esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[15] proposed a deep learning framework on blind Morse signal detection in the wideband spectrum and achieved state-of-art performance using real-world datasets. In [16], a k-means clustering algorithm was introduced to differentiate the elements in Morse signals after parameter extraction of the time-frequency image, and the suitability of the proposed method was investigated by simulation under different signal-to-noise ratio (SNR) conditions. In addition, [PERSON] et al. [17] used this k-means clustering algorithm for dynamic threshold and Morse code recognition in a noisy environment. However, existing works have focused on Morse telegraph signals recognition in RF wireless communications, and, to the best of our knowledge, no reported work has tried to present an automatic recognition scheme and performance analysis for maritime manually sent-like Morse light signals. Besides, the ML-based k-means algorithms have not been well studied in this case as well, and there is no related real-time experimental measurement yet. To address these issues, we first propose and experimentally demonstrate an automatic recognition framework of Morse light signals in maritime optical communications by an ML-based clustering approach. Specifically, we implement a flexible FSO hardware prototype to collect real-measured Morse light signals in the physical environment for the recognition algorithm test. A modified k-means clustering is designed to compute the decision threshold for binarization and identify elements including dot-dash and space recognition in Morse light signals. To improve the decoding efficiency, we make modifications to the classical k-means algorithm, and a selection sorting-assisted method has been adopted. To tackle with the emerging glitches in the binarization waveform, resulting from strong noise channels, a novel error correction scheme is presented to enhance the robustness to ambient light noises. The proposed recognition approach is implemented in a microcontroller unit (MCU), and the performance of the prototype is analyzed in terms of recognition accuracy and system robustness via real-time decoding results derived from the MCU.
## 3 LEO-Constellation-Augmented BDS POD Strategies
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Description** & **A Priori Constraints** \\ \hline \multirow{4}{*}{Observations} & \multirow{4}{*}{\
\begin{tabular}{c} Undifferenced dual-frequency \\ IF LC and PC \\ \end{tabular} } & Ground pseudo-range observations: 1 m; \\  & &
\begin{tabular}{c} Carrier-phase: 0.02 cycles; spaceborne \\ observations: calibrated with \\ real-calculated observations \\ \end{tabular} \\ \hline \multirow{2}{*}{Cut-off elevation} & Spaceborne observations: 1\({}^{\circ}\); ground \\  & observations: 7\({}^{\circ}\) \\ \hline Arc length & 72 h \\ \hline Sampling interval & Ground and spaceborne BDS observations: 10 s \\ \hline Satellite position and velocity & Initial value from the broadcast ephemeris & Position: 10 m; velocity: 0.1 m/s \\ \hline Station coordinates & PPP static solutions & 2 mm for horizontal and 5 mm for vertical component \\ \hline \multirow{4}{*}{Tropospheric delay} & LEO spaceborne: none; ground stations: \\  & Saastamoinen model/GMF, the zenith wet delay \\ \cline{1-1}  & estimated every 2 h \\ \hline BDS and LEO satellite clock & Broadcast ephemeris and white noise (estimated \\ offsets & with a BDS clock as reference) & 5000 m \\ \hline Spaceborne receiver clock offset & Estimated as white noise & 1000 m \\ \hline Ground receiver clock offset & Estimated as white noise & 9000 m \\ \hline EOP & Fixed & IGS final products \\ \hline Ambiguity & Float solution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The processing strategies for the BDS/LEO combined POD. Finally, the BDS and LEO satellite orbit dynamics and clock products are calculated and the precise orbits can be derived via orbit integration with the orbit dynamics information. The estimated final orbits are compared with the \"true\" orbits, which are described in Section 2.1. A flowchart of the above procedures is given in Figure 3.  
Figure 3: Flowchart for the BDS/LEO combined POD.
## Abbreviations  
\begin{tabular}{l l} ABC & Artificial Bee Colony \\ ACOSS & Ant Colony Optimization and Scatter Search \\ ACTIM & Activity Time \\ ANGEL & Ant Colony and Genetic Algorithm with Local Search \\ APR & Activity Priority Rules \\ BKO & Best-Known Optima \\ BMAP & Best Mode Assignment Problem \\ CPM & Differential Evolution \\ DE & Differential Evolution \\ EDA & Estimation of Distribution Algorithm \\ EFT\({}_{i}\) & Earliest Finish Time of activity \(i\) \\ GCUMRD & Greatest Cumulative Resource Demand \\ LFT\({}_{i}\) & Latest Finish Time of activity \(i\) \\ LNRJ & Least Non-Related Jobs \\ LPSRD & Least Product Sum of Resource and Duration \\ LRP & Least Resource Proportion \\ LRS & Least sum of Non-renewable Resource \\ LTRU & Least Total Resource Usage \\ MIS & Most Immediate Successors \\ MNC & Maximum Number of Cycles \\ moEDA & Multi-Objective Estimation Distribution Algorithm \\ MRCPSP & Multi-mode Resource Constrained Project Scheduling Problem \\ \end{tabular}  
MRCPSP/max  
Multimode RCPSP with minimal and maximal time lags  
MS\(i\)  
Makespan of Stage \(i\)  
MSR  
Mode Selection Rules  
Most Total Successors  
NP-Hard  
Non-deterministic polynomial time hard  
NPV  
Net Present Value  
PERT  
Program Evaluation and Review Technique  
PSO  
Particle Swarm Optimization  
RCPCP
Resource Constrained Project Scheduling Problem  
ROT  
Resource Over Time  
RSM  
Resource Scheduling Method  
SA  
Simulated Annealing  
SFLA  
Shuffled Frog-Leaping Algorithm  
SGR  
Shortest Feasible Mode  
SGS  
Serial Generation Scheme  
SLK  
Minimum Slack First  
TS  
Tabu Search  
WRUP  
Weighted Resource Utilization Ratio and Precedence
## 3 Discussion
Since the Bessel-Gaussian localized wave is a kind of synthetic polychromatic wave, the selection of half-modulated pulse width \(T_{0}\) is not negligible. By plotting the received probability under different half-modulated pulse width \(T_{0}\) with different Bessel cone angle \(\alpha\) in Figure 7, we can see that the received probability of signal vortex modes increases with increasing the half-modulated pulse width \(T_{0}\). This result is consistent with the law of Figure 1b and the conclusion that the time average will reduce turbulent scintillation [21]. If the Bessel cone angle \(\alpha\) increases, the received probability decreases. This result is because that the small Bessel cone angle \(\alpha\) means the long collimation distance, resulting in weak wavefront distortion, while weak wavefront distortion leads to low vortex crosstalk. In other words, we had better select the small Bessel cone angle \(\alpha\) and large half-modulated pulse  
Figure 6: Received probability of the vortex modes of localized wave of Bessel–Gaussian amplitude envelope in seawater turbulence under different beam waist \(w_{0}\) and receiving aperture diameter \(D\). width \(T_{0}\) to weaken the vortex crosstalk. These conclusions are good news for the application of long-distance submarine wireless optical communication based on vortex mode.
## 3 Motion Compensation Method  
### Reference-Channel Motion Compensation Algorithm  
In our previous work, a reference-channel motion compensation algorithm (RCMCA) was proposed for echo correction in moving targets in 3D TWI [10]. The RCMCA is implemented on an improved hardware system. In order to form a large aperture, while reducing the number of array elements, four transmitting and eight receiving antennas are used in the designed TDM MIMO radar. In addition, a reference channel is added to the system, as is shown in Figure 3a. The designed sparse MIMO array is based on the MIMO array optimization method of concentric circle rotation. The four transmitting antennas are installed at the endpoints of the square, and the eight receiving antennas are equally divided into two groups, and placed on two concentric circles. The projections of  
Figure 1: 3D TWI of the moving target.  
Figure 2: The target echo. (**a**) Stationary target; (**b**) moving target.  
the receiving antennas are the same in all dimensions, as shown in Figure 3b. Adjusting the rotation angles and radii of the two concentric circles can reduce the sidelobes in all dimensions simultaneously. The array can provide optimal 3D imaging.  
In the RCMCA-based system, the switching process of the MIMO channel and reference channel is given in Figure 4. \(T_{M}/R_{N}\) represents the channel formed by the \(M\)th transmitting antenna and the \(N\)th receiving antenna, and \(Ref\_C\) is the reference channel. The reference channel and the MIMO channel work interleaved. The reference channel will work once after each MIMO channel completes data acquisition. The position of the reference channel remains unchanged, it can obtain the position difference of the moving target and use it in the compensation of the MIMO channel.  
The RCMCA can improve the quality of the 3D TWI of moving targets significantly [10]. However, the additional reference channel will lead to more difficulties in the hardware design. In this paper, the range migration of the 3D TWI of moving targets will be solved, without increasing the complexity of the system.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 3 Results
### Comparison of the Accuracy of Slopes Derived from Digital Terrain Models  
The comparison of the accuracy of slopes was based on the usage of the tool _Create random points_ in ArcMap. With this tool, 50,000 randomly placed points in the selected areas were created (five locations with 3 km \(\times\) 3 km areas). A minimum distance between points of three meters was set (points were 3 m or more apart) to ensure a more even distribution. Values of slopes were assigned from the prepared slope maps of individual data sets to the generated points using the tool Extract values to points. The points were then divided into classes with a step of inclination of 5\({}^{\circ}\) and comparative statistics were calculated for each class. Mean values and standard deviations were selected as test statistics. The resulting set of values of the most important statistics is shown in Table 3. The average slope was calculated as the mean value of the slope within one class. The average difference was given by subtracting slopes of a selected data set from the DTM 5. The standard deviation of the difference from the DTM 5 indicated the extent to which slope values were spread within one class. Figures 2-4 show histograms of all values of the standard deviations of slopes without division into classes, unlike in Table 3, where the values were divided into slope classes. The goal was to verify the trends in the accuracy of the data with two different methods. The histograms showed a deviation of the average value from the zero value for all elevation models, and at the same time, the range of standard deviations was compared. The lower the detail of the model, the higher the variance. The comparison of the accuracy of slopes derived from elevation models produced the following results. The number of points in each class did not represent an even distribution but fit more suitably to real conditions. Slopes up to 15\({}^{\circ}\) were 59% of the points and slopes above 30\({}^{\circ}\) were 8% of the points. The key threshold slopes for the assessment of passability, from 15\({}^{\circ}\) to 30\({}^{\circ}\), represented 33% of the total number of determined slopes. This was sufficient due to the higher number of testing points. Graphs evaluating the characteristics of elevation models with individual classes of 5\({}^{\circ}\) inclination are shown in Figures 5-7.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 3 Results
### Tests with Gaussian Noise  
This experiment uses 14 videos of the ALOV dataset, one for each category. All the tests use NCC, HSA with two generations, and GPU. We apply Gaussian noise to test the resistance of the proposal to external perturbations. Gaussian noise requires the configuration parameters of median and standard deviation to generate random numbers with that distrubution [63], and then these are added to the values of each color channel. Figure 15 shows an example frame without noise (a), this is labeled as noise level 0. Then, the same frame with Gaussian noise using median and standard deviation with a noise level of 50 shown in (b). Finally, the same example frame with Gaussian noise using median and standard deviation with a noise level of 100 shown in (c).  
The Figure 16a shows that there is a difference in F-score between videos that display different levels of noise. In other words, the underlying object tracker (NCC) shows a degree susceptibility to noisy image frames. The difference between noise level 0 and level 50 is 34.88% of the overall standard deviation, and the difference between levels 50 and 100 is of 10.38% of the total standard deviation. On the other hand, Figure 16b shows that noise has no noticeable effect on the average time per frame.  
A conclusion drew from the results obtained with the combination of NCC, HSA with two generations, and GPU is that Gaussian noise affects the accuracy. Nevertheless, the difference in F-score observed between no noise (level 0), and some noise (50 and 100) is in the range of 0.11 to 0.14,  
Figure 16: (**a**) Comparison of F-score survival curves between different levels of noise. (**b**) Comparison of average time per frame survival curves between different levels of noise.  
Figure 14: Comparison of Score-Time Efficiency between using parallel HSA (GPU BEE), parallel exhaustive search (GPU NO BEE) and sequential HSA (CPU BEE).  
Figure 15: Example frames showing visual effect of Gaussian noise as parameters increase. (**a**) Noise level 0; (**b**) Noise level 50; (**c**) Noise level 100.  
which is small considering that the full range of the F-score is from 0 to 1. On the other hand, the noise has no effect on speed.
# Propolis as Natural Product in the Oral Cavity Bacterial Infections Treatment: A Systematic Review
## 4 Results
### Antibacterial Activity of Propolis in Oral Cavity Bacterial Infections
#### 4.2.1 In Vitro Studies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bacteria** & **MIC** & **MBC** & **Reference** \\ \hline \multirow{3}{*}{_Actinomyces naslundii_} & 51.2 + 44.4 \(\mu\)g/mL * & 89.6 + 98.1\(\mu\)g/mL * & [19] \\ \cline{2-4}  & 800–1600 \(\mu\)g/mL & \textgreater{}1600 \(\mu\)g/mL & [69] \\ \cline{2-4}  & \(\leq\)0.63\% & & [36] \\ \hline _Actinomyces odontolyticus_ & 12.5 \(\mu\)g/mL & & [7] \\ \hline \multirow{3}{*}{_Aggregatibacter_ actinomycetemontians_} & 30–60 \(\mu\)g/mL & 200–400 \(\mu\)g/mL & [63] \\ \cline{2-4}  & 64 \(\mu\)g/mL & 128 \(\mu\)g/mL & [77] \\ \hline _Eikenella corordens_ & 12.5 \(\mu\)g/mL & & [7] \\ \hline _Fusobacterium_ & 30–60 \(\mu\)g/mL & 200–400 \(\mu\)g/mL & [63] \\ \hline \multirow{3}{*}{_Fusobacterium nucleatum_} & 256.0 + 147.8 \(\mu\)g/mL * & 358.4 + 132.1 \(\mu\)g/mL * & [19] \\ \cline{2-4}  & 30–60 \(\mu\)g/mL & 200–400 \(\mu\)g/mL & [63] \\ \cline{2-4}  & 12.5 \(\mu\)g/mL & & [7] \\ \hline \multirow{3}{*}{_Lactobacillus acidophilus_} & 48.8 + 46.8 \(\mu\)g/mL * & 68.8 + 54.8 \(\mu\)g/mL * & [19] \\ \cline{2-4}  & 4.0 \(\mu\)g/mL & 4–8 \(\mu\)g/mL & [77] \\ \cline{2-4}  & \(\leq\)0.63\% & & [36] \\ \cline{2-4}  & 600 \(\mu\)g/mL & & [90] \\ \cline{2-4}  & 12.5 \(\mu\)g/mL (propolis solutions dissolved in benzene, diethyl ether and methyl chloride) & & \\ \cline{2-4}  & 6.3 \(\mu\)g/mL (propolis solutions dissolved in acetone) & & \\ \hline \multirow{3}{*}{4.5 \(\mu\)g/mL (cold ethanolic propolisolis) 5 \(\mu\)g/mL (hot ethanolic propolisolis) 5000 \(\mu\)g/mL & [94] \\ \cline{2-4}  & 5000 \(\mu\)g/mL & 5000 \(\mu\)g/mL & [100] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of antibacterial activity of different propolis extracts.
### Needs of ISM, WI-FI, and Wireless Lan Applications
#### 1.1.2 Wi-Fi  
Both 0.5 GHz and 1 GHz Wi-Fi bandwidths matter. Wi-Fi runs across the 2.4 GHz and 5 GHz frequency bands, featuring 0.5 GHz and 1 GHz bandwidth, respectively, and 802.11b/g/n Wi-Fi uses the 2.4 GHz band's 0.5 GHz bandwidth. Indoor use and wall penetration are possible. For 802.11a/n/ac Wi-Fi, the 5 GHz band has 1 GHz bandwidth. High-capacity and high-performance Wi-Fi networks benefit from its more channels, lower interference, and higher data speeds.
## 3 Research Methodology
### Naive Bayes (NB)  
Google Analytics provides specific performance metrics when it comes to user engagement measurement, focusing on the behavior of users by examining organic results.  
Bayesian learning is used to describe learning approaches based on reasoning regarding conditional probabilities. Naive Bayes (NB) refers to a probabilistic classification model that assesses a set of probabilities by computing the frequency and value arrangements in each dataset under the hypothesis that no dependencies amongst features occur. NB is applied to calculate the probability of occurrence of another element that has already occurred using Bayesian theory. This allows each characteristic to provide an unbiased and independent final decision (Figure 5) [22; 23; 24; 25; 26; 27].  
The conditional probability of event \(X\) occurring given that event \(Y\) has occurred is as follows:  
\[P(X|Y)\equiv\frac{P(X\wedge Y)}{P(Y)} \tag{3}\]  
In [PERSON] theorem, there is a simple relationship between \(P(X|Y)\) and \(P(Y|X)\):  
\[P(X|Y)\equiv\frac{P(X\wedge Y)}{P(Y)}=\frac{P(X\wedge Y)\times P(X)}{P(Y) \times P(X)}=\frac{P(X\wedge Y)}{P(X)}\times\frac{P(X)}{P(Y)}=P(Y|X)\times \frac{P(X)}{P(Y)} \tag{4}\]  
The relationship is also usefully expressed as follows:  
\[P(X\wedge Y)=P(X|Y)P(Y)=P(Y|X)P(X) \tag{5}\]  
\(X\) and \(Y\) are said to be independent if:  
\[P(X\wedge Y)=P(X)\times P(Y) \tag{6}\]  
This means that there is no statistical relationship between \(X\) and \(Y\), and knowing the value of one gives you no help in predicting the value of the other [22; 23; 24; 25]  
Note that if \(X\) and \(Y\) are independent:  
\[P(X|Y)=P(X) \tag{7}\]  
Figure 5: Naive Bayes (NB).  
Regarding the conditional independence, it is interesting to examine whether there is a statistical relationship between two variables in the special case that a third variable has a particular value. \(X\) is said to be conditionally independent of \(Y\) given \(Z\) if:  
\[\forall x_{i},y_{j},z_{k}(P(X=x_{i}|Y=y_{j}\wedge Z=z_{k})=P(X=x_{i}|Z=z_{k})) \tag{8}\]  
where \(x_{i},y_{j}\), and \(z_{k}\) are possible values of \(X\), \(Y\), and \(Z\)[21; 22; 23; 24].  
This is usually written as:  
\[P(X|Y\wedge Z)=P(X|Z) \tag{9}\]
## 3 Materials and Methods
### Technological and Research Areas
#### 3.2.4 Object Recognition  
Analyzing and understanding the content of digital images is nowadays the focus of the digital image processing field and an ongoing research challenge for contemporary technological developments. It serves many disciplines, such as mechanical and robotic vision, geoinformatics, bioinformatics, computer graphics and virtual reality, and collaborates with scientific fields such as pattern recognition, neural networks, fuzzy logic, and artificial intelligence. Object recognition is involved in a vast variety of applications of contemporary technology and is often an integral part of most technology projects and products.
## 1 Introduction
The aim of this study was to investigate the influence of different soil characteristics pH, organic matter (O.M. ), and texture on tungsten uptake by _Zea mays_, and to provide a simple model to evaluate the uptake of increasing tungsten concentration in soil. _Zea mays_ was selected due to its relevant production in the Mediterranean area, which highlights the growing interest in this crop. In Italy, the average production for over a decade has been \(\sim\)9.5 t ha\({}^{-1}\), which is higher than the mean European production. The experiment was carried out at a greenhouse scale by growing the plants on three agricultural soils characteristic of Mediterranean areas spiked with increasing concentrations of sodium tungstate. The concentrations were chosen to be close to natural soil values conditions and to take into account potential anthropogenic contamination, which can reach 2000 mg/kg in particular areas [24]. The chosen range of concentrations can be considered environmentally significant due to the increase in electronic waste, which is a feature of all industrialized countries [14]. Obtained results show that soil properties regulate tungsten plants uptake, which increases with increasing pH and decreasing organic matter content.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 2 Methodology
### Classification Model for Exercise Intensity Assessment
#### 2.3.1 Theoretical Basis of Feature Extraction  
As illustrated in Figure 5, a typical ECG cycle comprises several characteristic waveforms, including three primary components: the P wave, the QRS complex, and the T wave. The voltage amplitudes and time phase parameters of these waveforms serve as quantitative indicators for ECG signal analysis. In this study, five time domain features, including P-wave to R-wave (PR) interval, S-wave to T-wave (ST) segment, R-wave to R-wave (RR) interval, Q-wave to T-wave (QT) interval, and QRS duration, were extracted and used as part of the input features for model training [40].  
In addition, HRV reflects the subtle fluctuations in the time intervals between consecutive heartbeats and serves as a key indicator for evaluating the autonomic regulation of cardiac rhythm [41; 42]. In time domain analysis, HRV is commonly characterized by parameters such as the standard deviation of normal-to-normal intervals (SDNN), the root mean square of successive differences (RMSSD), and heart rate (HR). In this study, SDNN was selected as the primary metric to represent HRV [43], and it was calculated as follows:  
\[SDNN=\sqrt{\frac{1}{N-1}{\sum_{i=1}^{N}}\big{(}T_{RR_{i}}-\mu\big{)}} \tag{1}\]  
where \(T_{RR_{i}}\) represents the RR interval and \(\mu\) denotes the arithmetic mean of all RR intervals.  
Since time domain analysis provides limited differentiation between the regulatory roles of the sympathetic and parasympathetic nervous systems, this study further explores the frequency-domain characteristics of the individual ECG signals. The analysis includes the low-frequency (LF) component (0.04 Hz-0.15 Hz), which corresponds to sympathetic  
Figure 5: The time domain characteristic waveforms of the ECG signal.  
activity, and the high-frequency (HF) component (0.15 Hz-0.4 Hz), which reflects parasympathetic activity [44]. These components are calculated as follows:  
\[LF=\sum_{f\in[0.04,0.15)}P(f) \tag{2}\]  
\[HF=\sum_{f\in(0.15,0.4]}P(f) \tag{3}\]  
where \(P(f)\) is the power density spectrum of the preprocessed ECG signal, which is calculated as follows:  
\[P(f)=\frac{\left|FFT\{x(t)\}\right|^{2}}{L} \tag{4}\]  
where \(x(t)\) is the time series signal obtained by interpolating the RR interval sequence from the continuous ECG signal, which responds to the trend of heart rate over time, and \(L\) is the length of the sequence.