esa-sceva/satcom-chunk-collection huggingface dataset:
# Moving Surface Boundary-Layer Control on the Wake of Flow around a Square Cylinder
## 3 Results and Discussion
### Detailed Results and Analysis of \(D/L=0.3\) and \(h/D=1/4\)
#### 3.2.4 Wake Vortex Structure
When \(\theta=60^{\circ}\), the evolution process of the flow field around the square cylinder for various \(k\) values is similar to that of Figure 27b, and the vortex shedding mode presents an effect of 'drag reduction and lift increment' at this moment. The vortex near the square cylinder wake is stable, and the vortex street is formed farther downstream, when \(k=4\). Therefore, the fluctuation of the lift coefficient is significantly reduced, as well. Figure 28b shows that when the small circular cylinders do not rotate, the stable '2S' mode is formed in the square cylinder wake. The shear layer near square cylinder wake is stable and rolls up to a slightly farther downstream location when \(k=2\). At this time, the vortex has not been eliminated; however, the fluctuation value of the lift coefficient has decreased by 90% compared with that when \(k=0\). When \(k=4\), the mean of the lift coefficient is positive, and it increases with the increase in \(k\) value because of the overall downward deflection of the steady wake. At this point, the alternating shedding vortex in the wake of the square cylinder has been completely eliminated because the two small circular cylinders are both on the upper side of the square cylinder, and they rotate downstream together; this has a strong suction effect on the upper shear layer. Therefore, the shear layer that separated from lower side of the square cylinder is unable to curl into a vortex, and the wake presents an overall downward deflection, which leads to the increase in the mean of the lift coefficient. This is a newly discovered control mode, that is, when rotating cylinders are arranged on one side of the structure, the control effectiveness can also be achieved. For example, only one side of a long-span bridge deck is used for vehicle traffic, and the other side can be equipped with multiple rotating devices, such as vertical axis wind turbines placed horizontally, which can control the wake of the bridge deck while collecting wind energy.
# A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model
## 3 Experiments
### Experimental Settings
#### 3.1.1 Space Verification Experimental Settings  
For the space verification experiments, DAS, developed using OpenSim--an open source software system for biomechanical modeling [34]--and Matlab software are used to construct the human upper kinematic model [35]. The musculoskeletal model involved in this simulator was built by [PERSON] et al. in SIMM [36]. The anatomical data came from the cadaveric study conducted by [PERSON] et al. [37]  
The test logic behind the experiments is shown in Figure 8. Two performance indicators, namely average judgment time and accuracy, are used to verify the effectiveness and feasibility of the proposed space classification model for determining the HRI space. Since the kinematic model of DAS is already known, the problem of judging the boundary of the HRI space is equivalent to that of defining the range of the collaborative space of the two robotic arms. We experimentally verified the accuracy of the space classification model using inverse kinematics. In the space verification experiments, 500 moving points are tested in total. The side length of the voxel grid for voxel center filtering is treated as an independent variable. We further determined the influence of the number of midpoints in the point clouds on the algorithm's performance.
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 2 Methods
### The Measurement Campaign
To ensure full synchronization of transmitting and receiving, a reference PPS (Pulse Per Second) signal was used, generated on the transmitting side by the Quartzlock E80-GPS disciplined rubidium oscillator (whose starting point is synchronized with the beginning of the UTC second), while on the receiving side, the PPS signal was generated by the GNSS RTK Receiver GINTEC M1G2. The length of the generated impulse signal was set at 250 ns and it was repeated every second (according to the beginning of the UTC second). This value is due to the maximum hardware capabilities. The parameters of measurement antennas are as follows:  
* Receiving antenna--dipole Emco 3121C set, calibrated for 161.8375 MHz band, 1.4 dBi gain;
* Directional transmitting antenna--SIRIO WY155-3N, 7 dBi gain on the main beam direction and 16 dB backward attenuation. Figure 6: Schematic representation of the measurement set transmitting (**a**) and receiving (**b**). It is worth adding here that during all measurements carried out as part of the campaign, both the transmitting and receiving antennas were placed approximately two meters above ground level and the transmitting antenna was pointing at the nearest wind turbine. The transmitter-receiver line had to be parallel to the day's wind direction in order to identify a rotor whose plane is currently perpendicular to the impulse direction (and this was the targeted turbine rotor).
## 6 Conclusions  
In this paper, a new modification to the MDDM has been developed to provide a more accurate physical model of the MCSSC, especially at low irradiation conditions. The model parameters of the MTDM have been estimated using two soft parameter estimation paradigms called CLPSO and EHO. The simulation results have assessed the parameters estimated by the two competitive methods for the MDDM and MTDM. The MTDM shows better convergence and lower error than the MDDM and also, the I-V curve is much closer to the experimental data which was tested on a MCSSC of a Q6-1380 cell of 7.7 cm\({}^{2}\) area under low-level irradiation. Added to that, extracting the parameters under various irradiation and temperature levels has been employed for a CS6P-240P solar module. The proposed new optimization models have shown efficient performance in dealing with the natural characteristics of the MCSSC compared with previous studies and demonstrate an efficient optimizer with high accuracy. From the obtained results, the MTDM can be considered a promising more accurate solution physical model for MCSSC, especially at low illumination levels. In future work, the uncertainty of the climate condition as well as the shading effect will be incorporated in the estimation model of MCSSC.  
All authors have contributed to the preparation of this manuscript. [PERSON] and [PERSON] designed the idea strategy, studied the data, and wrote the manuscript. [PERSON] and [PERSON] revised and proofread the manuscript, also designed some figures. Finally, [PERSON] and [PERSON] reviewing, editing, and supporting different improvements for the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# A General Framework Based on Machine Learning for
## 2 Background  
Algorithm-selection strategies strive to exploit patterns in a broad spectrum of problem instances to better deal with them. They solve problems indirectly by determining the proper technique for each situation rather than by tackling the problem directly [10; 37; 38]. The selected algorithm is then applied to solve the instance. In this research as well as in previous studies, an algorithm-selection method defines a mechanism that controls how and when to use specific algorithms throughout the search [37]. Before applying any algorithm-selection approach, the task is to identify the strengths and weaknesses of the available algorithms. Algorithms are chosen based on the current problem state and, ideally, must relate to the best available alternative.  
In general, frameworks to generate and deploy algorithm selection strategies should follow a layered architecture. The data flow through these layers in order to generate an algorithm selector and then use it to solve unseen instances. In this architecture, some of the layers may be generic while others may be domain-dependent. The implementations of algorithm selectors available in the literature lack a standardized architecture since each implementation is different from the rest. For this work, we suggest that the algorithm selection process follows a three-layer architecture:  
1. The first layer contains the domain-related aspects such as the instances, their characterization, and the available algorithms to choose from.
2. The second layer \"learns\" which algorithm to apply given the features of the problem at hand.
3. The third layer applies the selected algorithm to solve unseen instances.  
In this work, we focus on the second layer, which is the one where the algorithm selection strategies are produced. This layer is domain-independent (if properly implemented). In the past, researchers have relied on different techniques to learn which algorithm to apply given the features of the problem at hand: neural networks [39], genetic algorithms [40], ant-based search [41], principal component and landscape analysis [42; 43], among others.  
As stated before, most of the research on algorithm selection strategies lack a layered-architecture, making it difficult to change one part of the algorithm selection process without affecting the others. Thus, researchers need to work on many different parts of their model to find whether a change improves the performance of the algorithm selector. Should the performance sit below expectations, more changes must be introduced and tested to improve the algorithm selection strategy. In this work, we propose an alternative where changes to the components of one layer only affect said layer within the algorithm selector. This makes it easier to debug and maintain the algorithm selector.
## 1 Introduction
* Finally, to gain more sights at high SNRs, the asymptotic OP expressions of the system performance for both the AF and DF protocols are also derived, from which we can know that the HIs level, the number of terrestrial relays and the number of antennas have great effects on the system performance at high SNRs. The rest of this paper is constructed as follows. The system model and problem formulation is provided in Section 2. In Section 3, the system performance of the considered networks is investigated. In Section 4, computer simulations are provided to verify the correctness of the theoretical results. In Section 5, a brief summary of this paper is provided. **Notations**: Bold uppercase letters denote matrices and bold lowercase letters denote vectors; \((\cdot)^{H}\) denotes the conjugate transpose, \(\left\|\cdot\right\|\) the Euclidean norm of a vector, \(\left|\cdot\right|\) the absolute value of a complex scalar; \(\exp\left(\cdot\right)\) is the exponential function, \(E\left[\cdot\right]\) the expectation operator, \(\mathcal{CN}\left(\mathbf{a},b\right)\) the complex Gaussian distribution of a random vector \(\mathbf{a}\) and covariance matrix \(b\), \(\mathbf{C}^{M\times N}\) the \(M\times N\) dimensional complex positive semidefinite matrix.
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 2 SA with Crystallization Heuristic and Feedback Strategies
### Proposed Feedback Strategies  
Algorithm 2 shows two different types of feedback: positive and negative feedback. As shown in Figure 2, the positive feedback reduces the crystallization factor, and the standard deviation increases. The negative feedback increases the crystallization factor, and the standard deviation reduces. Table 1 shows three examples of feedback strategies, and additional strategies can be proposed. According to our experience, the negative feedback is always the same: the crystallization factor is incremented by 1.  
Strategy I resets the crystallization factor \(c_{k}\) to the unit. This strategy must be used when the objective function requires more exploration. The algorithm can generate a \(x^{*}\) using larger jumps with higher probability. For example, the global optimum of an objective function with several local minima can be found using this strategy. Strategy III decrements the crystallization factor \(c_{k}\) by one unit. This strategy must be used when the objective function requires more refinement. The algorithm generates smaller jumps when generating \(x^{*}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**FS**} & \multicolumn{3}{c}{**Positive Feedback**} \\ \cline{2-4}  & **Exp Phase** & **Ref Phase** & **Negative Feedback** \\ \hline I & \(c_{k}\gets 1\) & \(c_{k}\gets 1\) & \(c_{k}\gets c_{k}+1\) \\ II & \(c_{k}\gets c_{k}/2\) & \(c_{k}\gets c_{k}/2\) & \(c_{k}\gets c_{k}+1\) \\ III & \(c_{k}\gets c_{k}-1\) & \(c_{k}\gets c_{k}-1\) & \(c_{k}\gets c_{k}+1\) \\ IV & \(c_{k}\gets 1\) & \(c_{k}\gets c_{k}-3\) & \(c_{k}\gets c_{k}+1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positive feedback strategies. Rules to update the crystallization factor \(c_{k}\). The negative feedback is the same for all four strategies. FS = feedback strategy. Exp = exploratory. Ref = refinement.  
Strategy II stays between strategies I and III. In this strategy, the crystallization factor is divided by 2. Strategy IV is a hybrid strategy in which the value attributed to the crystallization factor \(c_{k}\) depends on the algorithm phase: the exploratory or refinement phase. [PERSON] et al. explained that the objective function standard deviation can determine the transition temperature when the algorithm goes from the exploratory to refinement phase [PERSON] et al. (2018). The hybrid strategy performs exploration during the exploratory phase and performs refinement during the refinement phase. All four proposed strategies are used in this research. In the benchmark tests, strategies I and IV are used. The EIT application uses strategy II, and the airplane design application uses strategies I, II, and III.
## 4 Improved Tight Combination Model
### Convergence Analysis of Combination Error
#### 4.5.4 Controllability Analysis of the Sensor-Fusion Error Transmission System  
According to the modern control theory, a system is completely controllable when there is an unconstrained input signal \(u(t)\) that can make the system change from any initial state \(X_{0}\) to another expected state \(X\).  
For the state variables system:  
\[\dot{x}=Ax+Bu \tag{34}\]  
Considering whether the algebraic conditions of Equation (34) are valid, it can be determined whether the system is completely controllable, where A is the system matrix of \(n\times n\) dimension, B is the control matrix of \(n\times m\) dimension, and m is the number of input signals.  
\[rank\big{[}B\quad AB\quad A^{2}B\quad\cdots\quad A^{n-1}B\big{]}=n \tag{35}\]  
For an IMU, the noise disturbance is equivalent to the unconstrained input signal, and the position, velocity, and acceleration of the carrier are measured at each moment as state variables. According to the definition of the fully controllable system, it can be found that the IMU cannot be fully controllable under the influence of the integration calculation error. However, in the double-loop cumulative error model, the error controllability can be introduced because the error state vector at moment \(k\) is correlated with the error state quantity at moment \(k-1\) during the \(i\)th filter fusion, as demonstrated in the simulation experiments in Section 5.
###### Abstract  
Motion correction in magnetic resonance imaging (MRI) has become increasingly complex due to the high computational demands of iterative reconstruction algorithms and the heterogeneity of emerging computing platforms. However, the clinical applicability of these methods requires fast processing to ensure rapid and accurate diagnostics. Graphics processing units (GPUs) have demonstrated substantial performance gains in various reconstruction tasks. In this work, we present a GPU implementation of the reconstruction kernel for the generalized reconstruction by inversion of coupled systems (GRICS), an iterative joint optimization approach that enables 3D high-resolution image reconstruction with motion correction. Three implementations were compared: (i) a C++ CPU version, (ii) a Matlab-GPU version (with minimal code modifications allowing data storage in GPU memory), and (iii) a native GPU version using CUDA. Six distinct datasets, including various motion types, were tested. The results showed that the Matlab-GPU approach achieved speedups ranging from 1.2\(\times\) to 2.0\(\times\) compared to the CPU implementation, whereas the native CUDA version attained speedups of 9.7\(\times\) to 13.9\(\times\). Across all datasets, the normalized root mean square error (NRMSE) remained on the order of 10\({}^{-6}\) to 10\({}^{-4}\), indicating that the CUDA-accelerated method preserved image quality. Furthermore, a roofline analysis was conducted to quantify the kernel's performance on one of the evaluated datasets. The kernel achieved 250 GFLOP/s, representing a 15.6\(\times\) improvement over the performance of the Matlab-GPU version. These results confirm that GPU-based implementations of GRICS can drastically reduce reconstruction times while maintaining diagnostic fidelity, paving the way for more efficient clinical motion-compensated MRI workflows.  
magnetic resonance imaging; motion correction; GPU; CUDA +
Footnote †: journal: Applied sciences  
Article
## 3 Implementation and Results
### Implementation
#### 3.1.2 Algorithm Choice  
Infomax [63] is a widely used ICA algorithm, particularly in the field of biomedical imaging. It assumes that the underlying source component has a super-Gaussian distribution, which is a good model-match for the brain imaging signals [64]. Therefore, we used Infomax for the C-ICT model in this study.  
Since ICA is an iterative algorithm, the optimization of ICA yields different solutions depending on the initialization. Therefore, we performed ICA for 30 independent runs with different random initializations and selected the most consistent run using a metric called cross inter-symbol interference (cross-ISI) [65]. Similarly, IVA-G is also an iterative algorithm, so we performed IVA-G 50 times independently and used cross-ISI to select the most consistent run.
## 2 Models and Problem Formulation
### Definitions and Problem Formulation  
In this paper, we assume that there are \(n\) nodes in a plane and each node has only one antenna (interface), _i.e._, it can only transmit or receive with at most one neighboring node at one time.  
We also assume that each node is equipped with an identical antenna with the same beamwidth \(\theta_{m}\). Each node also has the same _transmission range_, denoted by \(R_{t}\) and the same _interference range_, denoted by \(R_{i}\). Typically, \(R_{i}\) is no less than \(R_{t}\), _i.e._, \(R_{i}\geq R_{t}\).  
Basic definitions are stated as follows.  
**Definition 1**: Link Set_. A link set is defined as a set of links among which no two links in this set share common nodes. Such a link set is denoted as \(LS\). A link set is used to describe a set of links that need to act simultaneously._  
**Definition 2**: Valid Assignment_. A valid assignment to a link set is an assignment of channels such that no two conflicting links are assigned an identical channel. A link set is called a Schedulable Link Set if and only if there exists a valid assignment for the link set._  
**Definition 3**: Interference Ratio_. The interference ratio is the ratio of the interference range to the transmission range, i.e., \(r=R_{i}/R_{t}\). Since \(R_{i}\geq R_{t}\), it is obvious that the interference ratio \(r\geq 1\). The number of interfering nodes around a node heavily depends on the interference ratio._  
**Definition 4**: Node Density_. There are \(n\) nodes randomly located in the plane. Let \(S\) denote the (infinite) set of sectors on the plane with radius \(R_{i}\) and angle \(\theta_{m}\). The number of nodes within sector \(s\) is denoted as \(N(s)\). The density of nodes is defined as \(D=\max_{s\in S}N(s)\)._  
Figure 2: The Interference Model.  
In order to compare our derived results to those with _OMN_ networks [12], we re-state the definition here.  
**Definition 5**: _[_12_]_ _Node Density with Omni-directional Antennas. _There are \(n\) nodes uniformly located in the plane. Let \(C\) denote the (infinite) set of circles on the plane with radius \(R_{i}\). The number of nodes within circle \(c\) is denoted as \(N(c)\). The density of nodes is defined as \(D_{o}=\max_{c\in C}N(c)\)._  
Then we give the definition of the upper bound on the number of channels to ensure collision-free communications in _DIR_ networks.  
**Definition 6**: Upper Bound on the number of channels. _There exist possibly many valid link sets, which represent different combination of communication pairs among the nodes. The problem is to find a number, denoted as \(U\), such that any link set \(LS\) derived from \(n\) nodes is schedulable using \(U\) channels. In other word, \(U\) is the upper bound of channels needed to ensure a collision-free link assignment._
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 3 Finite Element Modeling and Analysis
### Finite Element Modeling  
For the simplified electric control box, considering the complex geometry of the front and lower panels and the uneven thickness of the component, the solid element was used to discretize the component model, and the tetrahedral mesh with good adaptability was selected. The unit size was \(2\) mm. Because the upper cover was fabricated using the stamping process, the thickness was much less than the size in the other two directions, so the shell element is used for discrete. To study the strength and stiffness of the shell structure of the electric control box by performing random vibration analysis, the mass element used for the light electronic circuit board in the box was simplified. The finite element model established in this study is depicted in Figure 5.
# Instance and Data Generation for the Offline Nanosatellite Task Scheduling Problem
## 2 Problem Formulation  
The Offline Nanosatellite Task Scheduling (ONTS) problem can be fully captured by modeling the tasks, or payloads, required behavior, such as to guarantee mission Quality of Service (QoS) and the power generation and management systems. These two main issues are presented in this section.