esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 5 Discussion
### Comparison of SLABE and SEPHE Behaviors  
Two behaviors based on pheromones have been proposed to solve a common deployment task in unstructured environments. The behaviors differ mainly in the sensory needs required for their implementation. The SLABE strategy is much simpler and does not require a system for local positioning, which is required by the SEPHE strategy.  
In all the tests, both behaviors have shown that they are capable of achieving satisfactory coverage of the environment, taking into account the energy limitations imposed by the dynamics of the UAVs of the swarm.  
The data appear to confirm that a lower resolution of the location sensor corresponds to a higher collision rate and higher energy consumption of the swarm. We believe that this is due to the low resolution of the stigmergy sensor used in the SLABE approach. This sensor is not continuous and does not produce guidance information at all points in space, so the behavior is more subject to variation.  
Figure 19: Results of the SEPHE behavior simulations for the FGID take-off strategy in a _hard_ environment. The graph shows the average evolution and variation of the number of collisions, the time required to achieve 95% coverage of the environment, the energy consumed by the swarm, and the total flight time for 600 simulations (50 simulations on the same map for each combination of take-off time interval and maximum number of vehicles in flight).  
The increase in energy cost is determined by the granularity of the stigmergy sensor and by the indirect increase in the number of collisions.  
However, it is important to note that the average deployment times in both cases are comparable. We believe this to be an important conclusion since it reinforces the thesis that a swarm with a greater number of less-informed individuals can perform the same task as a swarm with a smaller number of more-informed individuals.  
If a system of stigmergy is available, as required by the SEPHE behavior, we can expect lower variability in all the parameters analyzed and a shorter time to achieve complete deployment. The systems required by each individual will increase the cost of the swarm. However, a swarm of low-cost drones with basic sensors can be used to develop a strategy with an average deployment time very similar to that of the SEPHE behavior at the cost of a higher probability of collision, greater energy needs (assuming that the drones that execute the behaviors have the same individual consumption) and a greater variability (but a much lower total cost).  
However, the devices that can execute the SLABE behavior can weigh less, thereby drastically reducing the energy consumption. Therefore, the SLABE behavior may require less energy for its implementation (as observed in Figure 14, where the drones develop a realistic energy model based on the requirements of ARDrone commercial drones).
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Histogram Analysis
#### 3.2.1 Chi-Square (\(\chi^{2}\))  
Chi-square \(\chi^{2}\) is a statistical test that is used in image encryption to test the randomness and uniformity of cipher images produced by an algorithm, by comparing the cipher image's histogram values with the expected distribution of a uniform histogram [50]. Chi-square tests each pixel of the cipher image to determine whether the distribution of pixel values is uniform or not. Chi-square (\(\chi^{2}\)) is mathematically represented as follows:  
\[\begin{split}\chi^{2}&=\sum_{i=0}^{255}\frac{(f_{i} -\mathcal{E})^{2}}{\mathcal{E}}\\ \mathcal{E}&=\frac{H\times W}{256},\end{split} \tag{8}\]  
where \(f\) represents the histogram of the cipher image, \(f_{i}\) is the cipher image's histogram value at index \(i\), \(\mathcal{E}\) represents the expected distribution value of a uniform histogram, and \(H\) and \(W\) are the height and width of the cipher image, respectively. A uniformly distributed histogram has a \(\chi^{2}\) value of 0. When the \(\chi^{2}\) value is low, it indicates that the histogram distribution of the cipher image is close to a uniform distribution. Furthermore, when the \(\chi^{2}\) value is higher, then the histogram is non-uniform. Since the histogram of an image is calculated for pixel values ranging from 0 to 255, we have 255 degrees of freedom, and if we assume a significance level of 0.05, then the critical chi-square value is 293.24. If the cipher image, \(\chi^{2}\), is lower or equal to the critical chi-square value, we accept the result as close to being uniform. From Equation (8), we can calculate the lower and upper limits of \(\chi^{2}\). To find the lower limit, we assume that the image has a uniform histogram, and then each element of the histogram, \(f\), will have a value of \(\frac{H\times W}{256}\). By substituting \(f\) in Equation (8), the lower limit can be calculated as follows:  
\[\begin{split}\chi^{2}&=256\times\frac{(\frac{H \times W}{256}-\mathcal{E})^{2}}{\mathcal{E}}\\ &=256\times\frac{(\frac{H\times W}{256}-\frac{H\times W}{256})^{2} }{\frac{H\times W}{256}}\\ &=256\times\frac{(0)^{2}}{\frac{H\times W}{256}}\\ \chi^{2}&=0,\end{split} \tag{9}\]  
From Equation (9), the lower limit for \(\chi^{2}\) is 0. For the upper limit, we assume that the image has one color, then the histogram, \(f\), will have one element with value \(H\times W\) and the rest will be zeros. By substituting \(f\) in Equation (8), the upper limit can be calculated as follows:  
\[\begin{split}\chi^{2}&=(255\times\frac{(0-\mathcal{ E})^{2}}{\mathcal{E}})+\frac{(H\times W-\mathcal{E})^{2}}{\mathcal{E}}\\ &=255\times\mathcal{E}+\frac{(H\times W-\mathcal{E})^{2}}{\mathcal{ E}},\end{split} \tag{10}\]  
Table 3 utilizes Equation (10) to compute the upper limit of \(\chi^{2}\) for different-sized images.
# Filtration Scheduling: Quality Changes in Freshly Produced Virgin Olive Oil
## 3 Results and Discussion  
### Turbidity Characterisation, Microbial Contamination, and Legal Requirements  
Filtration caused a deep change in the treated olive oil samples. The water content fell from 0.40 \(\pm\) 0.05 %_w/w_ to 0.07 \(\pm\) 0.02 %_w/w_, while the solids decreased from 0.28 \(\pm\) 0.08 %_w/w_ to 0.03 \(\pm\) 0.03 %_w/w_. These results are consistent with all of the studies in the literature that have applied depth filtration [20; 21; 27]. The turbidity fell from 2642 \(\pm\) 174 NTU to 18 \(\pm\) 4 NTU, and the water activity (aw) decreased from 0.70 \(\pm\) 0.03 to 0.42 \(\pm\) 0.04. These results are also consistent with the literature [11; 22; 26] regarding aw values and the turbidity grade of filtered oil.  
In the water-in-oil emulsions, like veiled olive oils, microorganisms (like yeasts and moulds) are dispersed within the microdroplets of water, because the oil matrix does not allow their survival and growth. For this reason, the removal of water microdroplets by filtration led to a statistically significant (\(p\)-value \(<\) 0.001) decrease in the microbial content. Specifically, the microorganisms grown on YPD agar medium fell from 3.8 \(\pm\) 0.2 log CFU/g in veiled oil samples to undetectable in filtered samples (Table 1). These results agree with those reported in the literature [20; 22; 31; 32]. This difference was still observed after 30 days of storage. Microbial survival in veiled oil samples can be explained by the following factors: (i) the dispersion of microorganisms in water, which is a good environment for microbial survival, (ii) the presence of insoluble solids, which are rich in microorganism nutrients, and (iii) a water activity value \(>\) 0.6, which supports microbial survival and enzyme activity [36; 37].  
All samples were characterised by chemical indexes that were well within the legal limits for the definition of extra virgin olive oil (Table 1). During storage, no differences emerged in the veiled and filtered olive oil samples. The filtered and veiled oil samples had similar values for acidity, K232, and \(\Delta\)K. Filtration takes to a small and not statistically significant (\(p\)-value \(>\) 0.05) increase in the peroxide number and to a decrease in the K270 value. Therefore, although auto-oxidation phenomena were slightly affected by the filtration treatment, they were negligible over time in the tested storage conditions.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### 5.2.1 Improved Power Efficiency  
Continuous and autonomous monitoring of water supply system infrastructures requires a constant power supply for the CPS sensing and communication devices. In severe applications, such as monitoring water quality and leaks in underwater or underground pipes, enhanced battery life and optimal energy usage are highly essential. Nevertheless, energy-improving strategies such as energy harvesters [149; 150; 151; 43], routing protocol algorithms [152; 44; 45; 46; 47], game-theoretic approach [153], and sensor data aggregation [154; 155; 156; 157] have been proposed with some level of success and limitations. As the CPS water system application requirements increases, further research studies are required to develop high performance and robust energy efficient methodologies.
# Dhad--A Children's Handwritten Arabic Characters Dataset for Automated Recognition
###### Abstract  
This study delves into the intricate realm of recognizing handwritten Arabic characters, specifically targeting children's script. Given the inherent complexities of the Arabic script, encompassing semi-cursive styles, distinct character forms based on position, and the inclusion of diacritical marks, the domain demands specialized attention. While prior research has largely concentrated on adult handwriting, the spotlight here is on children's handwritten Arabic characters, an area marked by its distinct challenges, such as variations in writing quality and increased distortions. To this end, we introduce a novel dataset, \"Dhad\", refined for enhanced quality and quantity. Our investigation employs a tri-fold experimental approach, encompassing the exploration of pre-trained deep learning models (i.e., MobileNet, ResNet50, and DenseNet121), custom-designed Convolutional Neural Network (CNN) architecture, and traditional classifiers (i.e., Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP)), leveraging deep visual features. The results illuminate the efficacy of fine-tuned pre-existing models, the potential of custom CNN designs, and the intricacies associated with disjointed classification paradigms. The pre-trained model MobileNet achieved the best test accuracy of 93.59% on the Dhad dataset. Additionally, as a conceptual proposal, we introduce the idea of a computer application designed specifically for children aged 7-12, aimed at improving Arabic handwriting skills. Our concluding reflections emphasize the need for nuanced dataset curation, advanced model architectures, and cohesive training strategies to navigate the multifaceted challenges of Arabic character recognition.  
deep learning; pre-trained models; child handwriting recognition; Dhad; Hijja 2020
## 4 Application of Fiber Optic Cables in Geophysics Exploration
### Vsp of Smf
Subsequently, a DAS SMF with active sources generated by rotary seismic sources was applied to facilitate autonomous permanent reservoir monitoring in the CO2 CRC Otway Field Site, Australia [62], by utilizing rotary seismic data as an active source that accurately controls routinely operated signal systems (ACROSS) [63; 64]. The DAS data were continuously collected and recorded in five wells with SMF cable cemented behind the casing and underwent automated processing at the close of each day. The data collected through the permanent reservoir monitoring system using DAS SMF and rotary seismic receivers yielded a notably higher SNR ranging from roughly 50 dB to over 100 dB for near-offset data shot. The DAS using SMF cable successfully showcased favorable data quality attributes, encompassing SNR and data repeatability. This was evident in time-lapse operations for the monitoring carried out using the DAS system. This research can be improved by enhancing the spectral-frequency content of seismic data by using dual-motor rotary seismic sources, subsequently combining high-resolution images for time-lapse acquisitions and the achievement of a high SNR for autonomous reservoir monitoring. Research applying SMF cables to VSP input DAS data is summarized in Table 2 below.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Algorithmic Complexity Analysis
#### 6.1.3 Model Training  
* Random Forest Training: The complexity of training a Random Forest model depends on the following:
* \(T\): Number of trees in the forest.
* \(d\): Maximum depth of each tree.
* \(f\): Number of features considered in each split.
* \(m\): Number of samples in the training set. For a single tree, the complexity is \(O(m\cdot f\cdot d)\). For trees \(T\), it becomes \(O(T\cdot m\cdot f\cdot d)\). Key Insight: Increasing \(T\) improves the performance of the model, but also increases the computational cost linearly.
###### Abstract  
Zeolites are micro- and mesoporous aluminosilicate minerals (both natural and industrially produced) widely used as catalysts and sorbents in domestic and commercial water purification and separation technologies. Their ability to selectively adsorb gases (i.e., water vapor, carbon dioxide, and sulfur dioxide removal) from an air stream makes them suitable for applications in odor reducing media used in filtering facepiece respirators (FFRs). FFRs are multilayer products in which the most important role is played by high-performance melt-blown electret nonwovens modified with activated carbon to adsorb malodorous compounds. Replacing carbon sorbents with zeolites could increase the efficiency of odor abatement, thus alleviating work-related hazards for individuals exposed to malodorous substances with adverse effects on human well-being. The objective of the present work was to analyze the influence of the textural parameters of zeolite and mesoporous silica materials on the protective and functional properties of polymeric nonwoven composites containing them. In our experiments, the longest breakthrough time against ammonia vapor was found for a nonwoven composite containing the inorganic mesoporous silica material type MCM-41. It was also characterized by high filtration efficiency against aerosols with solid and liquid dispersed phases (97% and 99% for sodium chloride and paraffin oil mist, respectively) at an airflow resistance of approximately 330 Pa. In turn, the composites containing the molecular sieve (SM-zeolite ZSM-5) exhibited the longest breakthrough time for acetone and cyclohexane vapors at the maximum allowable concentrations of 235 ppm and 81 ppm, respectively. Basic filtration tests showed that the composite was 97% effective against both test aerosols at an airflow resistance of 283.5 Pa.  
zeolites; mesoporous silica; sorption capacity; textural parameters; polymer composites Article The Effects of Textural Parameters of Zeolite and Silica Materials on the Protective and Functional Properties of Polymeric Nonwoven Composites [PERSON]  
1 Department of Personal Protective Equipment, Central Institute for Labour Protection--National Research Institute, 02-672 Warsaw, Poland; [EMAIL_ADDRESS]  
2
## 4 Parallel Implementation
Figure 6 shows how the efficiency of a parallel program depends on the number of processes involved in the calculation for the second computational block. Figure 2:2 The efficiency of parallel implementation for the first block of calculations. Figure 2:3 The efficiency of parallel implementation for the second block of calculations. The dependence of the acceleration on the program of the second block is shown in Figure 2:4  
The real speedup is slightly less than the estimated one, since a fraction of the time is lost when sending data at the beginning and when collecting results at the end of the parallel program execution. It seems that the speedup decreases as the number of processors increases, although the computational load on each process decreases. Figure 9 shows the total running time of the algorithm in relation to the synchronous implementation. The relative time was chosen to determine the dependence of the execution time on the number of processes in general. Figure 2:5 Dependence of acceleration on the program of the second block. Figure 2:6 Dependence of acceleration on the program of the first block. Although the execution time of each individual process decreases as the number of processes increases, the total time for 2 processes in relation to 1 process increases. The increase in total time when working with 2 processes can be explained by the fact that data fusion takes a lot of time, and therefore the time savings on each separate process are not significant. However, we managed to get a productivity increase of 29% for 8 and 16 processes. A further increase in the number of processes leads to a slowdown in the calculation and is not advisable. Thus, we can conclude that the most effective application of the algorithm is the parallelization of subtasks within the framework of parallel calculation of various input data. The considered algorithm is well suited for parallel implementation using multiprocessor systems; however, calibration is required by the number of processes and the amount of input data. Efficiency depends on the number of processes and its change can have a positive and negative direction.
## 4 Results
### Comparison of Different Models  
Based on the prepared datasets, a total of nine RF models was established. The corresponding ROC curves are shown in Figures 6 and 7, and the AUC values are shown in Tables 2 and 3. In terms of the sample representation forms, models based on the watershed unit datasets performed best overall, followed by the models based on the single-grid datasets. Models based on the multi-grid datasets had the worst performance. As for the negative-sample acquisition strategies, the negative-sample acquisition strategy based on the spy technique can make the models for multiple datasets perform well, especially for grid datasets. As for the watershed unit datasets, the IF algorithm was the key to improve the performance of the corresponding models. In real-world applications, a high-quality model also needs to consider its efficiency and computational cost. In increasing order, the computational costs in terms of sample representation forms are the single grid, watershed unit, and multi-grid. As for the negative-sample acquisition strategies, the IF algorithm can directly use all the samples for predictions, which makes it the most efficient. The SVM-based strategy is in second place because it requires a portion of unlabeled samples to be prepared beforehand as negative samples. The efficiency of the spy-based strategy is the worst because this strategy requires both negative and spy samples to be prepared in advance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Representation Forms** & **Conditioning Factors** & **SVM-Based** & **Spy-Based** & **IF-Based** \\ \hline \multirow{10}{*}{Multi-grid} & Rainfall & 0.027362 & 0.073548 & 0.014158 \\  & Altitude & 0.036237 & 0.061468 & 0.013859 \\  & Slope & 0.011209 & 0.033521 & 0.006381 \\  & Plane curvature & 0.003544 & 0.003607 & 0.001564 \\  & Profile curvature & 0.005132 & 0.008222 & 0.002221 \\  & TWI & 0.002520 & 0.004043 & 0.000903 \\  & NDVI & 0.004635 & 0.027623 & 0.001348 \\  & DTF & 0.018008 & 0.068456 & 0.010054 \\  & DTR & 0.203351 & 0.228164 & 0.134484 \\ \hline \multirow{10}{*}{Watershed unit} & Rainfall & 0.051102 & 0.067690 & 0.069879 \\  & Altitude & 0.054035 & 0.066648 & 0.079492 \\ \cline{1-1}  & Slope & 0.046257 & 0.067201 & 0.077270 \\ \cline{1-1}  & Plane curvature & 0.084715 & 0.103792 & 0.164850 \\ \cline{1-1}  & Profile curvature & 0.097196 & 0.114082 & 0.164520 \\ \cline{1-1}  & TWI & 0.040089 & 0.057586 & 0.070916 \\ \cline{1-1}  & NDVI & 0.01849 & 0.027924 & 0.037865 \\ \cline{1-1}  & DTF & 0.031382 & 0.042168 & 0.048835 \\ \cline{1-1}  & DTR & 0.099004 & 0.173459 & 0.215150 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 6: ROC curves based on different training datasets: (**a**) single grid; (**b**) multi-grid; (**c**) watershed unit.
## 3 Results and Discussion
### Disturbed by Turbidity  
#### 3.2.1 Effectiveness of DWM for Turbidity Measurement  
To verify the accuracy of the method, 10 different water samples from a city in Jilin Province were collected and measured their UV-Vis absorption spectra. The double-wavelength method proposed in this paper and the 546 nm single-wavelength method were used to predict the turbidity values of water samples, respectively. The prediction effect is shown in Table 7.  
It can be seen from Table 7 that the dual-wavelength method can predict the turbidity of water samples more effectively than the single wavelength method.
## 2 Study Area and Data Used
### Landslide Explanatory Variables  
Landslides usually occur by complex interactions among various explanatory variables, and there is no consensus about which landslide explanatory variables to use. In this study, 14 explanatory variables were selected, based on a literature review and data availability. These factors were divided into the following three categories: topography, hydrology, and forestry (Table 1, Figure A2). These factors were produced in raster format with a cell size of 10 \(\times\) 10 m, considering the scale of the input data, using ArcGIS 10.2 and ERDAS Imagine 2011; the total number of cells in the study area was 67,005. For the next process, the continuous variables among the explanatory variables were reclassified into seven classes, using ArcGIS 10.2. ArcGIS 10.2 provides various classification schemes, such as equal interval, standard deviation, natural break, quantile, etc. Natural break classification groups the classes based on break points that are relatively large jumps in data values. This classification method can be used to maximize the variance between classes. In addition, [PERSON] et al. (2016) [PERSON] et al. (2016) indicated that natural break classification is more appropriate for the classification of variables, because their results showed that the LSM produced had higher accuracy compared to that using a different classification method. Therefore, natural break classification was used in this study.