esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the performances of LPPT control for an SSWT were investigated. Three different LPPT control power strategies for an SSWT were proposed and tested in order to compare their characteristics and study the effect of some sudden variations (i.e., power variation and weather condition change) on their performances. The proposed methods are the fixed step size, [PERSON]'s method and the FL technique. All these methods are based on the P&O principle. The results show that whatever method is used, the system operates correctly, reaches LPPs, and identifies the direction of the next perturbation. However, what changes are the performances of each method. At first, the results differ according to the perturbation step size used. Indeed, for the fixed step size and [PERSON]'s methods, a large step size contributes to a faster dynamic response but excessive steady-state oscillations, which result in a low efficiency. This situation is reversed when the LPPT is running with a smaller step size. Thus, the LPPT with a fixed step size and variable step size based on [PERSON]'s methods should make a satisfactory tradeoff between the dynamics and oscillations. Regarding the FL method, it has better dynamic performances with more steady oscillations compared to the fixed step size and [PERSON]'s methods. In addition, it is also clearly demonstrated that all methods react correctly during a sudden change of power and wind velocity. They change in an identical way in terms of  
Figure 19: Drop in wind velocity: (**a**) \(p_{WT}\) based on \(v_{WT}\); (**b**) evolution of \(p_{WT}\) in time.  
Figure 18: Wind velocity profile.  
the response time. The FL technique stabilizes quickly compared to the fixed step size and [PERSON]'s methods, both in power increase or decrease and in rapid change of wind velocity. This method is also interesting for the control of an SSWT in a DC MG. Indeed, it will guarantee less fluctuation in the generated power that can be injected into the public grid.  
Future work will concern an effective integration of the SSWT in a DC MG with the adaptation of the proposed control strategies to the constraints linked to the management of power flow in this electrical structure.  
[PERSON] designed the research. [PERSON] performed the research, analyzed the data and wrote the paper. [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### Network Backbone
#### 3.4.3 Equivariant Layers  
The equivariant layer contains two layers; one is the equivariant feature extraction layer, and the other is the equivariant token embedding layer. These equivariant linear, nonlinear, and normalized layers are combined into the VN-MLP. Both the linear vector layer and the normalization vector layer are linear transformations that preserve the original directionality of the vector features. As a result, equivariant transmission between the two layers can be ensured after vectorization. However, the activation function in the nonlinear layer requires special treatment. The traditional ReLU function is an activation function for scalar features which does not apply to the vector features. This paper adopts a linear vector layer to predict the direction vector \(k\) that the vector \(V\) needs to activate. Then, the vector \(V\) is decomposed along the vector \(k\) to obtain a component vector \(q_{1}\) which is parallel to the direction vector \(k\) and \(q_{2}\), which is perpendicular to the vector \(k\). Then, the activation operation is performed on \(q_{1}\), and the vector ReLU is expressed as follows:  
\[V^{\prime}=\begin{cases}V,\ \ \ if\langle q_{1},k\rangle\geqslant 0\\ V-\langle q_{1},\frac{k}{\|k\|}\rangle\frac{k}{\|k\|},\ otherwise\end{cases} \tag{16}\]  
These vector layers are serially combined in the traditional structure of the MLP and finally stacked to form the VN-MLP. We denote the feature extraction network, the VN-MLP, as \(f_{mlp}\) and the embedding layer for vector token as \(f_{emb}\), so that we have  
\[f_{emb}(f_{mlp}(XR))=f_{emb}(\mathcal{V}R)=\mathcal{V}^{\prime}R\] \[=f_{emb}(\mathcal{V})R=f_{emb}(f_{mlp}(X))R \tag{17}\]  
where \(X\in\mathbb{R}^{N\times 3}\) is the input point cloud, \(\mathcal{V}\in\mathbb{R}^{N\times C\times 3}\) is the vector feature list after vector initialization, \(\mathcal{V}^{\prime}\in\mathbb{R}^{N\times C^{\prime}\times 3}\) is the vector token, and \(R\in\mathbb{R}^{3\times 3}\) is the ordinary rotation matrix. Here, we can see that the rotation operations that were originally located at the input point cloud are transferred equivariantly to the subsequent vector features. The autoencoder backbone after the vector layers is also equivariant, embedding vector neurons in the Transformer.
## 4 Discussion
### Limitations of the Study  
Finally, it is worth mentioning that although the results obtained are based on objective data extracted from the students' logs, several aspects could affect the conclusions of the study: (1) the sample of the study was limited to the students from a very concrete university and foreign language course, hence hindering the extrapolation of the results obtained; (2) the average age of the sample was limited to students around 20, and thus students felt immediately comfortable with using the digital version; (3) the design of the digital version itself. The fact that _Terminkalender_ was developed following popular instant messaging applications such as _WhatsApp_ or _Telegram_[60] does not imply that the interaction mode chosen is the best for developing the different competencies targeted by the current study; (4) students' linguistic and communicative competencies were solely analyzed in a very limited context, that is, the analysis was based on students' performance of solely one concrete learning task. To draw stronger conclusions from the study, a more longitudinal intervention focusing on one or more learning tasks is needed. Moreover, an additional exam testing both competencies separately, some days after the intervention, might have allowed us to obtain a more precise picture of each of the competencies developed and to draw some stronger conclusions on the pros and cons of the digital version when targeting concrete competencies.
# Thermal Analysis of Radiation Heat Transfer of Improved Fractal Solar Collectors
## 2 Design and Analysis
### Discrete Ordinates Radiation Model
If gray radiation calculation is performed, the walls are considered gray, that is, they are assumed to exhibit the same radiation properties at all wavelengths. However, if the non-gray DO model is used, the walls are considered non-gray, which means that the radiation properties can vary depending on the wavelength. This flexibility allows the DO model to perform realistic simulations considering various radiation problems and different material properties.
## 4 Investigation of Reduction of Gear Meshing Power Loss
### Analysis of Meshing Power Loss under Various Load Conditions  
To study the relationship between meshing power loss/efficiency and working conditions after gear tooth modification, the simulated working conditions were divided into two categories: One is constant torque and speed-up. In these conditions, the maximum torque is maintained at 2000 Nm, while the input speed increases from 200 r/min to 1500 r/min. The other is constant speed and torque-up conditions. Here, the input speed is held constant at 1500 r/min, and the input torque increases from 200 Nm to 2000 Nm. The analysis results for the accumulated power loss of the gear pairs under each working condition are shown in Figure 14.  
The cumulative meshing power loss of 13 gear pairs changes with the rotational speed as shown in Figure 14. It can be seen that the meshing loss increases approximately linearly with the increase of rotation speed. The five gear pairs of the fixed-axis gear train have high power loss in all working conditions. They are arranged in descending order as G1735, G1835, G2340, G3540, and G3536. The power losses of the sun and four planetary  
Figure 14: Gears meshing accumulated power loss under various operation conditions. (**a**) Accumulated power loss with speed variation; (**b**) accumulated power loss with torque variation.  
Figure 13: Gear meshing efficiency under load spectrum. (**a**) Theory tooth surface of G1735; (**b**) optimized tooth surface of G1735.  
gears meshing in the epicyclic gear train are basically equal; the least power loss is the internal ring and the four planetary gears. The cumulative power loss curve of each gear pair changes with the torque as shown in Figure 14b. The meshing loss increases with non-linear acceleration as the torque increases. The order of meshing loss of each gear pair is the same as the speed-up working condition. Under the whole load spectrum, the max meshing power loss of the entire gear system is 14.45 kW and the overall meshing efficiency is 95.4%.  
The curves of meshing efficiency-speed of each gear pair as shown in Figure 15a. It can be seen that the meshing efficiency increases with non-linear deceleration as the rotational speed increases. Under low-speed conditions, the meshing efficiency is lower and the change rate is large, but in high-speed conditions, the rate of change decreases and gradually converges. The changing curves of meshing efficiency with torque are shown in Figure 15b. Although the efficiency curves vary, the overall meshing efficiency decreases with the torque-up. The G1735 gear pair has the largest decline slope for meshing efficiency, followed by the four meshing pairs of the sun gear and the planet gear. However, their efficiency is relatively stable in the low torque range and drops rapidly at medium and high torques. The efficiency-torque curves of the other gear pairs are approximately linear decline.
## 4 Results and Discussion
### Model Case  
The measured FRF of the used mesh elements of the saddle tube is shown in Figure 7. The global resonance frequencies of the bicycle frame are listed in Table 2. The first natural bending resonance frequency occurred at 102.8 Hz. The excitation shaker was fixed in the middle of the frame at the saddle tube. After the model updating routine was performed, the updated material thicknesses of the evaluated points was changed according to the results in Table 4.  
As a result of the updating procedure, an average error of 4.2% in relative difference was achieved. By taking the mode shape in the objective function into account (Equation (7)), three modes were switched in the updating process, demonstrating the usefulness of the mode shapes and not just the resonance frequencies in the objective function. If this value were not included, the wrong frequency value differences for these modes would have been minimized.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \begin{tabular}{c} **Experimental Geometric** \\ **Measured Value (mm)** \\ \end{tabular} & \begin{tabular}{c} **Thermal Model** \\ **Computation (mm)** \\ \end{tabular} & \begin{tabular}{c} **Error (\%)** \\ **Error (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Structural Model** \\ **Computation (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Error (\%)** \\ **Error (\%)** \\ \end{tabular} \\ \hline Saddle tube (top) & 2.64 & 2.53 & \(-4,2\) & 2.68 & 1,5 \\ Head tube (top) & 4.05 & 4.05 & 0,0 & 3.19 & \(-21,2\) \\ Lower tube (middle) & 2.05 & 2.09 & 2,0 & 1.59 & \(-22,4\) \\ Top Tube (middle) & x & 2.28 & x & 2.65 & x \\ Rear Tube (middle) & x & 3.55 & x & 2.21 & x \\ \hline \end{tabular}
\end{table}
Table 4: Optimization results.  
Figure 9: Thermal response of the crack after PCT (Bin 4).
# Sharp Interface Capturing in Compressible Multi-Material Flows with a Diffuse Interface Method
## 5 Modification of the Advection Step: Sharp Interface Capturing (IC) Approach
### Reformulation of the Advection Step with the MLP-UB Method for the Sharp Interface Capturing
#### 5.2.1 Reconstruction of Primitive Form of Scalars Instead of Their Conservative Form  
In the present study, although we work with the conservative form of advection equations for \(\alpha_{\phi},\rho_{\phi},e_{\phi}\) (see Equations (19) and (20)), we perform the reconstruction of primitive scalars \(\alpha_{\phi},\rho_{\phi},e_{\phi}\) with MUSCL followed by the gradient limiting with MLP-UB to get their convective fluxes. This is mainly done to serve two purposes:  
1. As shown by [27; 43; 44], the application of the MUSCL on the primitive form of scalars leads to a more accurate reconstruction at the interface.
2. Reconstructing \(\alpha_{\phi},\rho_{\phi},e_{\phi}\) in their primitive form provides us the freedom to use different types of gradient limiters for \(\alpha_{\phi}\) and \(\rho_{\phi},e_{\phi}\). Here, we prefer to use a compressive flux limiter (MLP-UB in present case) for \(\alpha_{\phi}\), and a less compressive one for \(\rho_{\phi}\) and \(e_{\phi}\) individually. For instance we have used MINMOD [45] type limiter for \(\rho_{\phi}\) and \(e_{\phi}\).  
Further elaborating on point 2 given above, we choose to do so because regular wave solutions shown by \(\rho_{\phi}\) and \(e_{\phi}\) in multi-phase test problems are prone to suffer from _staircase effect_ (demonstrated in Section 5.2.2) when reconstructed with compressive limiters. However, the evolution of \(\alpha_{\phi}\) maintains the initial discontinuous solution, and thus to preserve this sharp discontinuity, we use a compressive limiter like MLP-UB.
## 5 Results  
### Interlaminar Stress Distributions in the Filled-Hole Laminate near the Hole  
A numerical solution of interlaminar stress distributions at the interface in the filled-hole laminate near the hole is shown in Figure 6. Analyses were performed in the condition of two levels of clamp-up load, namely non-tightened bolt case and tightened bolt case. In the former case, the clamping force is assumed as \(F_{0}\) = 10 N, the latter as \(F_{0}\) = 2000 N. The interlaminar shear stress \(\tau_{yz}\) is not present in this paper because of the similar distribution feature of \(\tau_{xz}\). The following remarks can be highlighted:  
1. For the non-tightened bolt case (clamping force \(F_{0}\) = 10 N), interlaminar stress concentrations mainly occur around the hole edge (Figure 6a,b, Position I), and tapers out at the interior of the laminate. This prediction is consistent with the previous phenomenon that laminated composites often exhibit transverse stress concentrations near material and geometric discontinuities (the so-called free-edge effect) [1]. In spite of the bolt, Position I can still be considered as geometric discontinuities, and the free-edge effects are observed here.
2. For the tightened bolt case (clamping force \(F_{0}\) = 2000 N), the results show that the interlaminar stress concentrations in the filled-hole laminate occur at Position I, as well as the interior of the laminate near the periphery of the bolt head (Figure 6c,d, Position II). Note that Position II of the laminate is not a free edge. However, traditionally, the free edge effect is only a concern near the free edge of the laminate, and decay to zero as the distance from the free edge increases. \(\sigma_{z}\) is a negative value in the clamp-up region, in other words, \(\sigma_{z}\) is interlaminar compressive stress. According to Reference [41], the compression interlaminar stress is able to delay the delamination initiation.
3. It should also be noted that the stress concentrations of \(\sigma_{z}\) and \(\tau_{xz}\) near Position II are not in the same circumference locations. The former at Position II is shown in Figure 6c,d. The latter is located near Position II, which is away from the hole in the in-plane direction, this position in the laminate is defined as Position III, as presented in Figure 6d.  
Figure 6: Stress distributions at the interface in the filled-hole laminate near the hole: (**a**) Interlaminar normal stress \(\sigma_{z}\), with non-tightened bolt; (**b**) Interlaminar shear stress \(\tau_{xz}\), with non-tightened bolt; (**c**) \(\sigma_{z}\), with tightened bolt; (**d**) \(\tau_{xz}\), with tightened bolt. 1—Bolt; 2—Interface; 3—Quarter clamp-up region; I—The hole edge; II—The projection boundary of the periphery of the bolt head on the laminate.
## 4 Discussion
### Implications, Challenges, and Future Works  
The proposed framework was also applied at three other workspaces with distinct attributes, including a laboratory, a factory, and a hospital (Appendix B). Each site produced continuous data monitoring stored in a central time-series database and can be visualized on an accessible dashboard in a format that is easily understandable users.  
In the process of repeating the monitoring study and applying the provided framework, we have demonstrated the best practice in integrating different proprietary IoT platforms using open-source software to unify the data structure. An added benefit with the selected software is the no vendor-lock in issue. This allows users to swap in or out of any IoT devices as long as an API is provided for accessed and exporting the data. Tomaintain a generic framework that is scalable, versatile, and avoids vendor lock-in, this paper demonstrated the purchase of sensors from different manufacturers and have data exporting and streaming capability.  
To elaborate on the importance of understanding the site and selecting a suitable communication protocol, during the factory monitoring study, the SenseCap Barometric Pressure sensors experienced interference from the sensing device to the gateway. Upon further investigation it was determined that the machinery was generating interference at the same frequency (915 MHz). The ultimate result was the inability to utilize the pressure sensors at this site.  
The IoT framework and data ingest model are intended to form the foundation of any indoor environmental monitoring study. Once the data are collected, they can be utilized for many different purposes. The data can be used for further analysis to determine harmful IAQ parameter sources. However, this kind of analysis could be the focus of a subsequent study.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Utilization of Model Uncertainty during Inference
#### 4.3.1 Comparison with the State of the Art
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Uncertainty**} \\  & \multicolumn{2}{c}{**Results**} & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Inference**} \\
**Approach** & **Reported In** & **mAP [\%]** & **rank-1 [\%]** & **M** & **D** & **V** & **M** & **D** & **V** \\ \hline AWTL baseline [73] & [22] & \(71.45\) & \(84.89\) & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\
**Bayesian Retrieval**[22] & [22] & \(72.19\) & \(85.87\) & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline ResNet-50 baseline & [28] & \(75.65\pm 0.21\) & \(90.48\pm 0.45\) & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\
**DistNet**[51] & [28] & \(74.61\pm 0.06\) & \(90.10\pm 0.29\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ PFE [21] & [28] & \(76.49\pm 0.23\) & \(90.48\pm 0.51\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ DUL [31] & [28] & \(77.12\pm 0.19\) & \(90.09\pm 0.17\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ MEIB [28] & [28] & \(79.67\pm 0.15\) & \(92.14\pm 0.16\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline IDE baseline [74] & [52] & \(78.4\) & \(89.7\) & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ LPP [52] & [52] & \(80.5\) & \(91.2\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ \hline BOT baseline [66] & & \(86.42\pm 0.09\) & \(94.53\pm 0.21\) & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\
**DistNet**[51] & & \(83.95\pm 0.20\) & \(93.44\pm 0.28\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ PFE [21] & & \(86.42\pm 0.09\) & \(94.45\pm 0.20\) & ✗ & ✓ & ✗ & ✗ & ✗ & ✗ \\ UAL [25] & & \(86.77\pm 0.14\) & \(94.64\pm 0.21\) & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ \\ UBER (ours) & \(\mathbf{87.21\pm 0.17}\) & \(\mathbf{94.67\pm 0.18}\) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with state-of-the-art re-identification approaches that employ uncertainty estimation. The benchmarking dataset was Market-1501 [68]. For each approach, the types of uncertainties employed are listed. Figure 5: Comparison with state-of-the-art re-identification approaches that employ uncertainty estimation as box plots over the results of 20 runs based on the BOT baseline [66]. The benchmarking dataset was Market-1501 [68]. 20 runs outperform the best of the 20 trained UAL models. The mean improvement of our approach UBER over UAL is higher than the improvement of UAL over the already strong baseline BOT. The highest mAP score achieved by the best of our trained UBER models across the 20 runs using an individually tailored constant \(c\) (see Section 4.3.4) was 87.52. Below, a detailed explanation of the methodology employed to incorporate uncertainties into the refinement of the feature vector is provided, along with a justification for the selection of model uncertainty as the optimal type of uncertainty to be applied.
## 3 Solution Approach  
### Lagrange Relaxation  
We propose a Lagrange relaxation approach for the CLSP [39; 40]. Let \(u_{t}\), \(t=1,\ldots,T\), be nonnegative Lagrange multipliers for capacity constraints (3); then, a Lagrange relaxation problem (\(LR\) (\(u\))) can be obtained as follows.  
\[\begin{array}{ll}LR\;(u):&z_{u}=min\;\sum\limits_{t=1}^{T}\sum\limits_{i=1} ^{I}(p_{it}x_{it}+h_{it}I_{it}+s_{i}y_{it})+\sum\limits_{t\in T}u_{t}(\sum \limits_{t}a_{i}x_{it}-C_{t})\\ &=\sum\limits_{t\in T}\sum\limits_{i\in I}((p_{it}+u_{it})x_{it}+h_{it}I_{it}+ s_{i}y_{it})-\sum\limits_{t\in T}u_{t}C_{t}\\ &s.t.\\ &(2),(4)-(6),\;u_{t}\geq 0,t\in T.\end{array}\]  
The \(LR\) (\(u\)) problem can be then decomposed into \(|I|\) independent uncapacitated single-item lot-sizing problems, each of which is solvable in \(O(T^{2})\)[25], and any of the previously mentioned single-item problem methods--for example, lot-for-lot, LUC, W-W dynamic programming, etc.--can be used to this aim. In this paper, W-W dynamic programming is chosen. Note that, for any given \(u\), the solution of the \(LR(u)\) will give a lower bound \(z_{LB}\) for the original problem (CLSP).
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 2 Principle of Operation of the Ship Central Cooling System  
This paper used the central cooling system of a certain type of bulk carrier as the research object. In this system, low-temperature fresh water, cooled by the central cooler, is regulated by the low-temperature fresh water three-way valve (hereinafter referred to as the \"three-way valve\") to precisely control its temperature at 36 \({}^{\circ}\)C. Subsequently, the low-temperature fresh water is transported by the cooling pump to various heat load equipment for heat exchange and finally returns to the central cooler to complete the cooling cycle (Figure 1).