esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[34] presented a new design of a floating platform for wind turbines. They combined the characteristics of semi-submersible and spar, and conducted the numerical hydrodynamic simulation by FAST. The results showed that the new model makes an effective reduction of the motions compared with the OC3 spar. However, the modifications in the above research works to the original OC3-Hywind spar platform are still relatively large. Thus, they will increase the difficulty of the floating platform construction as well as the costs of construction and maintenance. Adding clump masses on the mooring line is an effective way to improve the wave resistance ability of a spar buoy-type floating offshore wind turbine. [PERSON] et al. [35] proposed a hybrid mooring system with several clump weights and buoys, in which the Morison's Equation was applied to calculate the hydrodynamic loads. In addition, the optimization of the position and volume of the buoys was carried out based on the examination of the tension of mooring line. [PERSON] [36] studied the effects from the size of the clump weight and the position of the clump weight, in which the generalized Morison's equation is used. Furthermore, the size and position of the clump masses have been optimized by examining the tension of the mooring line. In order to model the interaction between the waves and the structures in a more accurate way, potential flow theory is adopted in the present research. The fatigue loads of the mooring line are also examined. The optimization of the location of the clump mass is based on the examination of the motion of the floating platform. A detailed comparison between the present research and that by [PERSON] et al. [35] and [PERSON] [36] is given in Table 1.  
In this paper, the structure of the OC3-Hywind spar platform remains unchanged. Only several clump masses are added at the mooring system. Each clump mass's weight is only one-tenth that of the attached mooring line. This is not likely to increase the difficulty of the platform construction very much. Furthermore, the position of the clump masses is varied and the response of the models at three regular waves and three irregular waves is calculated through the in-house code. Section 2 will introduce the numerical theories adopted in our in-house code. After that, Section 3 will present the configurations of OC3 models with clump masses (OC3 CM) and the load cases. Then, Section 4 will discuss the wind-wave-induced motion and the stability of the proposed OC3 CM model and compare it with the original OC3-Hywind spar. Finally, Section 5 will present the conclusions and future work.
## 5 Numerical Examples
### Example 1  
In this example, we find a minimal-norm and least-squares solution of the following consistent equation:  
\[\left[\begin{array}{ccc}1&2&3&-1\\ 3&2&1&-1\\ 2&3&1&1\end{array}\right]\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{array}\right]=\left[\begin{array}{c}1\\ 1\\ 1\end{array}\right]. \tag{129}\]  
Although Example 1 is simple, we use it to test the DOS and DOA for finding the minimal-norm solution for a consistent system.  
First we apply the DOS with \(m=2\) to solve this problem, where we can find a quite accurate solution of \(x_{1}=1.48176\times 10^{-1}\), \(x_{2}=1.92567\times 10^{-1}\), \(x_{3}=1.48141\times 10^{-1}\), and \(x_{4}=-2.22689\times 10^{-2}\), for which the three residual errors are \(r_{1}=2.012\times 10^{-6}\), \(r_{2}=7.125\times 10^{-5}\) and \(r_{3}=-7.51756\times 10^{-5}\), respectively.  
Although the DOS is an accurate solution with an error on the order of \(10^{-5}\), the accuracy can be improved by the DOA. In the application of the iterative DOA, initial guesses are given by \(x_{1}^{0}=x_{2}^{0}=x_{3}^{0}=x_{4}^{0}=c_{0}\) for some constant. Then, the DOA with \(m=1\) solves this problem with \(c_{0}=0\) and \(\varepsilon=10^{-12}\). As shown in Figure 1a, the DOA converges very fast in only seven steps. Furthermore, we can find very accurate solutions of \(x_{1}=1.48148\times 10^{-1}\), \(x_{2}=1.925926\times 10^{-1}\), \(x_{3}=1.48148\times 10^{-1}\), and \(x_{4}=-2.22222\times 10^{-2}\); the norm of \(\mathbf{x}\) is \(\|\mathbf{x}\|=0.28545\). The three residual errors are \(r_{1}=-5.63993\times 10^{-14}\), \(r_{2}=-5.61773\times 10^{-14}\) and \(r_{3}=-5.63993\times 10^{-14}\), respectively.  
On the other hand, if we take \(c_{0}=1\), \(r_{1}=5.6\times 10^{-13}\), \(r_{2}=5.59\times 10^{-13}\) and \(r_{3}=8.47\times 10^{-13}\) are obtained, which show that the solution \((x_{1},x_{2},x_{3},x_{4})=(0.4815,-0.2741,0.4815,0.3778)\) is accurate. However, the norm \(\|\mathbf{x}\|=0.82552\) is not the minimal one. Therefore, the correct one for the minimal-norm solution is \(c_{0}=0\). Unless otherwise specified, we will take the initial guess by \(\mathbf{x}_{0}=\mathbf{0}\) for most problems computed below.
## 3 Materials and Methods
### Execution Monitoring and Dataset Creation
#### 3.1.1 Execution Monitoring within the Spark Unified Memory Model  
As far as execution monitoring is concerned, a brief overview of the Spark unified memory model is required. In order to avoid OOM errors, Spark uses up to 90% of the heap memory, which is divided into three categories: reserved memory (300 MB), used to store Spark internal objects; user memory (40% of heap memory), used to store data structures and RDDs computed during transformations and actions; Spark memory (60% of heap memory), divided into execution and storage. The former refers to that used for computation during shuffle, join, sort, and aggregation processes, while the latter is used for caching RDDs. It is worth noting that, when no execution memory is used, storage can acquire all the available memory and vice versa. However, storage may not evict execution due to complexities in implementation, while stored data blocks are evicted from main memory according to a Least Recently Used (LRU) strategy.  
The occupancy of storage memory relies on the persistence operations performed natively by the algorithms. Table 2 reports some examples of data caching implemented in the aforementioned MLlib algorithms. In particular, the cache() call corresponds to persist(StorageLevel.MEMORY_AND_DISK), where MEMORY_AND_DISK is the default storage level used for the recent API based on DataFrames.  
According to the Spark unified memory model, the execution monitoring was done via the Spark REST APIs, which expose executor-level performance metrics, collected in a JSON file, including peak occupancy for both execution and storage memory along with execution time.
## 5 Results and Discussion
### Threats to Validity
#### 5.3.2 Construct Validity  
Threats to construct validity concern the relation between the theory and the observations. One threat to construct validity in this experiment was the evaluation metrics used to evaluate the prediction performance. The use of one evaluation measurement raises the risk of bias and contributes to misleading outcomes. To mitigate this threat, we employed three evaluation measures: one threshold-dependent measure (F-measure) and two threshold-independent measures (AUC and Brier) so they could be checked against each other. Moreover, we selected the commonly used measures found in the literature in software defect prediction, so they could be compared with other studies. The selection of the seven tree-based ensembles was the another threat to construct validity that might have led to bias. We mitigate this risk by combining well-known classification techniques (e.g., AdaBoost and random forests) with new techniques that have yet to be thoroughly examined in this area (e.g., extra trees and CatBoost). The final set of classifiers selected in our study included a wide variety of homogeneous ensembles from several categories (i.e., bagging and boosting).
## 3 The Proposed Algorithm  
### Overview  
Figure 2 describes the proposed algorithm. Each facet is extracted from a 3D triangle mesh. The index of each facet is then used with the key value \(\mathbf{K}\) to generate an interpolating vector corresponding to that facet. The key value \(\mathbf{K}\) is created from a user's key input by a key hashing function. The interpolating vector is then used to calculate the curvature coefficients of a spline curve of degree 2 in 3D space. These curvature coefficients of degree 2 are then used together with three vertices of facet to calculate three control points of the interpolated spline curve of degree 2 in 3D space. Next, the interpolating vector, curvature coefficients, and three control points of the spline curve are encrypted by the key value \(\mathbf{K}\). The encrypted curvature coefficients and the encrypted control points are used in an inverse interpolation process to compute three new vertices. These vertices will be distorted by the encrypted interpolating vector via the geometric distortion process in order to generate the encrypted facet. The encrypted facets are combined to obtain the encrypted 3D triangle mesh.  
As mentioned above, a 3D triangle mesh contains a set of facets. Each facet includes three vertices. Each vertex is presented by x, y, and z coordinates. We assume that a 3D triangle mesh \(\mathbf{M}=\{\mathbf{F}_{i}|i\in[1,|\mathbf{M}|\}\) with \(|\mathbf{M}|\) the number of facets; \(\mathbf{F}_{i}=\{v_{i,1},v_{i,2},v_{i,3}\) and \(\mathbf{n}_{i}\}\) is the \(i^{th}\) facet with three vertices \(\{v_{i,1},v_{i,2},v_{i,3}\}\) and the normal vector \(\mathbf{n}_{i}(nx_{i},ny_{i},nz_{i})\). Due to the fact that the normal  
Figure 1: Structure of a 3D triangle mesh.  
Figure 2: The proposed algorithm.  
vector of a facet does not determine the shape of 3D triangle mesh, we briefly consider the facet \(\mathbf{F}_{i}\) includes three vertices as Equation (1):  
\[\begin{array}{ll}\mathbf{F}_{i}&=\{v_{i,1},v_{i,2},v_{i,3}|i\in[1,|\mathbf{M}| ]\}\\ &=\{v_{i,j}|i\in[1,|\mathbf{M}|],j\in[1,3]\}\text{ with }v_{i,j}=\big{(}x_{i,j},y_{i,j},z_{i,j}\big{)}.\end{array} \tag{1}\]  
For brevity, main notations are defined as follows: \(\mathbf{T}_{i}=\{t_{i,j}|j\in[1,3]\}\) is the interpolating vector corresponding to \(\mathbf{F}_{i}\); \(\mathbf{C}_{i}=\{c_{i,k}|k\in[1,\,3]\}\) is three control points of the facet \(\mathbf{F}_{i}\); \(\mathbf{A}_{i}\) is the constructed matrix from the curvature coefficients of the interpolated spline curve corresponding to \(\mathbf{F}_{i}\); \(\mathbf{E}_{i}=\{\epsilon_{i,j}|i\in[1,|\mathbf{M}|],j\in[1,3]\}\) is the encrypted facet. Finally, \(G_{D}(.)\) and \(E_{F}(.)\) are the distortion function and the encryption function, respectively.
## 2 IoV-Based Architecture for SC Waste Management Strategies
Despite the notable efficacies of multiple mobile collectors, especially in relation to the issues of scalability and latency, there are several challenges associated with the scheme that are related to costs and complexity of the network, thereby necessitating a tradeoff between network cost and delays. We consider the need for a timely data collection and transmission before the data loses its significance and becomes useless and therefore propose a novel energy-efficient swarm intelligence IoV-based approach for opportunistic data collection and traffic engineering for SC waste management. Figure 2 shows an IoV-based network communication model, while Figure 3 shows a working model of the proposed approach for data collection in an LS-WSN environment. The first challenge is partitioning the entire network into multiple clusters and groups prior to data collection. To this end, we adopted the network partitioning method presented in [11]. The other challenge is how to find the optimal data collection points (DCPs) for the DCVs and subsequently assign some DCPs to each DCV in a manner that tends to reduce the round-trip time for the \(n\) number of DCVs and consequently minimize their total movement energy consumption. Otherwise, some DCVs will have more DCPs to visit than others, resulting in high movement energy consumption as a result of longer tour duration. This work is focused on a network with predetermined locations of smart bins or cluster heads (CHs) and optimal DCPs such that the DCVs do not necessarily need to traverse all the CHs but can visit certain optimal locations for data gathering. Figure 2: IoV-based network model showing V2X technologies.
## 5 Statistical Analysis of the Protocols Based on Subsets of Responses and ECC  
Practical issues are expected in the implementation of the methods described above in Section 4 since the noise injection could also generate erroneous bits in the cryptographic keys. One solution is to tolerate an approximate matching between the responses of the orderly subset generated during enrollment and the responses generated during verification. However, if the threshold of acceptance is too high, the probability of having two randomly chosen responses matching can also be too high. The term commonly used to describe such unwelcome matches is \"collision\" [61; 62]. In order to optimize the protocols, we developed a predictive modeling of these collisions. We also developed error management schemes able to process small residual erratic bits.
## 4 Experiments and Results
### Data Pre-Processing  
For the STP computation, only those passages labelled as RTN should be considered, thus, the audio clips labelled in one of the 28 ANE identified subcategories in both environments, as well those others labelled as complex passages, where a mixture of different sound sources (e.g., diverse ANEs together with RTN as background), were removed [15].  
The selection threshold \(\gamma\) has been set to \(\gamma=40\%\), i.e., a \(T=20\)-min audio recording was used for the corresponding STP computation if it contained at least 40% of RTN. This value was selected as a trade-off between representativeness and coverage, by ensuring the computation of mean hourly spectra of RTN considering more than 10% of the total time and assuming that less than 3% of the collected data is discarded, respectively.  
Moreover, other sensed 60-min periods were also discarded due to an episode of heavy rain between 13:00 and 15:00 during the weekend in the suburban scenario. After inspection of the corresponding audio recordings, it was found that a significant number of RTN periods contained residuals of rain sound as well as their STPs presenting a significant increase in high frequency energy STPs due to the contact of tyres with wet pavement. Thus, audio data from these 1-h periods were discarded because the rain episode could have potentially biased the corresponding clustering of the STPs.  
Figure 2: Map of the acoustic sensor locations of the WASN deployed across A90 circular highway surrounding the city of Rome (suburban environment), adapted from Google Maps\({}^{\text{\textregistered}}\).  
Table 1 shows the discarded 1-h periods from both suburban and urban scenarios, respectively, and provides information on the sensor name, the acoustic environment, the sensed day and the considered criterion for their identification. It can be observed that eleven periods were found in the suburban environment during the weekend day, four due to the _selection threshold_ criteria and seven due to a rain episode. Otherwise, up to eight 1-h periods were discarded during the weekday and only one on the weekend in the urban scenario, as they did not meet the _selection threshold_ criterion.
## 2 Materials and Methods
### Machine Learning Based FSO Research Background  
An important challenge in using machine learning techniques for scientific applications is to be able to use domain knowledge and data-driven algorithms in synergy. Machine learning algorithms have already been exploited by the optical communications community and, in particular, the free space optical communications community. For example, in [30], a data-driven fiber channel deep learning (DL) modeling method was introduced in an optical communication system. Specifically, a bidirectional long short-term memory was selected to perform fiber channel modeling for on-off keying (OOK) and pulse amplitude modulation 4 signals. A deep learning based atmospheric turbulence compensation method to correct the distorted vortex beam and improve the performance of orbit angular momentum (OAM) multiplexing communication is presented in [31]. FSO related channel modeling is studied using DL techniques in [32] for all turbulence strength regimes, and results published therein indicate that DL can provide performance that is reasonably close to the perfect channel estimation scheme. The ability to mitigate the negative effects of atmospheric turbulence for FSO systems performance is studied in [33; 34; 35] using ANN, generative machine learning (GML) and convolutional neural networks (CNN) through simulated and experimentally obtained data. ML techniques using DT, RF and ANN for \(C_{n}{}^{2}\) estimation, is presented in [36; 37] and compared against macroscopic meteorological parameters. Other ML algorithms have also been used to predict RSSI for a hybrid RF/FSO detector by developing both regression and classification models [38; 39]. Finally, an overview of the, as expected, next generation of the ANN, the so-called optical neural networks (ONNs) and previous studies on the field are reviewed in [40]. The novelty of the current study lies in the comparison of various classical ML algorithms that are presented for the first time in predicting RSSI measurements, especially for the particular domain of interest, Piraeus, Greece. The contribution of this paper is that it a) provides an extension of previous related works in terms of the utilized ML algorithms (i.e., KNN and ANN), which both performed sufficiently enough and b) provides a unique experimental data analysis in terms of the terrain of the link (i.e., maritime), which exhibits different characteristics from a terrestrial one.
# Study on Influencing Factors of Helicopter Brownout Evolution Based on CFD-DEM
## 4 Coupling Simulation Analysis  
The above two blades prove the reliability of the simulation model, but due to the lack of corresponding fuselage, the classic helicopter UH60 was selected to analysis the brownout. The discrete element model and computational fluid dynamics model were used for coupling calculation to analyze the formation and evolution of brownout. The software used are EDEM and FLUENT. The fluid solver is based on RANS with the \(SST\)\(k-\omega\) turbulence model. The spatial discretization is second order upwind for momentum, turbulent kinetic energy and specific dissipation rate.  
The UH60 adopts three-stage twist and swept rotor blades, with the airfoil of SC1095 and SC1094R8. The tip Mach number is 0.63, the collective pitch is 9\({}^{\circ}\), the blade radius is 8200 mm, chord length is 530 mm, the tip swept angle is 20\({}^{\circ}\) and the negative twist angle is 13\({}^{\circ}\)[20]. The model uses an unstructured grid with a total of 12 million. The time step size is the time taken for one degree per revolution of the blade tip.  
When the height is h/R = 0.52, the rotor ground effect flow field is calculated by using the sliding grid method. The time step size is the time required for each degree of blade rotation. The velocity and streamline distribution are shown in Figures 6 and 7. The x direction is the in-coming flow direction, and the z direction is the direction perpendicular to the ground.  
From the above figure it can be seen that there is obvious swirling air flows under the out-ward side of the blade, and that the air flows down from the tip of the blade and rolls  
Figure 6: X-direction and Z-direction velocity contours.  
Figure 7: Section velocity contours and streamline distribution.  
back over the blade when it reaches the ground. There are also two smaller raceways above the blade root, which are judged to be caused by the high-intensity vortex.  
In the process of the coupling simulation, the particles are simplified as spherical particles in the sand model, and the interaction between particles is solved by the Hertz Mindlin (no slip) model, and the interaction between particles and wall (including fuselage, blade and ground) is solved by the Hertz Mindlin with JKR (Johnson Kendall Roberts) contact model. The particles and the flow field exhibit two-way coupling. The flow field is solved for 0.5 s to achieve stability, then 5000 to 20,000 particles are added in the vicinity of the ground according to the time for calculation. The physical parameters of the particles are shown in Table 5[21].
## 3 Experiments and Analysis
### Comparison with Other Methods
#### 3.2.3 Comparison Experiment Prediction Results  
The comparison of prediction results for different models is shown in Figure 7. In order to more clearly differentiate the road segmentation results generated by the models, the road areas in the prediction results are marked in white, and the non-road areas in black. Furthermore, to highlight the differences in key regions, red bounding boxes are used for marking. The reference models exhibit varying degrees of misclassification, omission, and discontinuous segmentation in the road extraction task. In contrast, the CDFFNet model achieves more accurate identification of road targets at different scales, effectively capturing road edge information, and delivering significantly better prediction results compared to other models.  
Figure 5: Parameters (M) vs. mIoU.  
Figure 6: FPS vs. mIoU.