esa-sceva/satcom-chunk-collection huggingface dataset:
# YOLOv5 DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs
## 2 Materials and Methods
### Performance Evaluation  
For pig posture detection, this study employed multiple indicators to evaluate the performance of the model, including intersection over union (IoU), precision, recall, average precision (AP), mean average precision (mAP), parameters (Params), and floating point operations (FLOPs). IoU is mainly used to measure the degree of overlap between predicted and manually annotated bounding boxes. Its value ranges from 0 to 1, with a value closer to 1 indicating that the predicted bounding box is closer to the real bounding box [43]. Precision quantifies the accuracy of model predictions by measuring the proportion of correct predictions among all detected targets. Recall measures the effectiveness of the model by indicating the proportion of correct predictions among all positive samples [44]. AP, which is defined as the integration of precision and recall, refers to the area under the P-R curve plotted with recall as the \(x\)-axis and precision as the \(y\)-axis [45], and mAP is obtained by averaging multiple categories of AP. In pig posture detection experiments, unless otherwise specified, the parameters are taken by default. AP represents the average accuracy calculated when the IoU is equal to 0.5, and mAP indicates the average accuracy calculated at intervals of 0.05 for IoU from 0.5 to 0.95. Params are indicators to measure the space complexity of the model [46]. FLOPs are used to measure the time complexity of models [47]. In experiments, GFLOPs are often used as the unit of measurement for FLOPs.
# An Approach to Improve the Resolution of DLP 3D Printing by Parallel Mechanism
## 2 Design Principle and Overall Scheme  
### Design Principle  
For the optical projection devices, the size and resolution of the projection images vary with the projected distance from the projection lens to the printed area. When the projection distance is fixed, the optimal projection size and resolution are also fixed. The common DLP photocuring 3D printing process adopts the cumulative curing molding with the fixed projection resolution. The simplified molding process is shown in Figure 1a,b. The projection distance from DMD to the molding liquid level is fixed, and the resolution of each mask pattern projected to the molding liquid level is the same. Assuming that the dimension of either direction of the projection planes X and Y is 500 mm and there are 1000 pixels along this direction, the dimension of each pixel is 500 \(\upmu\)m, which means that the minimum feature size is 500 \(\upmu\)m. To print features as small as possible basis on a certain molding size, the molding method with the adjustable resolution in the printing process is designed in this study. The forming process adopted is shown in Figure 1c,d. After printing the \(n+1\)th layer of large format molding, the \(n\)th layer need to improve the fabrication resolution. More specifically, the lens position of the projection device is adjusted and the projection device is moved in space to project the best projection resolution to the forming liquid surface. The optics are modified to focus the light into a smaller image to fabricate features smaller than 500 \(\upmu\)m.  
We used a low-cost consumer-level projector ASK M5(ASK, Fredrikstad, Norway), and the optical lens is placed in front of the projector to shorten the pro  
Figure 1: A comparison of fixed resolution and adjustable resolution molding process: (**a**) Fixed resolution molding process; (**b**) Build area and pixel size of each layer with fixed resolution; (**c**) Adjustable resolution molding process; (**d**) Build area and pixel size of each layer with adjustable resolution.  
sufficient light intensity to solidify the polymer. With the lens is installed, the optimal projection plane is 92 mm \(\times\) 69 mm before manual zoom, while the size of after zoom is 74 mm \(\times\) 56 mm. Affected by the uneven light intensity and the distortion of the edge of the projection frame, the effective molding frames are 70 mm \(\times\) 56 mm and 55 mm \(\times\) 44 mm, which are called large format and small format, respectively, in the experiment.
# RARZ: Ring-Zone Based Routing Protocol for Wireless Sensor Networks
## 2 Related Work
### Neuro-Fuzzy Approaches  
An optimized zone-based energy-efficient routing protocol (OZEEP) [27] was proposed to enhance the network lifetime by optimizing the clustering and cluster head selection mechanism using a genetic fuzzy system (GFS). A new approach to enhance the energy efficiency in the network during routing was proposed based on rendezvous routing protocol [28], which forms a rendezvous region in the center of the sensing field and constructs a tree to send the data to BS. This approach outperforms the well-known existing techniques in terms of energy efficiency, an end to end delay, network lifetime and throughput, etc.
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## 2 Research Methodology  
The research is divided into three parts, as shown in Figure 1. In the first part, a simulation of an existing MED-TVC desalination plant in Saudi Arabia is conducted using Aspen Plus(r). The results of the simulation are validated with the actual data of the existing desalination plant, and the plant's heat requirement is determined from the simulation. The required heat to run the desalination plant as well as other design parameters are used as an input in the DEEP program to find the levelized cost of produced water via nuclear energy or through fossil oil. Water costs from both heat sources are obtained where the most sensitive parameters that affect water production costs are considered. Lastly, the carbon emissions resulting from generating the required heat for the existing desalination plant from either nuclear energy, crude oil, or natural gas are addressed and compared.
## 6 Conclusions  
The \"training and information\" surveys are currently assisting in raising the knowledge of the dangers of radon gas exposure in dwelling situations. Indoor radon levels were tested in 100 homes on the Sorrentine peninsula in southern Italy. After data preprocessing, the radon concentrations ranged from 23 to 933 Bq/m\({}^{3}\) (70 measurement points). Indoor radon concentrations are primarily impacted by two factors: the region's potential radon and the type of building, both of which were confirmed in this study. Approximately 13% of the dwellings had radon quantities over the action threshold set by Italian legislation. The kriging map (Gini-based approach) was estimated using the combination of the geographical distribution of measurement points in the territory and radon survey data, thus enabling the estimation of predicted radon levels in the chosen location.  
Knowledge of radon dispersion in the environment can contribute significantly to individual radiation safety and prevention, and provide a solution to economic and/or environmental issues related to land use.  
In this framework, the proposed approach appears to be particularly efficient for the inference of common source features from indoor investigation surveys, where we can expect larger uncertainties. In this case, the single measurements, which may be affected by a large random uncertainty, are assigned lower weights.  
This may be a valuable tool in the planning of new buildings as dwellings and major infrastructure projects, depending on the usage of various building materials and the selection of low-radon-risk regions. In this case, preventive actions can be applied.  
In the case of existing facilities, there are a number of mitigation techniques that can be implemented to limit radon exposure indoors. These interventions can be used separately or in combination to limit the primary contribution, which is generally the soil, and exposure from other radon sources (such as building materials). These interventions include substantial natural and forced ventilation, the sealing of the main access ways to radon, the depressurization of the subsoil, and the use of inexpensive sums [43; 44; 45]. These modifications must be tailored to each building, taking into account its architecture, the nature of the subsoil on which it is constructed, and its construction characteristics.  
Conceptualization, F.L.; methodology, F.L. and M.Q.; software, F.L.; validation, F.L.; formal analysis, F.L., I.O.-N. and M.Q.; investigation, F.L.; resources, F.L. and M.Q.; data curation, F.L. and I.O-N.; writing--original draft preparation, F.L., I.O.-N. and M.Q.; writing--review and editing, F.L., I.O.-N. and M.Q.; visualization, F.L., I.O.-N. and M.Q.; supervision, M.Q.; project administration, M.Q.; funding acquisition, F.L. and M.Q. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the University of Naples Federico II, the Italian Ministry of University and Research (MIUR), within the program PON R&I 2014-2020-Attraction and International Mobility (AIM)-CUP E61G19000020001.
## 7 Model Uncertainty Quantification of the EBM vs. IDA Analyses  
To quantitatively evaluate the performance of the EBM, the ratios \(K_{EBM}\) of the resistances of the EBM to those from the incremental dynamic analyses (IDA) are calculated according to Equation (2), as well as for the ratios of the displacements. The mean and standard deviation of the ratios \(K_{EBM}\) for Cases A, B, and C separately and as a total for the three cases considered are shown in Table 5.  
Histograms and PDFs of the ratios with regard to the resistances are presented in Figure 21. Figure 21a-c show ratios in terms of the resistances for Cases A, B, and C, respectively, in which lognormal distributions LN(0.97, 0.01), LN(0.99, 0.01), and LN(0.99, 0.01) are found to represent the histograms. Figure 21d presents the ratios of the ultimate capacities of all three cases as a whole (case  
Histograms and PDFs of the ratios in terms of the corresponding displacements for the three cases are presented in Figure 22. Figure 22a-d show the ratios of the displacements, respectively, for Cases A, B, C, and 'all', in which lognormal distributions LN(1.05, 0.08), LN(1.08, 0.08), LN(1.06,0.08), and LN(1.07,0.08) are fitted for the cases.  
According to these results, a great performance has been found for the model uncertainties involved in the application of the EBM instead of the more complex IDA, especially for the prediction of the resistance, i.e., resulting in a small bias and small standard deviation. The model uncertainty models can be adopted in a probabilistic design situation, for instance, as follows.  
Figure 21: Histograms and PDFs for the ratios of the EBM/IDA regarding resistances: (**a**) Case A; (**b**) Case B; (**c**) Case C; and (**d**) all three cases.  
Figure 22: Histograms and PDFs of the ratios of the EBM/IDA for displacements: (**a**) Case A; (**b**) Case B; (**c**) Case C; and (**d**) all three cases.  
1. Perform stochastic static non-linear analyses with specified random variables and column removal scenarios.
2. Apply the EBM (Equation (1)) to calculate dynamic ultimate capacities \(R\) according to the static capacity curves from step 1.
3. Evaluate the failure probability \(P_{f}\) through the following limit state function \(Z\): \[Z=K_{R}\cdot R/K_{EBM}-K_{L}\cdot L\] (5) where \(K_{R}\) and \(K_{L}\) are the resistance and load model uncertainties, respectively, [66]; \(L\) is the load effect; and \(K_{EBM}\) is the additional model uncertainty relating to the application of the EBM instead of the IDA (Table 5).  
Although the performance of the EBM is verified and its model uncertainty is calculated herein, it must be emphasized that the results are based on a particular RC frame and that further investigations might be considered in a similar manner as illustrated in this contribution before coming to a more generally applicable proposal for the model uncertainty involved.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
###### Abstract  
This review presents an overview of advanced air mobility broadband noise (BBN) prediction and control techniques, highlighting significant advancements in various prediction models. Methods such as the semi-empirical Brooks-Pope-Marcolini (BPM) model, analytical Amiet model, and time-domain models based on the FW-H equation have been extensively studied. Machine learning (ML) shows promise in BBN prediction but requires extensive data training and application to noise source mechanisms. Passive control methods, such as leading and trailing edge serrations and blade tip designs, have been partially successful but often compromise the aerodynamic performance. Active control methods, like suction and blowing control, trim adjustments, and dielectric barrier discharge (DBD) plasma actuators, show great potential, with the latter two being particularly effective for reducing BBN in thin propeller structures. Overall, while progress has been made in understanding and predicting BBN, further research is needed to refine these methods and develop comprehensive noise control strategies. These advancements hold significant promise for effective and efficient noise mitigation in future AAM vehicles.  
a +
Footnote †: journal: _Sciencedence_  
_Applied_  
_sciences_  
**Citation:** [PERSON]; [PERSON].  
Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review. _Appl. Sci._**2024**, _14_, 8455. [[https://doi.org/10.3390/app14188455](https://doi.org/10.3390/app14188455)]([https://doi.org/10.3390/app14188455](https://doi.org/10.3390/app14188455))
# A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
## 4 Experimental Evaluation
### Experimental Setup
The generated vertex labels less than 0 were all set to 1. **Comparison Method:** We compare the proposed SSiGraM algorithm with the GraMi[12] and we use the \(GRAMI\_UNDIRECTED\_SUBGRAPHS\) version of GraMi provided by the authors. **Running Environment:** All the experiments with SSiGraM are conducted on Apache Spark (version 1.6.1) deployed on Apache Hadoop YARN (version 2.7.1). The total executors is set to 20 with 6 GB memory and 1 core running at 2.4 GHz for each executor. The memory of driver program is also 6 GB and max results 2 GB. Thus, the total memory allocated from YARN is 128 GB. For the sake of fairness, GraMi is conducted on a Linux (Ubuntu 14.04) machine running at 2.4 GHz with 128 GB RAM. **Performance Metrics:** The support threshold \(\tau\) is the key evaluation metric as it determines whether a subgraph is frequent. Decreasing \(\tau\) results in an exponential increase in the number of possible candidates and thus exponential decrease in the performance of the mining algorithms. For a given time budget, an efficient algorithm should be able to solve mining problems for low \(\tau\) values. When \(\tau\) is given, efficiency is determined by the running time. In addition, we also give the total subgraphs each algorithm identified under each \(\tau\) value, proving the correctness of the SSiGraM algorithm.
# Kalman Filter-Based Differential Privacy Federated Learning Method
###### Abstract  
The data privacy leakage problem of federated learning has attracted widespread attention. Using differential privacy can protect the data privacy of each node in the federated learning, but adding noise to the model parameters will reduce the accuracy and convergence efficiency of the model. A Kalman Filter-based Differential Privacy Federated Learning Method (KDP-FL) has been proposed to solve this problem, which reduces the impact of the noise added on the model by Kalman filtering. Furthermore, the effectiveness of the proposed method is verified in the case of both Non-IID and IID data distributions. The experiments show that the accuracy of the proposed method is improved by 0.3-4.5% compared to differential privacy federated learning.  
federated learning; privacy protection; differential privacy; Kalman filter
###### Abstract  
The complex and unique topography of karst regions highlights the weaknesses of traditional hydrological models which fail to fully generalize them. The successive proposals of standard artificial intelligence (AI) models, pre-processing techniques, and post-processing methods have provided new opportunities to enhance the accuracy of runoff prediction in karst areas. In this study, the BP neural network model and the Elman neural network model were used for runoff prediction. Then, the performance of four coupled models--formed by combining two AI pre-processing techniques, Empirical Modal Decomposition (EMD) and Ensemble Empirical Modal Decomposition (EEMD), with the previously mentioned AI models--was investigated. Finally, the accuracy of triple-coupled models, formed by applying the post-processing method of quantile mapping (QM) to the previous coupled models, was estimated. The Nash-Sutcliffe efficiency (NSE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the peak percentage of threshold statistics (PPTS) were selected to evaluate and analyze the forecasting results of the above models. The results demonstrated that the BP model had the best prediction effect of the standard AI models, the coupled forecasting models had better prediction accuracy than the standard AI models, and the triple-coupled QM-EMD-Elman model had the best forecasting effect with an NSE value of 0.73, MAPE value of 0.75, RMSE value of 34.60, and PPTS value of 2.36.  
standard artificial intelligence model; pre-processing technique; quantile mapping method; monthly runoff prediction; karst watershed
## 4 Image Analyses
### Image Analyses without Solar Shading by Tree Leaves above the Pond Surface
#### 4.1.8 Results and Discussion of Image Analyses
In the fitting, as shown in Figure 16, the theory reproduced the measured RGB values well. The simulation results shown in Figure 15 show the intensity ratio of pond bottom irregular reflection: density fluctuation scattering by water: Mie scattering was 1: 4: 10, which was significantly different from the ratio reported by [PERSON] et al. (2016); [PERSON] et al. (2016), where the contribution of pond bottom irregular reflection was large. Therefore, the potent mechanisms that contributed to the coloration of Aoike Pond were concluded to be the density fluctuation scattering by water and the white Mie scattering by suspended solids, in this study. Before and after these two types of scatterings, along the optical paths inside the pond, absorption by water of long-wavelength visible light, i.e., red light, should occur and thus blue light remains and causes the blue coloration. coloration by this mechanism in essence generates the blue color of water. In addition, for the case of Ao-ike Pond, there is significant contribution of density fluctuation scattering. In this mechanism, blue light scatters more strongly than green or red light by a nature of water. The coloration caused by this mechanism emphasizes blue and the water body tends to exhibit a deep blue color. An example of the density fluctuation scattering by water for Ao-ike Pond is shown in Figure 15d. It should be noted that this deep blue color of Ao-ike Pond originates from both blue coloration by absorption of red light by water along the optical path before and after the scattering inside the pond and blue coloration by the wavelength-dependent scattered light by water. If water bodies contain a colorless and certain amount of suspended solids, the weak coloration effects by density fluctuation scattering of water is usually overridden by Mie scattering colorations by suspended solids which yield blue colorations as results of light absorptions by water. Coloration by this mechanism tends to exhibit a mixture of blue and white in the case where suspended solids in the water body are colorless, as shown in Figure 15e. In the case of Ao-ike Pond, due to a low concentration of suspended solids within highly transparent water body, it was concluded that the pond water gives the coloration by density fluctuation of water as well as that by Mie scattering of suspended solids. This blue coloration can be considered to occur from the nature of water. Figure 16: Relationship between observed and theoretical RGB values of the model fitting in image analyses (n = 245). (**a**) R values, (**b**) G values and (**c**) B values. In this study, we judged that, by introducing the observation device theory, the contribution of three coloration mechanisms could be examined more accurately than by the theories reported by [PERSON] et al. [16; 17].
## 3 Experiment and Model Validation
### Model Validation  
#### 3.2.1 The Relationship between the Volume Fraction and Electrical Conductivity  
For the purpose of studying whether there is a functional relationship between the VOF of salt solution and EC, salt solutions with different concentrations (C) were prepared at the experimental temperature and their conductivity was measured synchronously. Figure 5 shows a linear relationship between EC, C, and the VOF of the salt solution. The point which the value of VOF is 1 corresponds to the conductivity of the original solution.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Comparison and Analysis  
For a detailed discussion on the impact of occlusion on object pose tracking, this section sets up two sets of comparative experiments for analysis and validation.
## 3 Results
### The Seven-Landmark Model
\begin{table}
\begin{tabular}{l l l l l} \hline
**Models** & **GBM** & **SVM** & **Tree** & **NN** \\ \hline
7 Im at 35\%—shape & 79\% (0.72) & & & & \\
7 Im at 35\%—form & 65.8\% (0.53) & & & & \\ \hline
7 Im at 50\%—shape & & & & 51.3\% (0.33) \\
7 Im at 50\%—form & 60.5\% (0.46) & 54.8\% (0.39) & 56.4\% (0.39) & \\ \hline
7 Im at 65\%—shape & 59\% (0.45) & & 56.4\% (0.41) & \\
7 Im at 65\%—form & 66.7\% (0.54) & & 69.2\% (0.57) & 64.1\% (0.49) \\ \hline
7 Im at U section—shape & & & & \\
7 Im at U section—form & 56.1\% (0.41) & & & \\ \hline
10 smlm at 50\%—shape & 50\% (0.31) & & & \\
10 smlm at 50\%—form & 53.9\% (0.36) & 53.9\% (0.36) & 56.4\% (0.39) & \\ \hline
25 smlm at 50\%—shape & & & & \\
25 smlm at 50\%—form & 59\% (0.45) & & 64.1\% (0.5) & \\ \hline
50 smlm at 50\%—shape & & & & \\
50 smlm at 50\%—form & 64.1\% (0.51) & & 59\% (0.42) & \\ \hline
13 smlm at U section—shape & & & & \\
13 smlm at U section—form & 57.1\% (0.41) & & 52.4\% (0.35) & \\ \hline
23 smlm at U section—shape & & & & 52.4\% (0.36) \\
23 smlm at U section—form & 56.1\% (0.39) & 57.8\% (0.42) & & 52.4\% (0.34) \\ \hline
33 smlm at U section—shape & & & & \\
33 smlm at U section—form & & & & \\
10 EFA at 50\%—shape & & & & \\
10 EFA at 50\%—form & & & & 53.9\% (0.37) & \\ \hline
25 EFA at 50\%—shape & & & & \\
25 EFA at 50\%—form & & & & \\
50 EFA at 50\%—form & 51.3\% (0.33) & 51.3\% (0.32) & & 53.9\% (0.36) \\ \hline
13 EFA at 50\%—shape & 51.3\% (0.31) & & & \\
13 EFA at 50\%—form & 52.6\% (0.35) & & 59\% (0.42) & 53.9\% (0.36) \\ \hline
23 EFA at 50\%—shape & 51.3\% (0.31) & & & \\
23 EFA at 50\%—form & 53.9\% (0.36) & & 59\% (0.42) & 53.9\% (0.36) \\ \hline
33 EFA at 50\%—shape & 52.6\% (0.35) & & & \\
33 EFA at 50\%—form & 64.1\% (0.5) & & 59\% (0.43) & 56.4\% (0.39) \\ \hline
13 EFA at U section—shape & & & & \\
13 EFA at U section—form & 56.1\% (0.39) & & 50\% (0.31) & \\ \hline
23 EFA at U section—shape & & & & 50\% (0.32) & \\
33 EFA at U section—shape & & & & \\
33 EFA at U section—form & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy percentages >50% and kappa values (in parentheses) obtained for the different landmark (lm) and semilandmark (smlm) models generated for the present study, using gradient boosted machine (GBM), Support Vector Machine (SVM), Tree, and Neural Network (NN) algorithms. data obtained on the seven-landmark model seems plausible, only few ML algorithms are able to accurately classify more than 50% of the carnivore sample (Table 3), and only one of the tested GBM models on the seven-landmark models at 35% score length surpasses the 75% of accuracy.