of radiation. During the steep ascending phase, the electron's movement is relatively stable without substantial directional alterations, thereby enabling the accumulation of a significant amount of electron radiation within a very narrow \(\theta\) angle range. Meanwhile, in the slow descending phase, the electron radiation is unstable, and the movement direction undergoes rapid changes, resulting in a relatively sluggish accumulation of electron radiation.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 4 Results  
### Rq1  
_Is There UAV Path Planning in 3D?_  
RQ1 seeks to provide information on the objectives to be optimized, such as the minimum mission completion time so that the UAV consumes less energy and its total cost of 3D path planning is minimized. An optimal or feasible 3D UAV route must be cost-effective and efficient in time and energy; for this, the formulation of the system in path planning must also be considered, where certain factors must be known for the successful completion of the mission. The UAV must see the environment where it is going to fly, the dimensions of the area, and the number of UAVs used to complete the mission and be prepared for the obstacles that may arise during the flight. These aspects are essential in the path planning of the UAVs in 3D.  
Figure 12 shows a graph of the objectives to be optimized vs. the number of articles analyzed. If the number of papers for each objective exceeds the value of contributions is because, in the researched papers, more than one of them has considered several objectives within the same investigation. Figure 12 also shows that the researchers focus on minimizing cost (33.3%), energy (20.37%), and time (20.37%). Among 18 out of the 37 items, the priority was to minimize the cost. These included the computational cost, threat cost, software, and hardware cost, etc.  
Table 5 presents the objective functions of the 37 papers analyzed. Table 5 shows that 37.84% (14) of papers present several objective functions, such as shown in contribution A-18 in which the cost (where the minimum safety distance and maximum communication  
Figure 11: Scope of the study, based on [24].  
distance between UAVs are considered), environmental model (the topography of the land is a model), and the flight altitude restrictions are considered. In the same way, in articles A-29 and A-37, there are three objectives to be optimized. The articles A-03, A-04, A-05, A-10. A-15, A-20, A-23, A25, A-31, A-32, and A-33 have two objectives to be optimized. The rest of the items only have one objective function.