esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Validation of the Induction Time Prediction
The fit between the predicted values (Equations (15) and (19)) and experimental data from [25] is displayed in Figures 2 and 3. A very good fit was obtained between the experimental and predicated induction times, with an average difference of 10.7% and 2.4% for t\({}_{0A}\) in the spiral-wound and tubular systems, respectively, ranging from 1.5% to 20.0%. The average difference for t\({}_{\rm A}\) for the spiral-wound and tubular systems is 5.2% and 10.0%, respectively, with a range between 0.4% and 15.3% (Table 1). As expected, the same k\({}_{\rm a}\) = 1.0 \(\times\) 10\({}^{-3}\) L/mg s and k\({}_{\rm d}\) = 6.5 \(\times\) 10\({}^{-5}\) 1/s were obtained for predicting t\({}_{\rm A}\) in different RO membrane systems with the same pair of scalant and AS. This result supports the assumption that k\({}_{\rm a}\) and k\({}_{\rm d}\) for a specific pair of scalant and AS are independent of a particular RO system. The analysis for both studies validated the analytical model. It should be noted that generally, a tolerance of up to 30% for measurement system variability is considered acceptable [43]. Figure 1: Experimental and predicted induction times with and without AS as a function of the feed concentration in spiral-wound RO membrane (V\({}_{\rm f}\) = 0.72 m/s [24]). Predicted t\({}_{\rm OA}\) and t\({}_{\rm A}\) were calculated using Equations (15) and (19), respectively, with the fitted values as follows: k\({}_{\rm s}\) = 1.73 \(\times\) 10\({}^{4}\) s/m for t\({}_{\rm OA}\), and k\({}_{\rm A}\) = 3.17 \(\times\) 10\({}^{-4}\) L/mg s and k\({}_{\rm d}\) = 4.00 \(\times\) 10\({}^{-4}\) 1/s for t\({}_{\rm A}\). Figure 3: Experimental and predicted induction times with and without AS as a function of feed velocity in a tubular RO membrane (SI = 3.3 [25]). Fitted values for Equations (15) and (19): k\({}_{\rm s}\) = 7.37 \(\times\) 10\({}^{4}\) s/m for t\({}_{\rm OA}\), and k\({}_{\rm A}\) = 1.00 \(\times\) 10\({}^{-3}\) L/mg s and k\({}_{\rm d}\) = 6.5 \(\times\) 10\({}^{-5}\) 1/s for t\({}_{\rm A}\). Figure 2: Experimental and predicted induction times with and without AS as a function of feed velocity in a spiral-wound RO membrane (SI = 2.5 [25]). Fitted values for Equations (15) and (19): k\({}_{\rm s}\) = 2.43 \(\times\) 10\({}^{5}\) s/m for t\({}_{\rm OA}\), and 1.00 \(\times\) 10\({}^{-3}\) L/mg s and k\({}_{\rm d}\) = 6.5 \(\times\) 10\({}^{-5}\) 1/s for t\({}_{\rm A}\).
## 4 Experimental Results and Discussion  
### Statistical Analysis of Data  
In this study, four dependent variables were analyzed, and each variable was subjected to statistical analysis to create a mathematical model. These models provide information about the independent variables that influence the dependent variables. The two-state logit and the ordinal logit methods used in our study provide a direct fit to the type of dependent variables in the dataset (e.g., mortality status or causes of death). The two-state Logit model is a highly effective method for understanding binary decision processes. Binary outcomes, such as variables like mortality risk or survival status, are commonly modeled using this approach. It adds value to the decision-making process by providing probabilistic predictions. The model's robustness and ease of interpretability make it particularly valuable in addressing sensitive issues such as mortality prediction. In cases where ordinal outcome variables are present, this model is one of the most suitable methods for estimating rank-based effects. For instance, in situations where causes of death or degrees of complications are ranked, the ordinal logit model provides the ability to evaluate both the probability distributions and the impact of independent variables on the ordinal outcomes.  
While modern machine learning methods offer more flexibility, these models are preferred for the following reasons: Compared to machine learning models, the results of these models are easily interpretable. This is especially crucial in areas such as clinical decision support systems, where it is essential to explain why the model produced a particular outcome. Moreover, for small to medium-sized datasets, logit models generate results efficiently and do not require complex computational infrastructures. Finally, the long-standing acceptance of statistical approaches has enhanced the reliability and preference for these models. Particularly in the medical field, doctors and researchers face fewer challenges in understanding the results produced by these models.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 1 Introduction  
One problem faced by many industries is bin picking, where the goal consists of removing an item from a container with an open lid for further manipulation [1]. In most picking scenarios, the objects are arranged in a random fashion. Despite the broad body of research on bin picking, very few works dealt with the specific case of entangled objects. Handling entangled objects is all the more challenging as multiple objects may be inadvertently picked at once, rather than a single one, which can cause disruptions in industrial lines. In a recent publication by [PERSON] et al. [2], they claimed to be the first ones to present practical experiments of picking entangled objects from a pile.  
This research focuses on the case of tube-shaped objects, which are very common in manufacturing and production systems. Examples include refrigeration tubes in the automotive and aircraft industries, water pipes for plumbing installations and heat pipes in civil engineering. In many of these use cases, the tubes contain multiple curvatures along their length. Due to the widespread presence of curvedtubes in the industry, there is a great economic interest in automating the bin picking task for this sort of objects.  
This paper aims to present further advancements of the research regarding bin picking of entangled tube-shaped objects, by focusing on an industrial-inspired case study with radiator hoses from a vehicle's engine. Previously, a perception algorithm was developed to create a geometric model of the tubes from an input point cloud captured by a 3D scanner [3]. This article expands on the modeling algorithm by presenting a motion and grasp planning algorithm that allows a two-fingered gripper to pick, one-by-one, a set of freely curved tubes randomly arranged in a pile. Trajectory synthesis is performed using several heuristics on the tubes' models and the Gazebo simulator is used to test each trajectory and evaluate how likely it is to lead to a case of entanglement. Grasp planning is then performed using the tubes' models and the results of trajectory planning. An efficient resolution method from cases of entanglement using a force/torque sensor is also presented. The proposed solution assumes that all tubes have the same radius and approximately equal mass, which is acceptable in most bin picking scenarios. Finally, this paper intends to formalize key concepts for modeling and handling tube-shaped objects in robotic picking scenarios.  
The remainder of this manuscript is organized as follows. Section 2 presents relevant state-of-the-art research regarding bin picking. Section 3 details the complete approach used to perform bin picking on entangled tubes. It begins by providing a description of the aforementioned tube modeling technique. It then presents the motion planning, grasp planning, and entanglement resolution algorithms. Section 4 presents the experiments to evaluate the solution's performance and discusses the results. Finally, Section 5 summarizes the research's contributions and provides several lines for future research.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 4 Discussion
### Limitations and Future Works
In addition to these models, many deep learning-based HSI classification methods have been developed in recent years [17; 18; 19; 20; 23; 24; 25; 27; 28; 29; 30; 35; 36; 37]. It is of interest to conduct a cross-comparison to guide users to select the most suitable methods in specific applications and assist scholars in designing advanced models. Such a comparison requires large number of datasets with challenging cases in HSI classification, e.g., images with fine-grained object, sample imbalance, inter-class variation, and cloud contamination. However, due to the high cost of HSI acquisition and labeling [62], the amount of samples in the used datasets is still small as compared with the datasets in the field of computer vision [63; 64]. A recent study has developed a high-quality HSI classification benchmark dataset [65], which provides the starting point to construct a standard dataset. We plan to evaluate more HSI classification models after this dataset is released for public use so as to further understand the comprehensive performance differences between models.
###### Abstract  
The effect of non-sinusoidal motion which influences the energy extraction performance of foil is considered in this paper. Two oscillation motions, the combined non-sinusoidal plunging and sinusoidal pitching motion, as well as the combined non-sinusoidal pitching and sinusoidal plunging motion, are selected to investigate the oscillation process of two-dimensional parallel foils numerically. The optimal oscillation motion and average power coefficient at different combined motions are gained. The effects of the plunging motion and pitching motion at different oscillation motions are analyzed, and the evolution law of the foil lift force and vortex field are obtained. It is indicated that the non-sinusoidal motion has a significant influence on energy extraction. When the motion is combined (non-sinusoidal plunging and sinusoidal pitching motion), the best extraction performance is gained at \(K_{h}=-0.5\). The maximal \(C_{Pm}\) is 0.375 and the maximal \(\eta\) is 0.188. When the motion is combined (non-sinusoidal pitching and sinusoidal plunging motion), the maximal \(C_{Pm}\) is 0.623 and the maximal \(\eta\) is 0.312 which appear at \(K_{\theta}=2\). For the same frequency, the more the plunging motion is similar to the sinusoidal motion, the more energy is extracted by foils. While the more the pitching motion approximates to the square wave, the worse the achieved extraction performance is.  
parallel foils; energy extraction performance; non-sinusoidal motion; evolution of the vortex field
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
## 4 Experiment Results
### Validation of the Real-Time Wrench Model and Current Decoupling Unit
The simulation results were approximated to the desired force and torque, which also proves the validation of the real-time current decoupling unit. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Error Ratio** & **Coil 0** & **Coil 1** & **Coil 2** & **Coil 3** & **Coil 4** & **Coil 5** & **Coil 6** & **Coil 7** \\ \hline \(\eta_{\mathrm{F}}\) & 4.16\% & 4.53\% & 2.95\% & 5.05\% & 2.21\% & 4.89\% & 3.91\% & 3.78\% \\ \(\eta_{\mathrm{T}}\) & 8.98\% & 8.32\% & 6.59\% & 8.43\% & 9.39\% & 5.49\% & 5.84\% & 5.36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative error of force and torque solutions for each coil using BEM results as a benchmark. Figure 8: Current results of the proposed real-time current decoupling unit (line) and the off-line BEM simulation program (dots).
## 3 Energy Efficient MAC Protocol Design
### Routing and Transmit Power Calculation  
The coordinator collects the neighboring information from the sensor devices and calculates the schedules including the routing, transmission power and the slot assignment. The path-loss, _PL_, between any two sensors is calculated using the transmit power and received signal strength indication (_RSSI_) Equation (10). _Pt_ is the transmission power from node \(i\) to node \(j\). _RSSI_ is the receiving power of node \(j\). To calculate the routing for each sensor, the coordinator firstly initializes the next hop array and total power array. The initial value of next hop ID is pointed to the coordinator. The total power for data transmission from node \(i\) to node \(j\) is calculated as the power consumption for data transmission from node \(i\) to the coordinator calculated according to Equation (11). \(\sum_{\text{route}}Pt_{ij}\) is the summation of transmission power in the route. \(\sum_{\text{route}}Pr_{ij}\) is the summation of receive power except the coordinator. Excluding the receive power consumption of the coordinator is because the coordinator is outside the human body with large volume, so it is assumed that the coordinator has enough power. For each round calculation, if _TotalPower(i-\(>\)j) + TotalPower(j)_ is smaller than _TotalPower(i)_ (the total power for data transmission from node \(i\) to BS via a specific route), then the next hop of node \(i\) is pointed to node \(j\). The corresponding total power is updated. The route calculation ends when the next hop array does not change. Algorithm 1 shows the route calculation algorithm. For the calculated route, the corresponding minimum transmit power can be calculated according to Equation (12):  
\[\text{PL}=\text{Pt}-\text{RSSI} \tag{10}\]  
\[\text{TotalPower(i->}j)=\sum_{\text{route}}\text{Pt}_{\text{ij}}+\sum_{\text {route}}\text{Pr}_{\text{ij}} \tag{11}\]  
\[Pt_{ij}(dB)=Sensitivity+PL_{ij}+P_{guard}(dB) \tag{12}\]  
```
V: set of all the medical nodes in the networks SETNethop(node _i_) = BS SETTotalPower(node _i_) = TotalPower(_i_>_BS_) WHILENethopArray changed do WHILE\(i\in V\)do WHILE\(j\in V,j\
eq i\)do IFTotalPower(_i_>_j_) + TotalPower(_j_) +TotalPower(_i_) Then  Nexthop(_i_)=_j_ TotalPower(_i_>_j_) + TotalPower(_j_)  End if  End while  End while  End while  Check ifNexthopArray is changed End while  End while
```  
**Algorithm 1**Route calculation
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
Thus, a parametric optimization is needed for the detail configuration design on the topology optimization result. In this paper, a method of the topology optimization with multi-objective function is introduced for lightweight SiC primary mirror design using OptiStruct, which was launched by Altair and integrated into HyperWorks, a comprehensive computer aided engineering (CAE) simulation platform. In order to decrease the mirror surface error under axial gravity, lateral gravity, and polishing pressure at the same time, we combine the multiple objectives into a single objective function by assigning weighting factors to the objectives. In the topology optimization model, the RMS deformation error under each loading cases is replaced by the corresponding structural compliance for reducing the calculated amount, and the weighted compliance minimum is selected as the objective function. Under the constraints of the volume fraction, nodal displacement, draft direction, and cyclic symmetry condition, a new mirror configuration is proposed. To further reduce the local deformation of the mirror surface caused by gravity and polishing pressure, we add auxiliary ribs to the reestablishment model referenced to the topology optimization results. Then size optimization and sensitivity analysis method are used to determine the mirror structure parameters using ANSYS Workbench. The new configuration design mirror is compared with triangular configuration mirror and hexagonal configuration mirror in stiffness and optical performance. The results show the optimized mirror combines the advantages of these two classic lightweight mirrors. And the surface error of the mirror mounted by three given BFs under 1 g radial gravity is analyzed. We investigate the effects of non-coplanar distance \(L\) on the surface figure RMS and coefficient of astigmatism. When \(L\) is 3.5 mm, the surface error of the mirror takes the minimum value and almost has no astigmatism. The results show the mirror design method based on topology optimization with multi-objective function combined size optimization can satisfy the required optical performance and overcome the limitation of parametric studies simultaneously within the operation conditions.
# Portable Prototype of Hydrogen Fuel Cells for Educational Training
## 4 Experimental Setup
### Evaluation of the Characterization Results
#### 4.9.2 Parallel-Connected FC  
Experiments are performed with fuel cells connected in parallel, which are connected to a variable resistor that changes the current. The voltmeter is connected in parallel, and the ammeter is with a shunt in series. Figure 14 shows the connection diagram. Before starting the experiment, fill the tank with hydrogen and then connect the circuit as in Figure 14 and adjust the variable resistor so that a current of 1000 mA flows through it. Hydrogen consumption is recorded for three minutes, and the measurement has repeated three times. The mean consumption value is reduced by the leakage rate from the previous experiment, and the leakage rate is 0.8 mL/min, which means that 2.4 mL of hydrogen leaked out in three minutes. The energy efficiency of the fuel cell is calculated according to the following equation:  
\[\eta_{fc}=\frac{W_{electrical}}{W_{chemical}}=\frac{V\cdot I\cdot t+l^{2}\cdot R }{H_{\mathrm{H_{2}}}\cdot V}=\frac{0.44\cdot 1\cdot 180+1^{2}\cdot 0.1}{11920 \cdot(19-2.4)\cdot 10^{-3}}=0.4008 \tag{8}\]  
This equation shows that the energy efficiency of the cells on the model is 0.4008 or 40.08%. Other measurements are performed in the same way. Only the current of parallel connected fuel cells is changed. Thus, for currents equal 0.4-1 A, the efficiency is about 50%,which is more than the case of a series connection. This type of connection is preferred for applying a fuel cell in practice. The system's maximum efficiency is calculated according to the Equation (9) by taking the maximum efficiency value of the fuel cell in parallel connection and multiplying it by the maximum efficiency value of the electrolyzer.  
\[\eta_{t}=\eta_{fc}\cdot\eta_{el}=0.6671\cdot 0.9396=0.6268 \tag{9}\]  
The maximum efficiency of the system of parallel connected fuel cells is 62.68%.
## 3 Materials and Methods
### Analytical Analysis  
In analytical buckling analysis, Euler buckling theory is a fundamental theory elucidated in many reviews. It is important to note that the linear Euler buckling equation found in these reviews is valid only when the material is still within its elastic limit (that is, the critical slenderness ratio of a material, \(\alpha_{cri}\), is less than the slenderness ratio of a column, \(\alpha_{col}\)). The slenderness ratio \(\alpha_{col}\) is defined as \(L_{eff}\) / \(R\), where \(R\) is the least radius of gyration; R is defined as \(\sqrt{\frac{I_{min}}{A}}\). The slenderness ratio of a material \(\alpha_{cri}\) is expressed as \(\sqrt{\frac{2\pi^{2}E}{\sigma_{y}}}\), where \(\sigma_{y}\) is the yield strength of a material. However, if the value of \(\alpha_{cri}\) is higher than the value of \(\alpha_{col}\), the critical buckling load is calculated by using the Johnson equation, as shown below:  
\[F_{cri}=A\left[(\sigma_{y})-\frac{1}{E}\left(\frac{\sigma_{y}}{2\pi}\right)^{ 2}(\alpha_{col})^{2}\right]. \tag{1}\]  
As mentioned, the classical buckling analysis does not depend on a material's yield strength. Therefore, yield considerations are required in the buckling analysis of a column in order to obtain a stress (Euler's load divided by the cross-section area of the column, \(A\), given in the equation below) and compare this value to the yield strength of a material to identify a failure mode of the material under compression if yielding appears before buckling.  
\[\sigma_{cri}=\frac{F_{cri}}{A} \tag{2}\]
# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
## 4 Tensor-SOM
### Reference Vectors in Tensor-SOM  
In conventional SOM, each unit is associated with a single reference vector. In contrast, Tensor-SOM assigns one reference vector \(\mathbf{y}_{kl}\) to each unit pair \((k,l)\), corresponding to one point in the two-dimensional map space formed by the two modes. As a result, the model contains \(K\times L\) reference vectors in total, forming a third-order tensor:  
\[\mathbf{Y}=(y_{kld})\in\mathbb{R}^{K\times L\times D}.\]  
The reference tensor \(\mathbf{Y}\) is also randomly initialized to ensure unbiased and exploratory learning at the initial stage.
## 2 Theoretical Model  
### Equation of Motion  
To replicate the vibration of the elastic steel sheet, we employ a nonlinear Euler-Bernoulli Beam. Drawing from [PERSON] and [PERSON]'s theory of nonlinear beams [17], we specifically focus on the plane motion of the nonlinear beam. Employing [PERSON]'s second law, [PERSON]'s angle transformation, and Taylor series expansions, we derive the equation for the two-dimensional nonlinear beam as follows:  
\[\overline{m\ddot{u}}-EA\overline{m}^{\prime\prime}=EA\left(\frac{1}{2} \overline{W}^{\prime}{}^{2}-\overline{u}^{\prime}\overline{W}^{\prime}{}^{2} \right)^{\prime}+EI_{A}[\overline{W}^{\prime}\left(\overline{W}^{\prime\prime }-\overline{u}^{\prime\prime}\overline{W}^{\prime}-2\overline{u}^{\prime\prime }\overline{W}^{\prime\prime}-3\overline{u}^{\prime}\overline{W}^{\prime\prime }\right)]\ ^{\prime} \tag{1}\]  
\[\overline{m\ddot{W}}-EI_{A}\overline{W}^{\prime\prime}=EA(\overline{u}^{\prime }\overline{W}^{\prime}-\overline{u}^{\prime 2}\overline{W}^{\prime}+\frac{1}{2}\overline{W}^{ \prime\prime})^{\prime}+EI_{A}[\overline{u}^{\prime}\overline{W}^{\prime \prime}+(\overline{u}^{\prime}\overline{W}^{\prime})^{\prime\prime}-( \overline{u}^{\prime 2}-\overline{W}^{\prime 2})\overline{W}^{\prime\prime} \tag{2}\]  
The definition of coordinates in the above equation is shown in (Figure 5). In Equations (1) and (2), \((\ )^{\prime}\) represents the differential in space \((\frac{d}{d\overline{u}})\), \((\ )\) denotes the differential in time \((\frac{d}{d\overline{t}})\), \(\overline{m}\) is the mass of the beam, \(E\) represents [PERSON]'s modulus, \(A\) is the cross-sectional area of the nonlinear beam, \(I_{A}\) denotes the moment of inertia, \(\overline{q}\) is the uniform distributed simple harmonic load. We assume that the boundary conditions of this nonlinear beam are that one end is fixed, the other end is a free end, and a magnet (considered as a point mass) is added to the free end. The boundary conditions can be assumed to be:  
\[W\ (0,\overline{t})=0 \tag{3}\]  
Figure 4: Device schematic diagrams of slap-type VEH (**a**) installed in forward flight (moving) vehicles, (**b**) installed in vertical flight (moving) vehicles. Figure 5: Coordinate diagram of elastic beam vibration system. \[W^{\prime}\;(0,\overline{t})=0 \tag{4}\]  
\[EI_{A}\overline{W}^{\prime\prime}\;(\overline{l},\overline{t}\;)=0 \tag{5}\]  
\[EI_{A}\overline{W}^{\prime\prime}\;(\overline{l},\overline{t}\;)=-\overline{M} \alpha^{A}a^{4}\overline{W}\;(\overline{l},\overline{t}\;) \tag{6}\]  
In Equations (3)-(6), \(\overline{M}\) is the mass of the magnet. We assumed the free end of the system has no axial external force, and for a slender beam, the ratio of \(m/EA\) is small. The terms of \(\overline{m}\dot{\overline{u}}\) are neglected.