esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This article comprehensively compares the effects of spraying clear water and different concentrations of PEG-20,000 solution on the droplet size, distribution and deposition characteristics of the fan nozzle Tejet 80-03 VP. The conclusions are as follows:  
1. Under the same spraying medium condition, when the pressure increased from 0.2 MPa to 0.4 MPa, the \(D_{V0.5}\) of four spraying media respectively decreased by 29.7%, 19.7%, 22.9%, and 21.9%, and the \(\Phi_{Vol<150\ \mathrm{\mu m}}\) increased by 42.2%, 24.6%, 29.7% and 83.2%.
2. Under the same pressure, the \(D_{V0.5}\) of spraying 10 g/L PEG-20,000 solution increased by 5.2%, 13% and 20% compared with clear water at three pressures, and the \(\Phi_{Vol<150\ \mathrm{\mu m}}\) decreased by 4.9%, 11.5% and 16.7%; In the same way, the \(D_{V0.5}\) of spraying 20 g/L PEG-20,000 solution increased by 10.8%, 15.5% and 21.3% and the \(\Phi_{Vol<150\ \mathrm{\mu m}}\) decreased by 9.7%, 13.4% and 17.6%; The \(D_{V0.5}\) of spraying 30 g/L PEG-20,000 solution increased by 28.8%, 40.5% and 43% and the \(\Phi_{Vol<150\ \mathrm{\mu m}}\) decreased by 67.4%, 65.6% and 58%.
3. Under the same spraying medium condition, in the horizontal and vertical spray direction, the deposition increases with the increase of pressure. Both \(DPRP_{V}\) and \(DPRP_{H}\) of each spraying medium decrease with the increase of pressure.
4. Under the same pressure, spraying clear water in the horizontal and vertical spray direction, the total deposition is greater than that of spraying PEG-20,000 solution at three concentrations. As the concentration of PEG-20,000 solution increases, the amount of droplets decreases overall. The \(DPRP_{V}\) and \(DPRP_{H}\) of three concentrations of PEG-20,000 solution are greater than that of clear water.  
When the external wind speed or temperature is high, in order to reduce the drift and evaporation of the droplets in the air, low pressure and large droplets could be selected for spraying operation, and the PEG-20,000 could be appropriately added.  
Data curation, Y.J.; formal analysis, Y.J.; investigation, Y.J., Q.Z., Y.T., [PERSON] and S.W.; methodology, [PERSON] and S.D.; supervision, [PERSON] and S.D.; writing--original draft, Y.J.; writing--review & editing, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2017 YFD0701000).  
Not applicable.  
The data used to support the findings of this study are available from the first author or the corresponding author upon request.  
The author are very grateful to my tutors [PERSON] and [PERSON], and also to the help of teacher [PERSON] and classmates [PERSON], [PERSON] and [PERSON] during the tests.  
The authors declare no conflict of interest.
# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
## 2 System Overview
### Signal-Processing Chain  
A custom signal-processing chain was developed to condition the data for the image reconstruction step. The first step in the signal-processing chain is to check the health of all source transducers and receive hydrophones. Any sources that do not transmit or any dead or noisy transducers are excised, so they will not be included in the processing. The next step is to match the filter receiving data by convolving it with a replica of the transmitted pulse from each source. Matched filtering increases the temporal resolution of the data and improves the SNR by suppressing noise through coherent integration. Because the waveforms used in the ocean testing sonar system are generated on-demand in hardware and to provide information on the exact time of transmission, the transmitted signals are also recorded on separate loopback channels. The source transducers typically cannot transmit the full frequency extent of the generated waveforms (i.e., they have significant roll-off away from their peak resonant frequency), so a bandpass filter is also applied to suppress the artifact.  
Figure 4: A block diagram of the sparse sonar data acquisition system for \(N_{RX}\) receivers (blue) and \(N_{TX}\) transmitters (red).  
The final step of the signal-processing chain is calibrating the data, as shown in Figure 5, by time-aligning the data across receive channels and refining the estimates of array element locations and sound speed needed for image processing. Many hydrophones made up the sonar array used in the ocean testing, which required 12 separate 24-channel audio interfaces (i.e., analog-to-digital converters). Because of this requirement, slight timing differences can exist between channels connected to different audio interfaces. A broadband, pseudo-random noise signal was generated and injected into one channel on each audio interface. These channels were then used to estimate (using time correlation) and correct the relative time offsets between channels on different audio interfaces. The synchronized data were then used to refine the estimates of the transducer and hydrophone locations and the sound speed in the water. The refinement was completed using iterative optimization routines to maximize the total energy at each receiver after summing over the one-way (source-to-receiver or \"direct path\") signal from all sources. These refined estimates are subsequently used to perform range migration in the image processing step.
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 2 Background
### Discrete Cosine Transform  
Discrete cosine transform (DCT) is a key technology in image processing, which converts images from the spatial domain to the frequency domain [24]. DCT decomposes the image into components of different frequencies, where the low-frequency component reflects the overall structure of the image, while the high-frequency component contains the details of the image. DCT is widely used in the fields of image compression, coding and feature extraction. Its advantages are energy concentration, strong robustness, independent transform coefficients and wide adaptability. DCT also plays an important role in image watermarking processing. Through frequency domain conversion, DCT decomposes image information into components of different frequencies, making watermark embedding more efficient. Since the watermark can be hidden in the high-frequency component and is highly similar to the original image, it is not easy to be detected. In addition, the characteristics of DCT make the watermark have certain robustness. Even if the image is compressed, rotated or scaled, the effective extraction of the watermark can be guaranteed by selecting the appropriate DCT coefficients for watermark embedding. This is because the energy of DCT is mainly concentrated in a few low-frequency coefficients, which are more important to the overall characteristics of the image, so it can resist the influence of image processing operations.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 2 Artificial Intelligence: An Overview (Fundamentals and Applications in Agriculture)
### Different AI Application in AgroSciences
#### 2.3.2 AI in Precision Agriculture  
Precision agriculture is a farming management concept based on the observation, measurement, and response to inter- and intra-field variability in crops. AI plays a crucial role in this field by processing data from various sources, such as satellite imagery, drones, and sensors, to provide actionable insights [9]. Machine learning algorithms analyze this data to predict pest outbreaks, optimize irrigation schedules, and improve yield forecasts [30]. AI-driven systems can monitor crop health in real-time, allowing for timely interventions that enhance crop quality and yield. For example, deep learning models can analyze drone-captured images to identify pest or disease-affected areas, enabling targeted treatments that reduce chemical use and environmental impact [14].  
Several studies have demonstrated the potential of AI to optimize specific agricultural practices. For instance, [PERSON] et al. (2023) designed an IoT-based smart irrigation system integrated with AI and predictive models. This system saved up to 11% of irrigation water compared to traditional methods, while maintaining high crop yields. By integrating real-time data from soil moisture sensors and environmental variables, the system automatically adjusts irrigation schedules, reducing water waste [32]. Moreover, AI models, particularly those utilizing deep learning, have been applied for early detection of pests and diseases in crops. A study by [PERSON] et al. (2019) developed a non-destructive monitoring system using depth cameras for seedling health, which could automatically identify diseased plants and alert farmers in real-time. This method of management by allowing for timely interventions, reducing crop loss [33].
## 4 Discussion
### Signal Quality  
First, it is important to notice that during this experiment, the signal quality was good, with an average horizontal dilution of precision (0.83) lower than the one accepted for a good quality signal [14], and close to the one reported by [PERSON] et al. (0.74) [8] and other studies [13; 20]. In addition, a large number of satellites were connected to the GPS (16 on average), which is essential for a good-quality signal [14], and also close to the data of [PERSON] et al. [8] and other studies [13; 20]. As a result, signals from both studies were of similarly good quality, so they likely do not explain potential differences between the two studies.
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 2 Materials and Methods
### Training Subset Selection
#### 2.1.2 Applied Hypothesis Tests  
Five well-known hypothesis tests were applied for testing the normal distribution of the residual sets. Most of these tests belong to the group of empirical distribution function tests, where the difference between the empirical and hypothesized cumulative distributions is evaluated. From that group, Kolmogorov-Smirnov [29], Anderson-Darling [30] and Lilliefors' tests [31] were selected. KS and Lilliefors' tests belong to the supremum class of methods where the greatest vertical difference between the hypothesized and empirical distribution is evaluated. In KS test, the hypothesized distribution is completely specified, whereas in Lilliefors' test, the parameters are estimated based on the sample. Furthermore, AD test comes from the class of quadratic methods, where the squared difference between the empirical and hypothesized distributions is evaluated.  
As categorized in [34], one test from the class of moment tests and one from the class of correlation tests were selected as well. Jarque-Bera belongs to the class of moment tests, where the deviation from normality is detected based on the third and fourth moments of the distribution, namely, skewness and kurtosis [32]. Finally, Shapiro-Wilk test is a correlation test based on the ratio of two estimates for scale obtained from order statistics [33].  
The applied MATLAB(r) functions for KS, AD, Lilliefors' and JB tests were _kstest_, _adtest_, _lillietest_ and _jbtest_, respectively. SW was tested with the MATLAB(r) code provided by [35]. The standard 5% significance level was applied in all the hypothesis tests. The KS tests were conducted on the standardized residuals (\(\mu\) = 0, \(\sigma\) = 1) and in the other tests the residuals were not standardized. In the AD test, the hypothesized normal distribution was generated based on the mean and standard deviation of the residual set by using the _makedist_ function.
## 2 Optimization Model  
The problem regarding the optimal integration of FACTS devices in electrical distribution grids aims to minimize the annual costs related to energy losses and the investments made in FACTS installation. This problem generates an optimization model with the structure of a mixed-integer nonlinear programming (MINLP) model. This is because the optimization model contains binary/integer variables related to the location of the FACTS devices. At the same time, the model includes the continuous variables associated with power flows, nodal voltages, and the size of the FACTS devices. Figure 1 illustrates an example of a branch in an electrical network with FACTS devices installed.  
In this paper, the following notation is used. The sets \(\mathcal{H}\), \(\mathcal{B}\), and \(\mathcal{N}\) encompass all the analysis periods, network branches, and network nodes, respectively. \(\mathcal{R}\) and \(\mathcal{C}\) denote the sets of real and complex numbers, respectively. The subscripts \(k\) or \(m\) denote the nodes of the test system, the subscript \(l\) represents any branch connected between nodes \(k\) and \(m\), and the subscript \(h\) represents a specific period under analysis. The superscripts \(s\) and \(r\) denote the sending and receiving power flows of the distribution line connected between nodes \(k\) and \(m\), while the superscripts \(g\) and \(d\) represent variables related to generation and demand, respectively. The variable \(v\) denotes the nodal voltage, and \(s\) corresponds to apparent power in a node or distribution line. The operator \(\|\cdot\|\) denotes the Euclidean norm,while the operators \(\mathrm{real}(\cdot)\) and \(\mathrm{imag}(\cdot)\) correspond to the real and imaginary part of the complex number, respectively. Finally, \((\cdot)^{*}\) represents the conjugate of the complex number.
# Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
## 3 Methods
### Evaluation
#### 3.6.2 Dataset  
The historical data of CSPs is collected from _TopCoder_ leveraging the Rest API ([[https://tcapi.docs.apiary.io](https://tcapi.docs.apiary.io)]([https://tcapi.docs.apiary.io](https://tcapi.docs.apiary.io)), accessed on 15 March 2023). The obtained attributes include the project name, project ID, project type, detailed requirements, technologies, platforms, and prize of projects until July 2018. The extracted data are then saved in a database for further analysis. Notably, the mentioned attributes are selected based on the extensive experiment. The other attributes, i.e., developer name, do not contribute to increasing the performance of the proposed approach. Additionally, neglecting attributes such as project ID, project type, and prize money results in a performance decrease of 0.09% in the proposed approach. In contrast, excluding attributes (i.e., technologies, and platform) decreases the performance of the proposed approach by greater than 2%. However, the attribute (detailed requirements) significantly contributes to the success prediction of crowdsourced projects.  
The dataset overview is presented in Table 1. Statistics of Table 1 shows that the dataset contains _16,894_ records. Each sample in the dataset has a status of either _unsuccessful_ or _successful_. Out of 16,894 projects, 14,465 (85.61%) are successful and 2429 (14.39%) are unsuccessful. Moreover, the beanplot (Figure 2) shows the minimum and maximum words (\(w_{n}\)) in any project detail (\(d\)) from the dataset. The status attribute of a project obtained from the TopCoder specifies whether it is completed or not. However, since the status can have different values, such as completed, canceled-winner, unresponsive, and canceled-failed screening, we convert this multi-class classification into a binary classification. Specifically, we label completed projects with accepted submissions as _positive_, while completed projects with rejected submissions are labeled as _negative_. Projects with incomplete submissions, undefined status attributes, empty requirements attributes, or references to other web links are excluded from the analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No. of CSPs** & **16,894** \\ \hline Successful CSPs & 14,465 (85.61\%) \\ Unsuccessful CSPs & 2429 (14.39\%) \\ CSPs Classification & 2 \\ Minimum Words in CSPs Text & 1 \\ Maximum Words in CSPs Text & 3214 \\ Duration of Projects & Upto July 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CCSD dataset statistics.
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
## 3 Methodology
### Method of Estimating Critical Latency
#### 3.1.1 Formulation of Estimation  
Critical latency \(\Gamma\) can be written using the turning time \(t_{\text{SM}}\) between the turn start Point S and collision point M and the turn time correction value.  
\[\Gamma=t_{\text{SM}}+t_{corr}. \tag{1}\]  
\(t_{\text{SM}}\) can be represented as follows:  
\[t_{\text{SM}}=(R\theta_{\text{SM}})/v_{\text{OS}}, \tag{2}\]where \(R\) represents the radius that can be calculated from the observed turning circle, \(v_{\text{OS}}\) is the speed (knots) given to OS. In addition, \(\theta_{\text{SM}}\) is the central angle of S-M, which can be represented as follows:  
\[\theta_{\text{SM}}=\pi-\arctan(y_{\text{M}}/x_{\text{M}}), \tag{3}\]  
where \(x_{\text{M}}\) and \(y_{\text{M}}\) are \(x\)-axis values and \(y\)-axis values of Point M, respectively, and the designated position of M can be described as follows:  
\[M(x_{\text{M}},y_{\text{M}})=\left\{\begin{array}{l}x_{\text{M}}=R\ \cos(\theta_{ \text{TS}})\\ y_{\text{M}}=R\ \sin(\theta_{\text{TS}})\end{array}\right., \tag{4}\]  
where \(\theta_{\text{TS}}\) is the bearing perpendicular to TS' heading \(\phi_{\text{TS}}\) at Point M, \(\theta_{\text{TS}}=(\phi_{\text{TS}}+270)\big{(}\frac{\pi}{180}\big{)}\), \(S\) represents the turn start point given as follows:  
\[S(x_{S},y_{S})=\left\{\begin{array}{l}x_{S}=x_{\text{TC}}-R\\ y_{S}=y_{\text{TC}}\end{array}\right., \tag{5}\]  
where \(x_{\text{TC}}\) and \(y_{\text{TC}}\) are \(x\)-axis values and \(y\)-axis values of the turning circle TC position, respectively, relative to the turning angle \(\theta_{\text{TC}}(0\leq\theta_{\text{TC}}\leq 2\pi)\) that changes with equal angle, and the TC position can be represented as follows:  
\[\text{TC}(x_{\text{TC}},y_{\text{TC}})=\left\{\begin{array}{l}x=R\cos( \theta_{\text{TC}})\\ y=R\ \sin(\theta_{\text{TC}})\end{array}\right.. \tag{6}\]  
Next, \(t_{corr}\) is represented relative to \(\theta_{\text{SM}}\) as follows:  
\[t_{corr}=\sum_{\theta=0}^{\theta_{\text{SM}}}\text{ROT}(\theta_{\text{TC}}), \tag{7}\]  
where \(\text{ROT}(\theta_{\text{TC}})\) is ROT (Rate of Turn) (sec/deg) relative to \(\theta_{\text{TC}}\). ROT is used to correct the nonlinear turning speed and can be derived from the OS trajectory measured from the experiment.  
In addition, Point C, where the planned routes of OS and TS meet each other, using S-M's central angle \(\theta_{\text{C}}=(\phi_{\text{TS}}/2)(\pi/180)\), can be explained as follows:  
\[C(x_{C},\ y_{C})=\left\{\begin{array}{l}x_{C}=x_{S}\\ y_{c}=R\ \tan(\theta_{C})\end{array}\right.. \tag{8}\]
# Predicting Human Location Using Correlated Movements
## 7 Evaluation Results and Discussion
### Effects of Time Slot Feature Selection  
Figure 8 compares the precision of the mobility prediction model with two different time slot features: un-grouped and grouped. For the un-grouped case, 32 time slots in a day are mapped into a 32-dimensional vector. Meanwhile, for the grouped case, one-hot encoding converts a time slot index into a vector in \(\mathbb{B}^{3}\) where each dimension indicates one of the three parts of a day.  
In addition, as shown in Figure 8, it is clear that grouping time slot feature achieves higher prediction accuracy than with un-grouped features. The results reflect that people tend to spend time with each other for a relatively long period. This becomes more understandable in environments like university campuses or companies, where people usually stay with their colleagues for a significant amount of time during the day. After work, they may enjoy leisure activities with friends in the evening. Another reason for the low performance with the un-grouped case may be the small number of data samples compared with the large input dimensions in the un-grouped case.
## 4 Discussion
### Limitations and Future Work  
Due to COVID-19, the experiment was conducted remotely to maintain social distancing. The games were designed to be played online, so a remote study was a natural extension. However, some observations during playing sessions may have been missed, which could have provided additional insights into participants' experiences. Another limitation is a possible bias in the data due to the differences in time played, which could have affected the results. Some players spent 10 min, while others spent 20 min, mainly because the sessions were supervised or unsupervised. In the unsupervised session, it could not be determined whether players explored all game features and played effectively for 10 min. The analysis between time and enjoyment showed a tendency to increase enjoyment as the participant played longer. However, as the time variable was not controlled, it would be interesting for future experiments to control it and explore whether it is a factor in increasing enjoyment.  
Follow-up work included improving the games based on the study results and releasing them to the crowd. Once they were available and freely accessible, another study was  
Figure 4: Visual overview of the relationships among games, GDEs, and IMI constructs. Quadrants represent IMI constructs. Circles depict game concepts, with size reflecting IMI construct values. Squares indicate GDEs that had an impact on each IMI construct. Visuals: Earth for _Agentes_, red avatar for _Tesoros_, and yellow car for _Anotatlon_.  
conducted. This study was less controlled than the one presented in this paper, as it only required informed consent, and the questionnaires were presented at two points (i.e., at the end of training and the end of one level). The goal was to monitor long-term player enjoyment and to continue collecting PoS tags. From a linguistic point of view, additional data analysis evaluated the accuracy of the annotations collected, and these results are reported in [30]. The study highlighted the effectiveness of using GWAPs to verify PoS tags and improve accuracy rates. It also showed that factors such as the education level, field of study, and geographical background of the GWAP players had a significant impact on the accuracy of PoS tagging. Obviously, a linguistically proficient annotator would provide more PoS tags per time unit than a GWAP player (with or without linguistic knowledge). However, in the long term, the scale of the corpus may make the tasks expensive. On the other hand, linguistic tasks such as PoS tagging can be tedious and may not appeal to participants unless they incorporate gamification. For these reasons, GWAP approaches based on the principle of crowdsourcing are promising, as they focus on scalability and enjoyment rather than the efficiency of one particular contributor.