esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Detection Neck  
The Detection Neck adopts the classic RPN [25] structure. As shown in Figure 5, it includes three consecutive convolutional blocks (Block1, Block2, and Block3) to obtain feature maps with three different resolutions. Then, these three feature maps are upsampled using three transposed convolutional blocks (DeBlock1, DeBlock2, and DeBlock3) to obtain three feature maps with the same resolution, and finally, these three feature maps are concatenated together to obtain the final feature map. The details of the blocks and deblocks are shown in Table 1. In Table 1, Conv2d represents a 2D convolutional layer, while DeConv2d represents a 2D transposed convolutional layer. The numbers enclosed in parentheses indicate the number of input channels, the number of output channels, and the size of the convolution kernels, stride, and padding, respectively. Furthermore, the output of the Detection Neck is fed into the Detection Head to produce the final recognition results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Details** & **Module** & **Details** \\ \hline \multirow{2}{*}{Block1} & Conv2d (64, 64, 3, 2, 0) \(\times\) 1 & \multirow{2}{*}{DeBlock1} & DeConv2d (64, 128, 1, 1, 0) \(\times\) 1 \\  & Conv2d (64, 64, 3, 1, 1) \(\times\) 3 & & \\ \hline \multirow{2}{*}{Block2} & Conv2d (64, 128, 3, 2, 0) \(\times\) 1 & \multirow{2}{*}{DeBlock2} & DeConv2d (128, 128, 2, 2, 0) \(\times\) 1 \\  & Conv2d (128, 128, 3, 1, 1) \(\times\) 3 & & \\ \hline \multirow{2}{*}{Block3} & Conv2d (128, 256, 3, 2, 1) \(\times\) 1 & \multirow{2}{*}{DeBlock3} & DeConv2d (256, 128, 4, 4, 0) \(\times\) 1 \\  & Conv2d (256, 256, 3, 1, 1) \(\times\) 3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of blocks and deblocks.
## 2 Channel Reliability Modeling for 5G Communications
### Wireless Channel Model
\(\xi\) denotes the shadow fading of the signal, and, usually, \(\xi\) obeys the log-normal distribution. \(\left[\xi\right]_{dB}\) is a normally distributed random variable with a mean of 0 and variance of \(\sigma_{\xi}^{2}\). In wireless communication, to ensure the accuracy of the received signal, the SNR of the received signal is usually required to be more than a certain threshold, expressed mathematically as  
\[\Gamma_{\text{n}}\geq\gamma^{t} \tag{3}\]  
where \(\gamma^{t}\) denotes the desired SNR threshold.
## 5 Multiple Trees Hector Extension  
As we could see, in Hector, the packet delivery is guaranteed because of the use of a tree. Nevertheless, following that tree may lead to important stretch factors in the routing path. One way to bypass this drawback is to use multiple trees. All trees are built independently as explained in Section 3.2. Each node has one label per tree. The \(T\) label of a node \(u\) is now: \(T(u)=\left\{l_{i}(u)\right\}_{i=0,..,t-1}\) where \(t\) is the number of trees and \(l_{i}(u)\) is the label of node \(u\) in Tree \(i\).  
A \(2\)-tree example is displayed by Figure 2. Let us illustrate on this example what advantages may bring the use of several trees. Let us assume that node \(0\) wants to send a message to node \(6\). The shortest path in the graph from node \(0\) to node \(6\) is \(0-3-4-5-6\) (path length: \(4\) hops). If we use Tree \(A\) (blue tree), the message will follow labels \(A00-A0-A-A1-A10-A100-A1000\), which corresponds to a \(6\)-hop path going through nodes \(0-1-10-2-4-5-6\). If we use Tree \(B\) (blue tree), the message will follow labels \(B0110-B011-B01-B0-B1-B10\), which corresponds to a \(5\)-hop path going through nodes \(0-3-4-7-8-6\). Note that using Tree \(B\) allows the use of a shortcut between nodes \(7\) and \(8\).  
Hence, the use of several trees allows the use of more routes, which provides a better load balancing and shorter paths. In our example, node \(0\) will follow Tree \(B\) since it provides a shorter path than Tree \(A\).  
The use of several trees may even allow even shorter paths since the choice of the tree is performed independently at each routing step. If we look back at our example, node \(0\) computes the distance between each neighbor of its and the destination on every tree. It finds out that it has to send the message through Tree \(B\) to node \(3\). Node \(3\) runs the same algorithm and sends the message to node \(4\), still through Tree \(B\). When node \(4\) has to elect the next hop to the destination, it finds out that the path is shorter by following Tree \(A\) and sends the message to node \(5\), which delivers the message to node \(6\). By switching dynamically and naturally between trees all along the path from the source to the destination, a \(4-hop\) path is followed (through nodes \(0-3-4-5-6\)).  
This is the motivation of multiple-tree HECTOR.  
Note that building several trees bring obviously better performances but also presents a higher costs linked to the construction and maintenance of several trees. The evaluation performed in Section 6.4 shows the trade-off to adopt between cost and performance.
# Numerical Analysis of Laser Pattern Effects to Residual Stress on Metal 3D Printing
## 2 Composition of FEM Model
### Numerical Model  
In order to calculate the contents previously configured, a numerical analysis model was constructed by applying it to the Mechanical program from Ansys. To consider the thermal conduction phenomenon, a transient structure-thermal analysis was applied. To verify the analysis result, the processing area was set to 5 mm \(\times\) 5 mm to facilitate the comparison with a specimen, and an area with a width of approximately 1 mm was set around the laser path to simulate heat transfer into the surrounding powders. The height of one layer was set to 0.1 mm, which is the same as that of the verification specimen. In the previous study, the lamination process of one to ten layers was analyzed, and it was confirmed that the effect of the top layer was transmitted to up to three lower layers. Based on these results, it was inferred that the entire lamination process could be represented by lamination analysis, which sets the three layers to be interpreted consecutively [10]. The thickness of the base material was set to 1 mm, such that the heat transfer based on the heat input was similar to the actual process.  
For the thermal-structure coupled analysis, an element supporting the physical properties and coupling operations of each physical system was set. For the element type applied to the analysis, Solid 226 and Solid 227, provided by the software, were applied so that the above-formed expression could be calculated. The number of nodes in the grid used in the analysis was 19,373, and the number of elements was 19,579; the shape is shown in Figure 2. Boundary conditions were set for the entire analysis model, and the actual process conditions were applied.  
Restraining conditions were achieved by attaching the bottom surface to the base material or substrate, and heat transfer conditions were applied to the bottom and side  
Figure 2: FEM model of basic numerical study.  
surfaces. Because the thermal conduction coefficients of the metal and the powder differed significantly, the metal powder was set accordingly for each interface with a thermal conduction coefficient of 3.8 \(W\)/mm\({}^{2}\)\({}^{\circ}\)C and 7.63 \(W\)/mm\({}^{2}\)\({}^{\circ}\)C for the metal. On the upper surface, the thermal energy changed because of convection; however, the change was negligible because the temperature of the atmosphere was sufficiently high throughout the preheating process.
## 5 Experiments
### Ablation Study  
We conduct ablation experiments on the DuEE dataset under the 5-shot and 10-shot scenarios to investigate the effectiveness of various components of the ICD method. The experimental settings are as follows: \"-replay\" indicates the absence of old-class data replay, \"-replay(representative)\" indicates that old-class data is replayed randomly, rather than with representative samples, \"-replay(confusable)\" indicates that old-class data is replayed randomly instead of with confusable samples, \"-replay(repr&conf)\" means neither representative nor confusable samples are used, resulting in random replay, \"-contrast\" refers to the exclusion of semantic-boundary-smoothing contrastive learning, and \"-distill\" indicates the absence of hierarchical-distillation loss. The results are shown in Table 6.  
* **Effectiveness of Replay**: The inclusion of old-class data in replay increases the final recall rate by 8.41% and 5.70%, respectively, demonstrating a significant effect of storing old data on incremental learning. Furthermore, compared to random replay, selecting representative and confusable samples improves the recall rate of the final sub-task by 4.18% and 2.55%, respectively. In addition, the ablation of either component leads to a decrease in the final recall rate, while the simultaneous ablation of both components results in an even further decline. This indicates that each component plays a positive role, and, when used together, they produce additional benefits, highlighting the fact that constructing replay samples from these two perspectives adds more value.  
Figure 4: _Recall@3_ performance of every sub-task on ACE2005-Chinses and DuEE for comparative experiment.  
* **Effectiveness of Contrastive Learning**: Our method improves the final recall rate by 5.68% and 4.10%, compared to the removal of contrastive learning. This suggests that, in the context of few-shot incremental learning, using contrastive learning with spans around gold triggers provides additional information to the model, thereby improving performance.
* **Effectiveness of Hierarchical Distillation**: Compared to the exclusion of hierarchical distillation, our method achieves an increase in final recall rate by 1.71% and 2.06%, respectively. This relatively modest improvement indicates that while distillation loss helps retain original knowledge, its impact is somewhat limited.
## 2 Flow Solution  
### Governing Equations  
Our flow model is based on a compressible form of the RANS equations where an assumption is made on the homogeneity of the fluid mixture among the different phases involved in the computation [23; 24]. Including the energy equations for the solution allows prediction of the correct acoustic signal speeds. The governing continuity and momentum equations are  
\[\begin{split}\frac{\partial\alpha_{k}\rho_{k}}{\partial t}+\
abla \cdot\alpha_{k}\rho_{k}\mathbf{V}&=\Gamma_{k},\\ \frac{\partial\rho\mathbf{V}}{\partial t}+\
abla\cdot\rho\mathbf{ V}\mathbf{V}+\
abla p&=\
abla\cdot\mathbf{\tau}_{ij}+\rho \mathbf{g},\end{split} \tag{1}\]  
where \(p\) is the pressure, \(\mathbf{V}\) the absolute velocity in a global non-rotating coordinate system, \(\mathbf{\tau}_{ij}\) the stress tensor, \(\alpha_{k}\) a void (volume) fraction of phase \(k\), \(\rho_{k}\) the density, \(t\) the time, \(\Gamma_{k}\) the mass-transfer term, and \(\mathbf{g}\) the gravity vector. The void fraction is defined as \(\alpha_{k}=\mathcal{V}_{k}/\mathcal{V}\), where \(\mathcal{V}_{k}\) denotes the volume occupied by phase \(k\) of the total volume, \(\mathcal{V}\). For the mass transfer, \(\sum_{k}\Gamma_{k}=0\) holds, and consequently only a single mass-transfer term is needed. The energy equations for phase \(k=g\) or \(l\), the indices referring to gas (\(g\)) and liquid (\(l\)) phases, are written as  
\[\frac{\partial\alpha_{k}\rho_{k}(e_{k}+\frac{V^{2}}{2})}{\partial t }+\
abla\cdot\alpha_{k}\rho_{k}(e_{k}+\frac{V^{2}}{2})\mathbf{V}=\] \[-\
abla\cdot\alpha_{k}\mathbf{q}_{k}+\
abla\cdot\alpha_{k}\tau_{ ij}\cdot\mathbf{V}+q_{ik}+\Gamma_{k}(h_{\mathrm{Issat}}+\frac{V^{2}}{2})+ \alpha_{k}\rho_{k}\mathbf{g}\cdot\mathbf{V}. \tag{2}\]  
Here, \(e_{k}\) is the specific internal energy, \(\mathbf{q}_{k}\) the heat flux, \(q_{ik}\) interfacial heat transfer from the interface to phase \(k\), and \(h_{\mathrm{Issat}}\) saturation enthalpy. The interfacial heat transfer coefficients are based on the mass-transfer model by assuming a saturated temperature for the gas phase, and the heat transfer is then determined by the mass-transfer model. In the case of a homogeneous flow, the resulting sound speed \(c\) for a two-phase mixture is obtained from the eigenvalues of the Jacobian of the flux vector as  
\[\frac{1}{\rho c^{2}}=\frac{\alpha}{\rho_{g}c_{g}^{2}}+\frac{1-\alpha}{\rho_{l }c_{l}^{2}}\;\;\text{and}\;\;\;\frac{1}{c_{k}^{2}}=\frac{\partial\rho_{k}}{ \partial p}+\frac{1}{\rho_{k}}\frac{\partial\rho_{k}}{\partial h_{k}}. \tag{3}\]  
In the expressions above, \(h_{k}\) denotes the enthalpy of phase \(k\).  
The momentum and total continuity equations in the homogeneous model do not change, except for the material properties like density and viscosity. They are calculated for the mixture as a weighted sum by the phasic volume fractions. The turbulence effects are those of single-phase models for the mixture with material properties derived from the pressure and temperatures of the individual phases. The determination of the material properties is described in [25].
## 6 Discussion and Future Works
### Future Works  
The performance analysis for the proposed fuzzy control-based self-adaptive system has been done only in an outdoor scenario with just one arbitrary deployment. Other scenarios, either indoor or outdoor, should be explored in further works, including also different deployment schemes. The system's performance results obtained should then be compared to the results shown in this paper.  
Furthermore, in the proposed system, we have relied on the use of fuzzy functions for the decision-making blocks of the controller. These two functions operate in a similar way, as described in the system description: they both use one input and generate one output. It may be of interest to explore the use of more inputs, in particular in the case of the primary loop function of decision making. In this case, we were using the error on the node degree \(e_{ND}\) as an input once it has been normalized as \(e_{1}\). If for instance we add the previous communication range variation rate (\(\Delta cr(k-1)\)) as an input, could it be used to help in the reduction of the oscillations? If so, could this eliminate the need for the use of a tolerance value like \(\xi_{ND}\)?  
Figure 15: Network packet delivery ratio.  
As we are talking about the functions of decision-making, although we have explored the use of fuzzy logic-based functions, the self-adaptive system is open to the use of any other kind of function or mechanism for this task. For instance, it can be worth exploring the use of genetic and evolutionary algorithms for decision-making [35], multi-criteria decision-making methods [36] or any other one. The performance and system impact for these other functions should also be analyzed.  
The analysis presented in this paper has also provided a configuration for the parameters of the self-adaptive system that give really good performance results. However, we cannot affirm that this configuration is the best one or even if there is a best one. The use of machine learning techniques to identify the best or the fittest parameter set is also another path for further research. It can also be done taking into account that the configuration can be different for each node in the network, depending on its own context and environmental conditions.  
Finally, we have noticed that there are oscillations in the node degree even when there is no change in the transmission power of any node in the network. When this oscillation is within the tolerance parameter, the network can remain in the steady state. However, there is a chance that this oscillation can introduce errors in the system, causing it to enter into an active adaptation cycle. We have considered that these oscillations in the node degree can be due to changes in the environmental conditions. Additionally, these changes cause more oscillations with respect of those nodes that are located in the limit of the communication range of their neighbors. The following subsection proposes an idea of a possible workaround to cope with this problem.
# On-Board Wind Scatterometry
## 4 Experiment and Results
### Data Description  
An L0 product from CFOSCAT that was received from the ground station was employed for the experiment to validate the proposed on-board processing method. CFOSCAT is the first rotating fan-beam scatterometer ever in space [43]. It was launched in October 2018 onboard CFOSAT. The data employed for this research were from 14 th March 2019. Observations were obtained from 9:30 to 11:05, corresponding to a track of data received in the ground station. For the validation of the proposed method, accuracy evaluation was achieved by a comparison made with the ground-processed products [44].
## 3 Applications of IL-Based Gels
### Battery  
The application of IL-based gel as a nonaqueous electrolyte and/or separator in batteries has been widely reported, especially in 2020; for example, in aluminum (Al) batteries, sodium (Na) batteries, calcium (Ca) batteries, organic batteries and, most of all, lithium (Li) batteries. Benefits such as a wide electrochemical window and relatively high ionic conductivity are the basic reasons for ionic liquid gel (ILG) composite modifications widely used as electrolytes in batteries [25]. Alongside liquid electrolytes, the prospect of IL-based gel applications in solid electrolytes has also been characterized and explored by [PERSON] et al. [27] who analyzed IL-based gel electrode interfaces in Li battery. The stretching properties that benefit electronic devices have also been the focus of research [74]. The general concept for using IL-based gels is as a cathode-anode interaction link.  
The role of IL-based gels as electrolytes in batteries can be categorized into gel polymer electrolyte (GPE) or organic ionic gel (OIG), the latter being a new demonstration in 2020 by [PERSON] based on their previous report [75; 76; 77] in which he tested Li batteries and studied ion interactions and aggregation based on the effect of ionic conductivity on the liquid-solid interface of IL-based gels by optical spectroscopy and fast-field cycle diffusometry (FFC), nuclear magnetic resonance (NMR), and gradient pulsed (PFG) NMR methods. The difference between GPE and OIG is presented in Table 1. Further, [PERSON] introduced a glucopyranadise-derived monosaccharide that can bind a large amount of ionic liquid (IL) as a low molecular weight gelator (LMWG) in a basic IL-based gel system that is then dissolved in a mixed ionic solution, which may contain various types of ions or other charge carriers [77]. This dissolution requires the help of high temperatures to ensure everything is mixed successfully, but when all the components come together, it is enough to cool this mixture and turn it into a gel.
## 3 Collision Avoidance Model
### Constraints of Collision Avoidance Process  
When the USV is sailing in the sea, it is usually necessary to follow certain rules to avoid the collision risk between the USV and obstacles. In the International Regulations for Preventing Collisions at Sea (COLREGs) formulated by the International Maritime Organization (IMO), the rules for avoiding collisions are given in the case of head-on, crossing and overtaking. Figure 5 is the schematic diagram of the three encounter situations.  
In Figure 5, the USV sails from the starting point to the terminal point, and the blue vessel is the obstacle vessel encountered, in which the vessel shape with a solid edge represents the current position of the obstacle vessel, and the vessel shape with a dashed edge represents the position of the obstacle vessel at the last moment, from which the sailing trend of the obstacle vessel can be seen. The course of the USV is the sailing direction, and the course difference between the USV and the obstacle vessel starts from the USV course. The rules are described as follows:  
1. Overtaking situation: when the course difference between the USV and the obstacle vessel is within [0\({}^{\circ}\), 45\({}^{\circ}\)] and [315\({}^{\circ}\), 360\({}^{\circ}\)], if the velocity of the USV is higher than that of the encountering vessel, then the USV turns left to pass the obstacle vessel.
2. Head-on situation: if the course difference is within [165\({}^{\circ}\), 195\({}^{\circ}\)], the USV turns right to avoid the obstacle vessel.
3. Crossing situation: if the course difference is within [45\({}^{\circ}\), 165\({}^{\circ}\)], the obstacle vessel crosses on the starboard side of the USV, then the USV turns right; if the course difference is within [195\({}^{\circ}\), 315\({}^{\circ}\)], the obstacle vessel crosses on the port side of the USV, then the USV turns left.
## 3 Current Decoupling Control Based on ITHESO for Torque Ripple Reduction  
### Torque Ripple Factor in the VFRM  
In VFRM, the position-dependent torque ripple does not necessarily stay aligned with the sine (\(3\theta_{\mathrm{e}}\)) term alone. Due to mechanical or magnetic phase shifts introduced by factors such as rotor geometry, magnetic saturation, or misalignment, it is often observed that the torque ripple has both sine and cosine components.  
Consider a phase-shifted version of the torque ripple. A sine function can be represented as a combination of a sine and cosine function using a phase shift according to [PERSON]'s formula  
\[e^{\mathrm{j}\phi}=\cos(\phi)+\sin(\phi) \tag{4}\]Thus, for any angle \(\alpha\):  
\[\begin{array}{l}\sin(\alpha)=\frac{e^{i\alpha}-e^{-i\alpha}}{2i}\\ \cos(\alpha)=\frac{e^{i\alpha}+e^{-i\alpha}}{2j}\end{array} \tag{5}\]  
The term \(\sin(3\theta e+2\beta)\) can be expanded using the trigonometric identity as:  
\[\sin(3\theta_{e}+2\beta)=\sin(3\theta_{e})\cos(2\beta)+\cos(3\theta_{e})\sin(2\beta) \tag{6}\]  
Thus, the second term in Equation (3), which represents the torque ripple component, can be rewritten as:  
\[T_{e,ripple}=\frac{3}{8}N_{t}L_{\delta}\Big{(}\dot{\frac{i}{d}}+\dot{\frac{i}{d }}\Big{)}(\sin(3\theta_{e})\cos(2\beta)+\cos(3\theta_{e})\sin(2\beta)) \tag{7}\]  
This form reflects that the ripple component has both sine and cosine terms due to the advanced current angle \(\beta\). Physically, this means that depending on the value of \(\beta\), the torque ripple will exhibit a phase shift that combines both sine and cosine components.  
The angle \(\beta\) is related to the ratio between the d-axis and q-axis currents, which determines the phase shift in the current vector of the VFRM. Specifically, \(\beta\) can be calculated as:  
\[\beta=\tan^{-1}\left(\frac{\dot{\frac{i}{d}}}{\dot{\frac{i}{d}}}\right) \tag{8}\]  
when \(i_{\rm d}=0\), \(\beta=0\), and the torque ripple component simplifies to a pure sine function \(sin(3\theta_{e})\) without any cosine component.  
For non-zero \(i_{\rm d}\), \(\beta\) introduces a phase shift, causing both sine and cosine terms to appear in the torque ripple component.  
Given this, we can write the final form of the torque ripple component as:  
\[T_{e,ripple}=\frac{3}{8}N_{t}L_{\delta}\Big{(}\dot{\frac{i}{d}}+\dot{\frac{i}{d }}\Big{)}\left(\sin(3\theta_{e})\cos\left(2\tan^{-1}\left(\frac{\dot{\frac{i}{ d}}}{\dot{\frac{i}{d}}}\right)\right)+\cos(3\theta_{e})\sin\left(2\tan^{-1} \left(\frac{\dot{\frac{i}{d}}}{\dot{\frac{i}{d}}}\right)\right)\right) \tag{9}\]  
This expression now accounts for both the sine and cosine components, with coefficients that depend on \(i_{\rm d}\) and \(i_{\rm q}\) through the advanced current angle \(\beta\).  
This decomposition shows that the torque ripple is a position-dependent periodic disturbance with both sine and cosine terms due to the phase shift \(\beta\) introduced by the d-axis current. The magnitude and phase of the ripple are influenced by the dq-axis currents, so it is possible to control the ripple by adjusting these currents.