esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Calculation Method
### EWF Model
#### 2.2.2 Calculation Process  
For the collection of water drops on solid walls, the source term can be obtained from the following equation:  
\[\dot{m}_{s}=\alpha\rho_{l}V_{d}A \tag{19}\]  
where \(V_{d}\) is the velocity of the water drop perpendicular to the surface and \(A\) is the surface area.  
The momentum source term \(\dot{q}\) can be calculated from the following equation:  
\[\dot{q}=\dot{m}_{s}\mathbf{u} \tag{20}\]  
Figure 3: EWF model.  
Local water-drop collection coefficient \(\beta\)[34] is the proportion of the practical water-drop collection rate on the surface of the micro-element to the maximal possible water-drop collection rate. The Eulerian method is obtained from the following formula [35]:  
\[\beta=\frac{\alpha_{n}(\mathbf{u}\cdot\mathbf{n})}{V_{\infty}},\ \alpha_{n}=\frac{\alpha}{ \alpha_{\infty}}. \tag{21}\]  
where \(\alpha_{n}\) is the normalized volume fraction of the water drop, \(\mathbf{n}\) is the unit surface vertical vector, \(V_{\infty}\) is the free inflow velocity, and \(\alpha_{\infty}\) is the LWC of the water drop in the distant field.  
The continuity equation and momentum conservation equation of the fluid film are the basis of the EWF model. The EWF model is coupled with the Eulerian multiphase flow model through the source term of the control equation. In the EWF model, the second phase will be trapped on the wall to shape a fluid film, and the mass and momentum of the film separated from the wall will be added to the second phase of multiphase flow due to the high relative velocity between the air and the film. The material set in the EWF model is water in the calculation process; the water drop collected by the wing wall will be transferred from the multiphase flow to the EWF model as the source term of the water film control equation. In this paper, the mass and momentum flux of the fixed water film is specified in the wing wall boundary conditions, which are added to the control equation as source terms to compute the water-drop collection rate and film thickness on the wall [38]. Figure 4 shows the calculation process of the approach in this paper, where \(C\) is the convergence absolute criterion for all equations.  
In this work, the discretization of the first order explicit time and the first order upwind control equation is adopted in the solution algorithm of the EWF model. In the solution of pressure, a second-order accuracy approach is implemented, whereas for the solution of momentum, volume fraction, turbulent kinetic energy, specific dissipation rate, and energy, a first-order upwind scheme is employed.  
Figure 4: Flow chart of water-drop collection and fluid film flow calculation.  
The present article does not account for the effect of gravitational forces, whilst employing the following physical parameter values in the proposed solution: \(\rho_{l}\) = 1000 kg/m\({}^{3}\), \(\rho_{a}\) = 1.225 kg/m\({}^{3}\), \(\mu_{a}\) = 1.79 \(\times\) 10\({}^{-5}\) kg/(m\(\cdot\)s), \(P_{\sigma}\) = 0.07194 N/m.
# ALReg: Registration of 3D Point Clouds Using Active Learning
## 2 Related Work
### Active Learning for Point Clouds  
Recently, active learning gained prominence in 3D point cloud research. In [28], an active learning framework for point cloud segmentation is presented. In this study, three selection methods are investigated (shape-level, superpoint-level and point-level) using a hybrid acquisition function. While a feature-based distance metric is introduced to evaluate the diversity of unlabeled data, entropy is used as an uncertainty score. In ReDAL (Region-Based and Diversity-Aware Active Learning) [26], a super-point level active selection is done for training. Although entropy is used as an uncertainty metric as in the previous study, feature diversity is calculated using color intensity differences and surface variances. Also, a diversity-aware selection is presented to assure diversity while selecting multiple items. In SSDR-AL (Spatial-Structural Diversity Reasoning for Active Learning) [27], the uncertainty score is weighted according to class distributions. Then, Chamfer distance on the feature-graph space is used to select the most diverse points. In [7], a conditional Markov random field for segmentation is trained using classification errors as scores. In [10], a classification method for terrestrial laser scanning data is presented and a classification confidence metric to select points is used. In [8], four different active learning strategies are evaluated on airborne laser scan (ALS) data: Random sampling, point entropy, segment [76] entropy, and mutual information where segment entropy metric has the best scores. In [9], an ensemble of classifiers is used, and disagreement between each classifier's predictions is scored for segmenting ALS point clouds. In [77], a multi-granularity active learning pipeline is developed.  
As can be seen from the related works, studies on active learning for point clouds entirely focus on the segmentation task. Generally, the main purpose is using fewer point clouds or point cloud parts while training. However, none of these studies have considered active learning usage in point cloud registration. Thus, to the best of our knowledge, AlReg is the first study on point cloud registration with active learning.
## 3 Results and Discussion
This situation is also illustrated in Figures 4 and 5, where depending on the computational grid resolution, the area depicting the occurrence of a certain concentration level changes. This stems from a fact that, in the case of low resolutions (e.g., 1000 m), a given area is represented solely by one terrain characteristic when actually, in its surroundings, there may be a greater diversification of the height of the terrain and land cover classes, which becomes visible only at higher grid resolutions. Consequently, the algorithms of the kinematic terrain effect, the flow of air masses from the slope or the blocking effects of the terrain in the wind field diagnostic model [48] result in obtaining different diffusion conditions for different horizontal resolutions. On the other hand, according to the methodology described in Reference [49], establishing the actual receptor height resulting from the varying landform determines the air concentration level. Increase in the computational resolution causes the receptors located below or above the receptor representing a given area at lower grid resolution (greater grid spacing) to be included in the calculations. For example, the higher the elevation of the computational point, the shorter the vertical distances between the receptor and emitted plume, so the concentration obtained at the surface area is higher. A certain solution to this problem is the introduction of additional computational receptors, which represent the highest elevation points located within the impact of the emitter, as it is, for example, recommended for the AERMOD model [57]. It should also be noted that, at the long averaging periods, the resolution is not as significant, which is shown by high correlation coefficients between the results of calculations between Variant V1 and other analyzed variants for the annual average concentrations (Table 4 and Figure 8). In this case, the spatial distribution of the concentrations  
Figure 8: Spatial differentiation of the correlation coefficients between the analyzed cases obtained for distinct types of the NO\({}_{2}\) air concentrations in the discrete receptors grouped depending on the distance from the emission source. is determined primarily by the wind rose and not only by the terrain. However, it should be kept in mind that the wind rose characteristic for this terrain is dependent to a great extent on the flow direction of the main ventilation channel in Krakow, which runs along the Vistula River valley [58] and on the presence of elevations located mainly in the southern and northern part of the computational area (Figure 3).
## 2 Theory and Formulation
### Problem Description and Fundamental Equations
Solution Procedure**  
An analytical method is now implemented to solve the governing Equations (28) and (29), based on the following kinematic definition  
\[u =UE^{i(\beta_{x}x-\omega_{n}t)} \tag{30}\] \[w =We^{i(\beta_{x}x-\omega_{n}t)} \tag{31}\]  
in which \(U\) and \(W\) denote the wave amplitudes, \(\beta_{x}\) and \(\omega_{n}\) denote the wave number and circular frequency propagated waves, respectively. By substitution of \(w\) and \(u\) from Equations (30) and (31) into Equations (28) and (29) we get the following eigenvalue problem  
\[\left(\left[ \begin{array}{cc}k_{11}&k_{12}\\ k_{21}&k_{22}\end{array} \right]-\omega_{n}^{2}\left[ \begin{array}{cc}m_{11 }&m_{12}\\ m_{21}&m_{22}\end{array} \right]\right)\left[ \begin{array}{c}U\\ W\end{array} \right]=0 \tag{32}\]  
which is solved in terms of \(\omega_{n}\), and wave frequency. This means that the following determinant must be equal to zero  
\[\left|\left[K-\omega_{n}^{2}M\right]_{2\times 2}\right|=0 \tag{33}\]  
where the wave frequency \(f\) is defined as  
\[f=\frac{\omega_{n}}{2\pi} \tag{34}\]  
Thus, the phase velocity associated with the problem can be determined as  
\[C=\frac{\omega_{n}}{\beta_{x}} \tag{35}\]
## 3 An Improved Objective Function Based on SVDM
### Selection of Key Parameters in Improved Objective Function  
The calculation of the frequency response \(h_{aq}(\mathbf{\mu},\overline{\omega_{i}})\) in the improved objective function depends on the damping ratio and excitation. The weight of the frequency and mode shape terms of the theoretical FE model in the improved objective function will affect the accuracy of damage identification, and the gradient of the improved objective function will be helpful to increase the optimization efficiency. So, in order to ensure the efficiency of the improved objective function and the accuracy of damage identification results, the selection of these key parameters is very important. The selection principles of these parameters are introduced and discussed as below.
## 3 Results and Discussion  
### FTIR (Fourier Transform infrared spectroscopy)  
Figure 1 shows the Fourier Transform infrared spectroscopy (FTIR) spectra of PZT particles before and after treated respectively with coupling agent contents of 0%, 0.5%, 1%, 1.5%, and 2%. As shown in Figure 1, there is a stretching-vibration-absorption peak of functional groups of CH\({}_{3}\), CH\({}_{2}\), CH at 2929.7 cm\({}^{-1}\). At the same time there exists a stretching vibration characteristic peak of C-O-Si at 1060.7 cm\({}^{-1}\). All these peaks belonged to the characteristic peaks of KH-570. Further analysis of the results showed that the peak intensity at 761.8 cm\({}^{-1}\) increased, and the peak became narrowing and sharp. The appearance of such absorption peaks showed that KH-570 had an effect on the surface of  
PZT particles. Figure 1b shows the FTIR spectrum PZT ceramic particles treated and untreated by the titanate coupling agent NDZ-101, respectively. It can be found that the PZT treated by NDZ-101 showed an evident absorption peak of CH\({}_{2}\) and CH\({}_{3}\) at 2920.07 cm\({}^{-1}\), which was the characteristic absorption peak of CH\({}_{2}\) and CH\({}_{3}\) functional group in NDZ-101. It showed NDZ-101 on the surface of PZT particles. In addition, it showed an absorption peak of Ti-O bond at 1076.22 cm\({}^{-1}\), which had an overlap with the absorption peak of PZT, and the peak intensity was increased, which showed that the chemical reaction may occur between titanate coupling agent and PZT particles.
# VirtualFilter: A High-Performance Multimodal 3D Object Detection Method with Semantic Filtering
## 2 Related Work
### Image Semantic Segmentation  
The semantic segmentation task aims to classify every pixel in a 2D image and fully extract semantic information from the image. This task has a long history, and the model structure has undergone many transformations, beginning with Fully Convolutional Networks (FCNs) [14] in 2015, which replaced fully connected layers with convolutional layers to enable dense pixel-level predictions. Shortly after, models like U-Net [15] and SegNet [16] introduced encoder-decoder architectures with innovations like skip connections and pooling indices for better spatial information retention. From 2016 onwards, DeepLab [17] (v1 to v3+) advanced multi-scale feature extraction using atrous convolutions and spatial pyramid pooling, while PSPNet [18] introduced pyramid pooling for context aggregation. Recently, Transformer-based models like SegFormer [19] and Swin Transformer [20] have revolutionized segmentation by leveraging global dependencies, achieving state-of-the-art performance in various benchmarks.  
Previous academic work has also incorporated semantic information into 3D object detection tasks. For instance, Method PointPainting [9] uses projection to reduce the dimensionality of point clouds and input them into a semantic segmentation network to obtain semantic information of point cloud data, which is then used for subsequent feature extraction work. This method has universality, and through this method, all networks thatuse point cloud data as input can achieve improvement. However, at the same time, the addition of semantic information also increases the complexity of subsequent calculations. The MVP [1] method directly uses the semantic segmentation results of 2D images to generate virtual point clouds in order to compensate for the shortcomings of the original point clouds. However, the results of semantic segmentation of images are not completely accurate. If only the semantic information of the image is used to generate the virtual point cloud, it will also cause the problem of inaccurate generation of pseudo point cloud.
# A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm
## 5 Conclusions  
A modeling method using ANN for ball joint rotation angle detection was proposed in this paper. The corresponding ANN algorithm and software were designed and equipped, and a new prototype was developed. The main conclusions include:  
1. The RBF ANN was used to construct the relationship between the input and output of the intelligent ball joint measurement model. According to the technical characteristics and requirements of the ANN, a new prototype of intelligent ball joint was rematched and developed.
2. The BP ANN was used to divide the measuring space of the intelligent ball joint into four sections. The whole algorithm comprises the front-end BP ANN partition algorithm and the back-end RBF ANN calculation angle algorithm. It optimizes the number of inputs and hidden layers of the ANN, and it improves the calculation accuracy.
3. The experimental data show that, compared with the traditional analytical model, the measurement accuracy of the intelligent ball joint is improved by using ANN. At the same time, the manufacturing accuracy requirement and the assembly of the ball joint are reduced, and the real-time performance of the measurement method is increased.
4. Python language was used to compile the upper computer interface of the intelligent ball joint, which meets the training, learning, and modeling requirements in the measurement of a ball joint, and basically achieves the real-time display of the rotation angle. This study reminds us that in precision measurement or sensor development, it is not necessary to establish traditional analytical models, and various intelligent algorithms may be a new choice.  
[PERSON] and [PERSON] conceived, designed the instrument structure, and wrote the paper, [PERSON] guided the instrument design and provided new ideas about the instrument structure; [PERSON]-[PERSON] designed and produced the data acquisition system; [PERSON] finished the experiment and designed the software.  
The authors are grateful for the financial support provided by the Natural Science Foundation of China (51475133, 51675157,51775163)  
The authors declare no conflict of interest.
## 2 Related Work
### Hierarchical Federated Learning  
Several scalability challenges have been associated with conventional FL architectures, and to tackle them, a solution known as Hierarchical Federated Learning (HFL) has been developed. In HFL, model updates are collected at different levels (e. g., local institutions, regional hubs, and central servers) to reduce communication load and computational load at each level [26]. This hierarchical structure in federated learning has been proven to enhance scalability and alleviate the communication overhead challenges in large-scale distributed systems [27]. Study [28] extended hierarchical aggregation models for healthcare applications and proved that these models were suitable in cases where the data was collected from different healthcare providers located in different geographical regions.  
The incorporation of hierarchical structures in federated learning is most suitable for healthcare systems that have many hospitals, clinics, and specialized institutions. These systems can benefit from hierarchical aggregation as it makes it possible to optimize the models for certain regions before aggregating them, and hence the communication load on the central servers is minimized. Still, to the best of our knowledge, incorporating privacy-preserving techniques like DP or SMPC in traditional hierarchical FL systems has not been discussed prominently in the literature.  
Study [29] presented a new approach known as Hierarchical Split Federated Learning (HSFL). The HSFL framework was developed on the basis of a grouping mechanism. In each group, partial models were trained on devices while the rest of the computations were performed on edge servers. Following the computation of the local models, each group performed local aggregation at the edge. This edge-aggregated model was then pushed to the end devices for model updating. By the end of this process, each group had fashioned themselves a distinct edge-aggregated HSFL model through several iterations. These models were then communicated to edge servers and averaged to generate a global model. Furthermore, an optimization problem was formulated to minimize the cost of HSFL based on parameters such as the relative local accuracy (RLA) of the devices, transmission latency, transmission energy, and compute latency at the edge servers. This problem was recognized as a MINLP problem, which was a challenge because of its nonlinearity. To address this, the work broke down the problem into sub-problems: an edge computing resource allocation problem and a joint sub-problem that covered RLA minimization, wireless resource management, task assignment, and power control. Edge computing resource allocation was performed through convex optimization because the problem had convex properties.
## 3 Results  
Using the base model (M\({}_{\text{genPCSA}}\)) resulted in a mean relative error of around 25% (Figure 4). The error was substantially reduced (approximately 15%) when a second level of personalization was introduced (exploiting the MRI or EMG data), with no particular distinctions between the M\({}_{\text{s8 PCSA}}\) and the M\({}_{\text{genPCSAsEMG}}\) models. A greater relative error reduction (to 5%) was observed when both anthropometric measurements and subject-specific muscle activations were used (the M\({}_{\text{s8 PCSAsEMG}}\)).  
At a subject level (Figure 5), the M\({}_{\text{genPCSA}}\) model approximated the experimental data only for one out of nine subjects (Figure 5a, subject 7). Using segmented muscle volumes to further tune the MIF values resulted in a more apparent reduction in the simulated extension torque, which more closely approximated the measured values. The above was not true for subjects 6 and 8, for whom the M\({}_{\text{ssPCSA}}\) model predicted an MVIC torque further from the experimental data than the M\({}_{\text{genPCSA}}\) (Figure 5b). The additional use of EMG data (M\({}_{\text{ssPCSAsEMG}}\)), more specifically to limit the maximum activation levels of quadriceps muscle, enabled to improve the predictions, which well approximated the experimental values: for all the nine subjects, the M\({}_{\text{ssPCSAsEMG}}\) model was able to predict the experimental MVIC torque within 6%.  
Last, using \(\mathrm{M_{genPCSAssEMG}}\) models, the error remained high. Only the models of four out of nine subjects predicted a maximal torque with a predictive error within 10% of the corresponding real data.
## 3 Modeling and Control  
### Milli/Microbot Dynamic Analysis  
In the actuation process, in addition to actuation by an external force \(F_{m}\) applied by the magnetic field, the milli/microrobot is also subjected to liquid viscous forces \(F_{d}\) that opposes its direction of motion, as well as \(F_{\delta}\) and buoyancy forces \(F_{b}\) in the liquid environment. The electrostatic force and Van der Waals  
Figure 5: Rectangular electromagnetic coil set actuation platform model.  
force have a weak influence on the milli/microrobot motion compared with the above-mentioned main forces [28; 29; 30; 31; 32] by considering the milli/microrobot's millimeter-scale shape. As a result, they both have been ignored in order to simplify the dynamic model, which is shown as follows:  
\[F_{m}+F_{d}+F_{g}+F_{b}=ma \tag{17}\]  
The liquid viscous force \(F_{d}\) of the milli/microrobot in the liquid environment can be approximated as:  
\[F_{d}=-\frac{1}{2}\rho_{f}(v-v_{f})^{2}AC_{d} \tag{18}\]  
where \(v\) of is the moving speed the milli/microrobot, \(v_{f}\) is the liquid flow velocity, \(\rho_{f}\) is the fluid density, \(A\) is the front-end area when the milli/microrobot moving, and \(C_{d}\) is the drag coefficient, which can be estimated as follows:  
\[C_{d}=\frac{24}{R_{e}}+\frac{6}{1+\sqrt{R_{e}}}+0.4\]  
\[R_{e}=\frac{\rho_{f}(v-v_{f})L}{\mu_{f}}\]  
where \(R_{e}\) is the Reynolds number, \(\mu_{f}\) is the dynamic viscosity coefficient of the fluid, \(L\) represents the characteristic length of the milli/microrobot.  
The milli/microrobot in a liquid environment receives its own buoyancy force in the vertical direction. The calculation formula is expressed by Equation (19), where \(V\) represents the volume of the milli/microrobot and \(\rho\) represents the material density of the milli/microrobot.  
\[F_{g}+F_{b}=V(\rho-\rho_{f})g \tag{19}\]  
From the above analysis, (19) can be rewritten as (20), where \(k_{x}\), \(k_{y}\), \(k_{z}\) are constant coefficients determined by the milli/microrobot in the actual working environment.  
\[\begin{split} m\left(\begin{array}{c}a_{x}\\ a_{y}\\ a_{z}\end{array}\right)&=\left(\begin{array}{c}F_{mx}\\ F_{my}\\ F_{mz}\end{array}\right)+\left(\begin{array}{c}0\\ 0\\ -V(\rho-\rho_{f})g\end{array}\right)\\ &\quad+\left(\begin{array}{ccc}-k_{x}&0&0\\ 0&-k_{y}&0\\ 0&0&-k_{z}\end{array}\right)\left(\begin{array}{c}(v-v_{f})_{x}^{2}\\ (v-v_{f})_{y}^{2}\\ (v-v_{f})_{z}^{2}\end{array}\right)\end{split} \tag{20}\]  
Control the milli/microrobot to move along a particular trajectory which can be divided into calculating the required magnetic field and gradient at discrete points on the trajectory in real-time. An example is shown in Figure 6. The milli/microrobot moves along the planned spatial curve trajectory called \"mountain climbing\" motion from the starting point in three-dimensional space, and the main force analysis at each point is indicated by different color arrows.