esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on the Recent Numerical Studies of Liquid Atomization
## 2 General Overview of Liquid Atomization  
### Atomization Types  
As shown in Figure 1, liquids can be atomized through different layouts of nozzles and external gas streams. The spray formed by single-phase liquids ejecting into stationary atmosphere is classified as non-gas-assisted, since the first factor leading to primary breakup is the internal turbulence and the external gas has weak influence [15]. The ejection speed and turbulence stem from the great pressure difference between inside and outside the nozzle, so the non-gas-assisted atomization is usually called pressure atomization [16]. Nozzles of round shape and slot are commonly adopted, and pressure swirl nozzles into which a swirl structure is built help expand the spray angle and generate finer droplets [17]. Several liquid streams colliding with each other rapidly can also lead to atomization, called impact atomization [18]. One advantage of non-gas-assisted atomization is the free mixing ratio of gas and liquid. While colliding jets effectively induce interfacial instabilities through momentum exchange, recent studies demonstrate that pulsating injection can further enhance atomization efficiency by modulating vortex formation. Periodic flow oscillations amplify Kelvin-Helmholtz instabilities at the gas-liquid interface, reducing critical Weber numbers for droplet detachment by 15-30% [19]. This mechanism promotes finer sprays (<50 \(\upmu\)m SMD) while suppressing satellite droplet formation, as evidenced by time-resolved PIV measurements in intermittent jets [20]. Such pulsation-driven effects warrant consideration alongside geometric nozzle optimization in next-generation atomizer designs.  
For gas-assisted atomization [26], gas is conveyed compulsively to intervene with the liquid either inside or outside the nozzle, and the aerodynamic forces become the first factor leading to primary breakup. Crossflow atomization [27] is usually adopted in jet engines, where the air usually flows at high-speed vertical to the fuel ejection direction. Coaxial atomization [28] is one kind of parallel-flow atomization when the liquid is ejected through a hole in the center of nozzle, while the high-speed gas is ejected through the toroidal slot around the nozzle. Sometime there could be an angle between the path of the liquid and gas instead of parallel, so the gas impacts somewhere downstream of the liquid exit and stimulates the breakup. Coaxial atomization is often encountered in powder production from liquid metal.  
Pre-filming atomization [29] is being investigated for use in lean burn combustors to further promote fuel efficiency and low pollution. The liquid film is pre-distributed on a plane through conceal holes or wall jet and the high-speed gas is blowing from both up  
Figure 1: Atomization types (**a**) without and (**b**) with air-assisted [4; 21; 22; 23; 24; 25].  
and down sides of the plane. The instabilities between the gas and liquid grow before the plane edge after which the atomization starts from the primary breakup of the liquid film.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 2 Background
### Positional Accuracy of Web Imagery
The uncertainty in positional accuracy due to georeferencing and orthorectification errors can limit the use of web maps. If positional accuracy better than 1.5 m is required in the display of geolocated data on a web map, an empirical correction could be made based on an assessment of the positional accuracy of the GE imagery. For local mapping, a 2D conformation transformation can be used for transformation between grid coordinate systems using a four-parameter transformation (also known as a similarity or Helmert's transformation) based on the parameters of scale, rotation, and translation in both x and y directions. These four parameters can be estimated when two horizontal control points are known in both Universal Transverse Mercator (UTM) coordinate systems. In this conformal transformation, straight lines remain straight, and orthogonal angles are preserved. This procedure is illustrated in the following case study.
## 4 Results and Discussion
### Permutation Analysis  
The effectiveness of the developed classifiers was measured using the \(k\)-fold cross validation technique, reporting average accuracy rate up to 84.63 \(\pm\) 0.63%. However, in order to prove that the estimated classification results are not achieved by chance, the permutation-based \(p\)-value statistical test is performed [66]. Permutation analysis consists in evaluating the \(p\)-value under a specific null hypothesis, that is: features and targets are independent. To this end, \(M\) permutations of the labels are generated and the corresponding statistical measure (here, the accuracy) is evaluated. Overall, A\({}_{j}\) accuracy values are estimated (with \(j\) = 1, 2,..., \(M\)). Then, the \(p\)-value is calculated as fraction between the number of A\({}_{j}\) accuracies equal or higher than the accuracy rate evaluated with the original features-labels relationship (i.e., 84.63%) and the total number of permutations (i.e., \(M\)). If \(p\)-value is smaller than a certain threshold \(\alpha\) (generally 0.05), the null hypothesis is rejected leading to the conclusion that the classifier is statistically independent. Ideally, all the possible permutations of the labels should be tested; but, since this is computationally too much expensive, \(M\) = 100 is used (as it has been shown to produce stable results [66]). Simulation results reported that \(p\)-value = 0.00/100 = 0.00 \(<\) 0.05. Hence, the null hypothesis was rejected and the developed classifier is statistically significant.
### Wastewater-Based Epidemiology during COVID-19 Pandemic  
#### N. S. Thomaidis  
Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771 Athens, Greece; ntho@chem.uoa.gr  
December 2019 will be remembered as the springboard of Coronavirus Disease 2019 (COVID-19), which causes viral pneumonia and many other health effects. However, the measures taken to restrict the virus spread affected societies, economies, and, indirectly, mental and physical health, habits, and lifestyle. Wastewater-based epidemiology became a promising chemical tool for monitoring SARS-CoV-2 infection dynamics, as well as a population's chemical exposure, public health, lifestyle, and how the ongoing pandemic affected the aforementioned parameters [1; 2; 3]. In the present study, the effects of COVID-19 pandemic on the population of Athens, employing WBE, are presented. During the first wave of the pandemic (March-April 2020), the analysis of wastewater revealed significant increase in surfactants (+196%) and biocides (+152%), while a significant decrease was observed for tobacco and industrial chemicals (\(-33\%\) and \(-52\%\), respectively). For illicit drugs, and especially for stimulants such as amphetamine, methamphetamine, and cocaine, an increasing trend was observed from March 2020 to March 2021. In addition, during March 2020, when there was no effective treatment for SARS-CoV-2, antiviral and antibiotics demonstrated significant increases (+170% and +57%, respectively), while NSAIDs use was decreased (\(-27\%\)). Oxazepam, one of the most common benzodiazepines, reached peak use levels during December 2020, remaining high during January to March 2021. On 7 th of November, when the 2 nd total lockdown was announced, SARS-CoV-2 viral load showed a stable trend until the end of January, when extremely high viral loads were observed for the first time in Athens. For the following three months (February to May), the observed increase was the result of the SARS-CoV-2 alpha-variant, as revealed by NGS analysis of RNA extracts. After the end of the restrictions and lockdown (May to June) SARS-CoV-2 viral load demonstrated a decreasing trend during June, while the prevalence of delta-variant and the absence of restriction measures resulted in a fourth wave with high viral load in wastewater in July and August 2021. The time-trend analysis of viral load in the wastewater of Athens correlated with infections dynamics and epidemiological variables, demonstrating the potential of WBE to be used as an early warning system of the pandemic dynamics.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## Appendix A Two-Dimensional Euler-Bernoulli Beam Element  
As shown in Figure 11, the two-dimensional Euler-Bernoulli beam element has two nodes with the DOFs of Equation (11).  
\[\hat{d}^{\prime}_{e}=\left[\begin{array}{cccc}\hat{u}^{\prime}_{1}&\hat{v}^{ \prime}_{1}&\hat{\partial}^{\prime}_{1}&\hat{u}^{\prime}_{2}&\hat{\partial}^{ \prime}_{2}&\hat{\partial}^{\prime}_{2}\end{array}\right]^{T} \tag{12}\]  
The strain \(\epsilon^{\prime}\) at an arbitrary position in the element is formed by the longitudinal strain \(\epsilon^{\prime}_{xx}\) and the strain \(\epsilon^{\prime}_{yy}\) generated by the Poisson effect.  
\[\epsilon^{\prime}=\left[\begin{array}{cc}\epsilon^{\prime}_{xx}&\epsilon^{ \prime}_{yy}\end{array}\right]^{T} \tag{13}\]  
Matrix \(B_{e}\) relating the node DOF \(\hat{d}^{\prime}_{e}\) of the element with the strain \(\epsilon^{\prime}\) at an arbitrary position in the element is as follows.  
\[B_{e}=\left[\begin{array}{c}B_{xx}\\ -\
u B_{xx}\end{array}\right] \tag{14}\]  
where  
\[B_{xx}=\left[\begin{array}{cc}-\frac{1}{L}&-y_{e}\left(-\frac{6}{L^{2}}+ \frac{12\
u_{e}}{L^{3}}\right)&-y_{e}\left(-\frac{4}{L}+\frac{6\
u_{e}}{L^{2}} \right)&\frac{1}{L}&-y_{e}\left(\frac{6}{L^{2}}-\frac{12\
u_{e}}{L^{3}}\right) &-y_{e}\left(-\frac{2}{L}+\frac{6\
u_{e}}{L^{2}}\right)\end{array}\right] \tag{15}\]  
The displacement \(d^{\prime}\) at an arbitrary position in the element is formed by the longitudinal displacement \(u^{\prime}\) and the transverse displacement \(v^{\prime}\).  
\[d^{\prime}=\left[\begin{array}{cc}u^{\prime}&v^{\prime}\end{array}\right]^{T} \tag{16}\]Matrix \(N_{e}\) relating the node DOF \(\hat{d}^{\prime}_{e}\) of the element and the displacement \(d^{\prime}\) at an arbitrary position in the element is as follows.  
\[N_{e}=\left[\begin{array}{cc}1-\frac{x_{e}}{L}&-y_{e}\Big{(}-\frac{6x_{e}}{L^{ 2}}+\frac{6x_{e}^{2}}{L^{3}}\Big{)}&-y_{e}\Big{(}1-\frac{4x_{e}}{L}+\frac{3x_{e }^{2}}{L^{2}}\Big{)}&\frac{x_{e}}{L}&-y_{e}\Big{(}\frac{6x_{e}}{L^{2}}-\frac{6x _{e}^{2}}{L^{3}}\Big{)}&-y_{e}\Big{(}-\frac{2x_{e}}{L}+\frac{3x_{e}^{2}}{L^{2}} \Big{)}\\ 0&1-\frac{3x_{e}^{2}}{L^{2}}+\frac{2x_{e}^{3}}{L^{3}}&x_{e}-\frac{2x_{e}^{2}}{ L}+\frac{x_{e}^{3}}{L^{2}}&0&\frac{3x_{e}^{2}}{L^{2}}-\frac{2x_{e}^{3}}{L^{3}}&- \frac{x_{e}^{2}}{L}+\frac{x_{e}^{3}}{L^{2}}\end{array}\right] \tag{10}\]
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis
#### 4.4.3 Comparison of the Average Time Consumption of Feature Extraction per Image  
Tables 13 and 14 list the average time consumption used for feature extraction per image of the different methods considered in this study for the self-built dataset and VAIS dataset. As can be observed, the feature extraction speed of improved CNN was faster than the other methods due to its shallow layers and few parameters. Due to the combination of HOG and LBP features, the average time consumption of feature extraction per image increased a little. However, the HOG and LBP features were fused with the high-level features extracted by the improved CNN network to further supplement the contour and spatial texture feature of ship images. The combination of three features could more accurately represent the features of ship images, and obtained a more robust ship classification model. In addition, experimental results also show that the classification accuracy of the proposed method was the best, and the time consumption of 14.074 ms and 15.329 ms was also relatively fast. Meanwhile, the CNN method used mini-batch technology, so the feature extraction speed was faster than traditional methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Time Consumption of Feature Extraction (ms)** \\ \hline HOG + SVM & 4.061 \\ LBP + SVM & 1.460 \\ CNN [29] & 0.056 \\ Fine-AlexNet & 0.682 \\ Fine-ResNet-18 & 0.829 \\ Fine-VGG-16 & 0.772 \\ HOG + CNN & 7.504 \\ LBP + CNN & 7.410 \\ Improved CNN & 0.047 \\ Proposed Method & 14.074 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The average time consumption of feature extraction per image for the self-built dataset.  
Figure 16: Confusion matrix and its normalization of the proposed method using the VAIS dataset. Note that numbers 0–5 denote medium-other, merchant, medium-passenger, sailing, small, and tug ship types, respectively; (**a**) confusion matrix; and (**b**) confusion matrix normalization.
# Evaluation of Noise-Reduction Techniques for Gas-Turbine Test Stands: A Preliminary Analysis
###### Abstract  
Emphasizing the importance of acoustic attenuation in maintaining compliance with stringent noise regulations and enhancing workplace safety, this analysis covers theoretical and practical aspects of prediction methods used for the development of sound attenuators for gas-turbine testing stands. This paper presents a preliminary analysis and evaluation of the improvement of the Embleton method for projecting a noise attenuator for industrial applications, especially for gas-turbine test stands. While primarily focusing on the static acoustic behavior of the attenuator, certain considerations were also made regarding flow conditions, Mach number-dependent attenuation, pressure drop, and self-generated noise aspects to provide a comprehensive perspective on applying a suitable evaluation method. The study investigates different calculation methods for the assessment of noise reduction for linear and staggered baffles applied on a scaled reduced model of an attenuator. Thus, the critical parameters and development requirements necessary for effective noise reduction in high-performance gas-turbine testing environments will be evaluated in a downscaled model. Key factors examined include the selection of design parameters and configurations from various topological options (single, double, and triple parallel baffles vs. double and triple staggered baffles). Advanced computational methods, like analytic and finite-element analysis (FEM), are used to predict acoustic performance and evaluate the prediction method. Experimental validation is performed to corroborate the simulation results, ensuring the reliability and efficiency of the attenuator. The results indicate that an improved prediction method led to a better design for a sound-attenuator module, which can significantly reduce noise levels without compromising the operational performance of the gas turbine inside a test cell.  
baffle attenuator; gas-turbine stand; experimental validation; Embleton's method; anechoic chamber; rectangular duct flow +
Footnote †: journal: Applied  
Accepted: 20 June 2024  
Published: 29 June 2024
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation  
In this section, we evaluate the performance of the proposed EOAR protocol. The experiment verifies the packet delivery ratio, end-to-end delay, energy consumption and average network lifetime. We compare EOAR with HHVBF and VHGOR protocols.
## 5 Conclusions  
In this article, approaches to filtering chaotic activities by using the relationship between activities have been proposed. The approaches can identify and filter chaotic activities by extracting the direct following relationships and direct preceding relationships between activities in the event log. These approaches can identify and delete multiple activities in each algorithm run by using the judgment conditions, and these methods do not involve complex operations. The proposed methods are compared with the frequency-based chaotic activity filtering method and the entropy-based chaotic activity filtering methods by using simulation logs and real event logs. It can be seen that the accuracy and time performance of the proposed filtering method are closely related to the used log itself, that is, for different event logs, the filtering method proposed in this paper presents different effects. However, it can be observed that the accuracy of the proposed filtering methods is better than that of the frequency-based filtering method, and is close to that of the entropy-based chaotic activity filtering method, so the proposed methods have certain advantages in the accuracy of chaotic activity filtering. Moreover, the proposed filtering methods are superior to the traditional frequency-based filtering method and the entropy-based filtering methods in terms of time performance. For simulation event logs, the filtering methods proposed in this paper can improve the operating efficiency by 23.4% on average, and for real event logs, the filtering methods proposed in this paper can improve the operating efficiency by 84.25% on average. It can be concluded that the filtering methods proposed in this paper can effectively shorten the running time required for chaotic activity filtering and effectively improve the efficiency of chaotic filtering. Therefore, the methods proposed in this paper can reduce the pre-processing time of the event log and improve the efficiency without losing too much accuracy. In general, these methods can ensure that the filtered event log has a certain integrity. Meanwhile, since the current mainstream method for the indirect evaluation of the accuracy of the filtering method is to evaluate the quality of the filtered process model obtained after filtering the event log, the smaller the number of filtered event logs obtained after filtering the original event log, the shorter the time required for the evaluation of the filtering method. The filtering approaches proposed in this paper can also reduce the number of logs obtained after filtering the original logs, so they can shorten the time required for the indirect evaluation of chaotic activity filtering approaches. By using a simulation log and real event log to test, it can be concluded that the direct filtering method and indirect method based on the relationship between activities put forward in this article have certain effectiveness and high efficiency.
# On Coding and Decoding Reconfigurable Radiation Pattern Modulation Symbols
## 3 Results  
In any digital communication scheme, the BER is the standard figure of merit for quantifying its performance. Different computational methods are used to calculate the BER,and the Monte-Carlo simulation is by far the most popular technique among these [29; 30]. The Monte-Carlo simulation uses a deterministic approach to calculate the BER. The usual approach consists of producing pseudo-arbitrary \(N\) bits, simulating the transmission and detection of these bits, and finally counting the number of mismatches in the output. The symbols are demodulated using a maximum-likelihood (ML) demodulation, and the number of mismatches between the input and output will be counted as \(n\) errors. The BER is then  
\[BER=\frac{n}{N}. \tag{3}\]
# Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
## 2 Proposed Methodology
### YOLOv3  
You Only Look Once is a family of single-shot object detectors. YOLOv3 improves over the second version in the sense that it incorporates multi-scale detection, improves the loss function and makes use of a stronger feature extraction network [44].  
YOLOv3 runs over two blocks, namely a feature extractor and an object detector. Object scale change is tackled by extracting the features at three different scales.  
Figure 1: Mask R-CNN architecture for crop circle detection.  
Feature extraction is performed via Darknet-53, which consists of 53 layers. Features from the last three residual blocks are considered for further multi-scale object detection. This latter is formed of several 1 \(\times\) 1 and 3 \(\times\) 3 Conv layers, topped with a 1 \(\times\) 1 Conv layer to produce the final output. Moreover, large-scale features are concatenated with medium-scale features, and these latter are concatenated with small-scale features, which enables the small-scale features to avail the results of large-scale features.  
Object search is addressed by means of a set of anchor boxes over a grid of the image, which all share the same centroid, and the box that shares the highest intersection over union with the ground truth is selected.  
As per the loss function, four terms are envisioned, namely the objectness loss, width and height loss, centroid loss, and classification loss. We choose the architecture depicted in Figure 2.
## 5 Results and Discussion
### Spatial Analysis
#### 5.3.2 Spatial Autocorrelation  
We formulated the null hypothesis as the presence of complete spatial randomness. To test this, we conducted the Moran's I test using two k-nearest-neighbors weight matrices, with k = 4 and k = 8. With all the \(p\)-values (k = 4, p = 0.003; k = 8, \(p\) = 0.001) being statistically significant and positive z-scores (k = 4, Zi = 3.2037; k = 8, Zi = 6.6389) obtained using different weight matrices, we reject the null hypothesis. The results show a statistically significant spatial correlation, implying that the spatial distribution of high or low values of CF-EB clusters more spatially than it would under random spatial processes.  
Figure 7 presents the univariate Local Spatial Autocorrelation results, called LISA cluster maps, for CF-EB using different numbers of neighbors (k = 8 and k = 4). The patterns exhibited by Figure 6(a),b are strikingly similar. Both univariate LISA cluster maps reveal more clusters with positive local spatial correlation (high-high and low-low) than those with negative local spatial correlation (high-low and low-high).  
Locations with a positive local spatial correlation can be understood as high-high or low-low. In both univariate LISA cluster maps (k = 4 and k = 8), the low-low clusters are chiefly located around Central Park and the Chelsea area, representing low-risk locales. These low-risk areas could be the product of numerous factors such as well-structured road  
Figure 6: The spatial map of grid-based EB-estimated crash frequency.  
Figure 7: Univariate LISA cluster maps.  
infrastructure, effective traffic management, or lower traffic volumes. Large pedestrian zones around Central Park or lower vehicle speed limits might also contribute to reduced crash rates. High-high locales, on the other hand, predominantly encompass areas near the Queensboro Bridge linkage road and Midtown East, between 34 th Street and 42 nd Street. This could result from high traffic volume, intricate road systems, elevated vehicle speeds, or insufficient traffic control measures. The close proximity to the Queensboro Bridge, a significant transport route, may lead to increased traffic volumes and, consequently, more accidents.  
In terms of negative local spatial correlation, both LISA cluster maps display fewer high-low locations but a significant number of low-high locations. High-low clusters primarily surround low-low clusters, such as the Central Park area. This arrangement suggests a sudden transition from low crash frequency regions to higher crash frequency regions, which could be caused by abrupt changes in road conditions, traffic volume, or other influential factors. Meanwhile, low-high clusters are predominantly situated near some high-high locations, such as the Queensboro Bridge linkage road area and Midtown East, between 34 th Street and 42 nd Street. Despite being surrounded by high-risk areas, these relatively safer grids might possess certain characteristics or interventions, such as lower speed limits, or well-designed intersections, that render them less susceptible to crashes.