esa-sceva/satcom-chunk-collection huggingface dataset:
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
## 3 Energy Analysis
The terms in Equation (4) quantify the different forms of energy:  
\[W_{s} =-\int_{0}^{t}\mathbf{u}_{d}\cdot\int_{\mathcal{A}_{d}}\Bigl{[}p-\mu \Bigl{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Bigr{)}\Bigr{]}\mathbf{n}dAdt= \int_{0}^{t}\mathbf{u}_{d}\cdot\mathbf{F}dt \tag{5}\] \[E_{Pote} =-\int_{0}^{t}\bar{\mathbf{u}}_{a}\cdot\int_{V}\rho gdVdt\] (6) \[E_{Kine} =\int_{\mathcal{V}}\frac{1}{2}\rho\bar{\mathbf{u}}_{a}\cdot\bar{\mathbf{ u}}_{a}dV\] (7) \[E_{Defo} =\int_{0}^{t}\bar{\mathbf{u}}_{a}\cdot\int_{\mathcal{V}}\Bigl{[} \frac{\partial\rho\mathbf{u}}{\partial t}+\mathbf{\
abla}\cdot(\rho\mathbf{u}\otimes\mathbf{ u})+\rho\mathbf{a}_{d}\Bigr{]}dVdt-E_{Kine}\] (8) \[E_{visc} =-\int_{0}^{t}\bar{\mathbf{u}}_{a}\cdot\int_{V}\mathbf{\
abla}\cdot\mu \Bigl{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Bigr{)}dVdt+W_{s_{u}}\] (9) \[E_{ST} =-\int_{0}^{t}\bar{\mathbf{u}}_{a}\cdot\int_{V}\gamma\kappa\mathbf{ \
abla}adVdt \tag{10}\]  
where \(\mathbf{F}\) is the force from the tank onto the liquid and \(W_{s_{u}}\) is the domain boundary viscous component included in (5). Therefore, the force is computed via the following expression,  
\[F_{Slosh}=-\int_{\mathcal{A}_{d}}\Bigl{[}p-\mu\Bigl{(}\mathbf{\
abla}\mathbf{u}+\mathbf{ \
abla}\mathbf{u}^{T}\Bigr{)}\Bigr{]}\mathbf{n}dA \tag{11}\]  
which is used for the CFD load calculations. The reason for subtracting the kinetic energy term from the first integral in (8) is to account for the liquid impact related losses (we assume initial fluid velocities of \(\mathbf{u}_{a}^{0}=0\)). These impact losses (liquid-liquid or liquid-wall) have been shown [23; 24] to be accounted for by the liquid incompressible pressure-poisson fractional step [17] method employed in our solver. This is consistent with the accuracy achieved in the preceding section in computing slosh induced energy dissipation. Note that liquid impact was found to account for the dominant source of energy dissipation in this work. From the above, it is instructive to note that \(E_{Disp}\) may also be approximated from experimentally measured data as follows:  
\[E_{Disp} =\int_{0}^{t}\mathbf{u}_{d}\cdot\mathbf{F}dt-E_{Kine}-E_{Pote}\] \[\approx\int_{0}^{t}\mathbf{u}_{d}\cdot\mathbf{F}dt-\frac{1}{2}m_{f} \mathbf{u}_{d}\cdot\mathbf{u}_{d}-\int_{o}^{t}m_{f}\mathbf{u}_{d}\cdot gdt \tag{12}\]  
where \(m_{f}\) is the fluid mass in the tank and \(\mathbf{F}\) is the force of the tank on the fluid (which may be inferred from load cell measurements). This is provided that accurate tank position (displacement) data are available and noting it was found that \(E_{ST}\) accounts for less than 1% of the energy budget.
## 4 Discussion
### Mesh Convergence  
Although in the literature, the displacement [78] and strain energy [52, 76] are also used as parameters of mesh convergence, the von Mises stress was chosen since mesh resolution is more sensitive to internal mechanical parameters than displacement [52]. Based on the results, the PSM is more sensitive to the mesh resolution in terms of stress due to its element-based material assignment strategy (Figure S3). In contrast, in the LBM, relatively minor stress changes occurred due to the homogeneous modelling of different anatomical sections. Both the LBM and PSM gave results less than 10% at 1 mm element size, agreeing with the findings of [PERSON] et al. [11], who applied an identical imaging protocol and similar bone material assignment strategy. The required computational time of the models with the finest mesh increased significantly compared to the 1 mm results, which also reinforced the choice of the 1 mm element since the elevated computational demand does not couple with higher accuracy (Figure S3).
## 4 Conclusions  
In this study, the stress response under the most unfavorable environment and axial load is calculated by establishing finite-element models under different axial loads, and the environmental effects are evaluated. The main conclusions are as follows:  
1. The temperature stresses in each structural layer show regular variations, with complete tensile stresses in winter and alternating tensile and compressive stresses in spring and fall.
2. The winter temperature difference is more serious than the fall and spring temperature differences in terms of damage to the pavement, and since the maximum tensile stress on the road surface in winter reaches 1.0228 MPa, the pavement is very susceptible to cracking due to tension.
3. The temperature-load coupling stress varies periodically, and the temperature stress has less influence on the coupling stress in spring and fall and more influence in winter.
4. Under traveling load, the coupling stress and its influence range decrease with the increase in depth, while the farther away from the wheel load position, the smaller the traveling load disturbance. The maximum coupling stresses appear directly below the wheel loads under single-axle and double-axle loads and below the wheel loads on both sides under triple-axle loads.
5. Under the most unfavorable loading conditions, the degree of influence of environmental effects along the depth direction gradually decreases in winter, and it first decreases and then increases in spring and autumn. Overall, the difference in the degree of environmental effects along the depth direction under different axle loads is not significant, which is 70.53%, 41.90%, 27.13%, and 23.77%, in order.  
Figure 16: Fatigue life under different combinations of axle loads.  
**Author Contributions:** Conceptualization, Y.G.; Data curation, Z.L.; Methodology, Y.L.; Funding acquisition, L.Z. and Y.G.; Project administration, Y.G. and Y.D.; Resources, Y.D.; Writing--original draft, Z.L. and Y.L.; Supervision, B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors gratefully acknowledge the financial support received from the Innovation Group in Fundamental Research (25)RRA145), Ningxia Hui Autonomous Region Key R&D Program (2023 BEG02044), the National Natural Science Foundation of China (52468065), the Industry Support and Guidance Project by University and College in Gansu Province (2020C-13), and the Gansu Science and Technology Major Project (22 ZD6 GA010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Opportunities for Machine Learning in District Heating
## 2 District Heating Networks
### Operational Challenges in Adapting District Heating Networks
#### 2.1.2 Heat Distribution  
The distribution side consists of the piping network, booster pump installations, thermal storage systems and heat transfer stations between primary and secondary networks. All of these can create inefficiencies in a DH network. The distribution side is often a black spot for DH network operators, since only limited data are collected. Furthermore, as long as there are no heat transfer stations, thermal storage or booster pump stations present, as often is the case for small and medium-sized networks, no control points are present in the distribution networks. In that case, optimal control of the distribution side is not relevant. In case one or more of the mentioned installations are present, the control can be optimised in the same way as described in the production side section. Furthermore, as an advanced form of thermal energy storage, the temperature in the supply pipes of the network could be temporary increased at times, resulting in the use of the network pipes themselves as thermal energy storage.  
Analytics is seen as more important in the distribution side. Detection of leakages or insulation faults in the piping system remain a major source of inefficiencies and will gain importance over time. Simulation-based analysis can be used to detect malfunctioning components in the network, whereby measured behaviour is compared to simulated behaviour, and an alarm is raised once the difference exceeds a certain threshold. Since each network has its own topology, it is very complicated to build a simulation model for each individual network, especially for large networks. ML could have great potential in this field, by building automated, black- or grey-box data-based simulation models as an alternative.
## 2 Astronomical Parameters
### Transit-Spectroscopy Parameters
The noise and SNR [16] on \(A_{pt}(\lambda)\) of single transit in any spectral bin can be, respectively, approximated by  
\[\sigma_{A_{pt}}(\lambda)=\frac{\sqrt{2t}}{\sqrt{T_{14}}}\frac{\sigma_{\rm OOT }(\lambda)}{S_{\rm OOT}(\lambda)} \tag{4}\]  
and  
\[SNR_{A_{pt}}(\lambda)=\frac{A_{pt}(\lambda)}{\sigma_{A_{pt}}(\lambda)}=\frac{A _{pt}(\lambda)\sqrt{T_{14}}}{\sqrt{2t}}SNR_{\rm OOT}(\lambda), \tag{5}\]  
where \(S_{\rm OOT}(\lambda)\) represents the total observed signal of single exposure at the wavelength of \(\lambda\) in the OOT period, \(\sigma_{\rm OOT}(\lambda)\) is the noise on \(S_{\rm OOT}(\lambda)\), \(SNR_{\rm OOT}(\lambda)\) is the corresponding signal-to-noise ratio, and \(t\) is the single exposure time. \(T_{14}\) is the transit time in Figure 1a, defined as the planet traveling duration from ingress to egress  
\[T_{14}=\frac{p_{r_{s}}}{\pi a}\sqrt{\left(1+\frac{r_{p}}{r_{s}}\right)^{2}- \left(\frac{a\cos i}{r_{s}}\right)^{2}}, \tag{6}\]  
where \(i\), \(a\), and \(P\) are the planetary orbital inclination, semimajor axis, and period, respectively [17]. The value of \(SNR_{A_{pt}}\) is generally too small to meet the requirements, so it is necessary to combine multiple transit observations to realize targets. In order to achieve a goal SNR, the number of transits is given by  
\[N_{transit}=\left(\frac{SNR_{equal}}{SNR_{A_{pt}}}\right)^{2}. \tag{7}\]  
These are also applied to parameter estimation in a secondary eclipse, of which the substituted atmospheric contribution and SNR are expressed as \(A_{se}(\lambda)\) and \(SNR_{A_{se}}(\lambda)\),  
\[A_{se}(\lambda)=\frac{B_{\lambda}(T_{p})}{B_{\lambda}(T_{s})}\bigg{(}\frac{r_ {p}}{r_{s}}\bigg{)}^{2}+\rho\bigg{(}\frac{r_{p}}{a}\bigg{)}^{2} \tag{8}\]  
and  
\[SNR_{A_{se}}(\lambda)=\frac{A_{se}(\lambda)}{\sqrt{2}}SNR_{\rm OOT}(\lambda), \tag{9}\]  
where \(B_{\lambda}(T_{p})\) and \(B_{\lambda}(T_{s})\) are the Planck function for the planet and star, respectively, and \(\rho\) is the geometric albedo.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 4 Experiments and Results
### Experimental Setup  
All layers of the core model (ResNet50) were frozen, and the weights of classification layers and the incorporated blocks (SE, CA, or NL) were initialized by the Lecun initializer. We used the Adam optimizer with a mini-batch size of 100 and a learning rate of 0.0001. The models were then fine-tuned where all layers were unfrozen, and the learning rate was set to \(1\times 10^{-6}\). The proposed system was implemented and evaluated in the Python programming language using the Keras library in the Google Colab cloud service. It was tested against two datasets. The first one was used in [19], was collected from Google, and contains 317-584 images per class (2764 images total) of size \(224\times 224\). The number of clothing pattern designs in this dataset is six: solid, striped, checkered, dotted, zigzag, and floral. The other dataset was CCYN [16], which includes 627 images of four clothing pattern designs: plaid, striped, patternless, and irregular, with 156, 157, 156, and 158 images in each category. The resolution of each image is \(140\times 140\). Both datasets were split into 60%, 20%, and 20% for training, validation, and testing, respectively. To overcome the overfitting problem due to insufficient training datasets, the simplest approach was to employ geometric transformations to generate additional samples. The geometric transformations specifications shown in Table 2 were applied.
## 3 AD and PD and Bioactive Rosemary Compounds with Health-Promoting Effects
### Carnosic Acid
#### 3.5.2 In Vivo Health-Promoting Effects of Camosic Acid against AD and PD  
Taking a look into the in vivo studies on CA, a transgenic OW13 Caenorhabditis elegans model of PD was examined. The outcome suggested that a dose-dependent reduction of CA in human \(\alpha\)-syn aggregation was found in the roundworm muscle cells. In the same study, in ROS-treated rats, the immunoblots displayed the same results as in the cells, meaning that the diterpene attenuated the upregulation of ubiquitinated proteins and reduced PINK1 and Parkin protein levels [16]. Rats administered CA before exposure to  
Figure 3: Stimulation of the Nrf2 pathway to induce phase II gene activation by CA pro-drugs like catechol. Catechol is at first transformed to a CA congener called quinone, an active electrophilic drug, which undergoes a nucleophilic attack in its electron deficient carbon by the Keep1 target protein that carries the Nrf2 factor. This factor consequently translocates to the nuclei where phase II gene activation occurs.  
6-OHDA showed an enhancement of locomotor activity and reduction in the apomorphine-induced rotation. Also, the rats pretreated with CA showed increased defenses against the lipid peroxidation and GSH downregulation caused by 6-OHDA. Lastly, like in the in vitro experiment in the same study, pretreatment with CA led to a rise in the levels of the \(\gamma\)-glutamate-cysteine ligase catalytic subunit and \(\gamma\)-glutamate-cysteine ligase modifier subunit, superoxide dismutase, and glutathione reductase. CA treatment reversed the activation of JNK terminal kinase and p38, the reduction in the Bcl-2/Bax ratio, the increase in the cleaved caspase 3/caspase 3 and cleaved PARP/PARP ratios, as well as the decrease in tyrosine hydroxylase (TH) levels [10]. Another in vivo study in rats revealed that CA can effectively reverse the upregulation of ubiquitinated proteins and the decrease in PINK1 and Parkin protein levels in the rats exposed to 6-OHDA, similar to the results of the in vitro experiment [16]. In an in vivo experiment for AD, CA administration in hAPP-J20 mice ameliorated the memory and learning deficits in the Morris water maze test [85].  
The first appearance of the favorable effect of CA in vivo was witnessed in an experimental rat model of AD. This study suggested that CA (3 mg/kg) prevented hippocampal neurons from undergoing apoptosis, specifically those in the cornu ammonis 1 (CA1) region of the hippocampus, which impeded A\(\beta\)-related neurodegeneration [86]. The effect of CA (3 mg/kg) on memory and behavioral alterations caused by A\(\beta\) was also explored, which found that the molecule could reduce spatial and learning memory loss [87]. CA also diminished the dendritic spine loss in rat neurons treated with oligomeric A\(\beta\)[85], while its administration further ameliorated the memory and learning of hAPP-J20 mice in the Morris water maze test [85].
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 4 Experiment
### Segmentation  
We test the segmentation task of our network on the ShapeNetPart dataset, and, similarly, the segmentation also belongs to the task of rotation invariance. Our method also performs well in this task. Table 2 shows the segmentation results of each model for the ShapeNetPart dataset. Given the ability of vector neurons to learn invariant semantic information in point clouds, it can be seen that our network performs well in both rotated and non-rotated states. Although it does not achieve the best performance in the non-rotated state, it demonstrates strong adaptability in segmenting rotated point clouds. The vector information extracted by the network in the rotated point cloud helps the learning of the invariant semantic information in the rotated point cloud, thereby demonstrating strong generalization ability and performance. This enables our network to achieve the best performance in the segmentation task involving rotated point clouds.  
Figure 8 shows the partial segmentation effect of our network for the ShapeNetPart dataset under different rotation states. It can be seen that our network adeptly segments various components of the point cloud across different rotation states, demonstrating its competence in the segmentation task and showcasing robust rotation invariance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Methods** & **i** & **z** & **SO(3)** \\ \hline \multirow{8}{*}{Traditional network} & PointTransformer [18] & 93.7 & - & - \\  & Point-BERT [19] & 93.2 & - & - \\  & Point-MAE [20] & 93.8 & - & - \\  & GPSFormer [42] & 94.1 & - & - \\  & ShellNet [37] & 93.1 & **93.1** & 87.8 \\  & PointNet [38] & 89.2 & 85.9 & 74.7 \\  & PointNet++ [16] & 90.7 & 91.8 & 85.0 \\  & DGCNN [17] & 92.9 & 90.3 & 88.6 \\ \hline \multirow{8}{*}{Equivariant network} & Deltaconv [31] & 93.8 & - & - \\  & VN-PointNet [15] & - & 77.5 & 77.2 \\ \cline{1-1}  & VN-DGCNN [15] & - & 89.5 & 90.2 \\ \cline{1-1}  & SFCNN [30] & - & 91.4 & 90.1 \\ \cline{1-1}  & Cluster [27] & - & 87.1 & 87.1 \\ \cline{1-1}  & GC-Conv [29] & - & 89.0 & 89.2 \\ \cline{1-1}  & Ours & **94.2** & 92.8 & **90.6** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification on ModelNet40. Test classification accuracy (%) on the ModelNet40 dataset in three train/test scenarios. We bolded the results of the best performing networks.  
Figure 8: Segmentation of rotated point clouds. This figure illustrates the comparison of segmentation results obtained from our network’s output for different point cloud rotation states, namely, Z and SO(3), in contrast to the original point cloud.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Service Placement Metric
#### 2.3.1 Performance  
An SP deals with fast and efficient approaches to provide resource management, maintenance, and fail-over. Many approaches are attempts to provide better performance.  
Table 3 shows the suggested metrics to evaluate the performance of SP in previous studies. We have categorized the SP metrics for performance. In recent studies, the following six metrics were typically selected to evaluate SP performance: execution time, waiting time, resource utilization, transferred data, number of migrations, and throughput. These metrics are optimized in Table 3, according to different behaviors, such as the retrieval of maximal or minimal values. In addition, most of the approaches focus on minimization of the placement time. Time minimization reduces the execution time, downtime, and waiting time [1; 2; 3; 4; 5; 6; 7]. Time minimization is an important metric due to the affection of the performance of VMs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Metric** & **Decision Value** & **Algorithm** \\ \hline
[22] & Response time, QoS & SP & GA \\
[23] & Resource wastage & SP & GA \\
[24] & Cost & Load dispatching & Greedy, GA \\
[25] & Cost & Task distribution & Mixed ILP \\
[26] & Network latency & SP & ILP \\
[27] & Power consumption & SP & Weight \\
[28] & Delay & SP & Markov \\
[29] & Resource usage & Resource allocation & Consensus \\
[30] & Energy & SP & First-fit \\
[31] & Delay & SP & Own algorithms \\ Proposed scheme & Multi-metrics & SP & Fuzzy-AHP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related work on SP.
## 3 Results
### Computation of Relative Distance  
In this case, the real-world distance corresponding to one pixel was 0.94 \(\upmu\)m. We conducted an error analysis of the distance from the electrode tip to the root (n = 41) (Figure 20). The mean square error was 3.15 \(\upmu\)m, and the root mean square error was 3.54 \(\upmu\)m. The max distance error was 6.10 \(\upmu\)m. The RE\({}_{D}\) was 1.32%. The results indicate that the automatic detection of relative distance was more accurate than human eyes under these conditions.
## 4 From Exhaustive Search to Non-Linear Analysis  
In the former sections simulations of the model were made. By trying different quantities for the injected DCs, periods of hypothetical successful treatments can be inferred. Table 2 shows that therapies with \(10^{3}\) are not sufficient for a successful therapy. Table 2 shows that a reasonable period of infusion \(10^{9}\) is enough for successful treatment. The result of Table 2 shows that injections of \(10^{7}\) during smaller periods have a better reduction of tumor growth percentage. Nevertheless, for the Department of Tissue and Cellular Biology in UNAM to create doses greater than \(10^{8}\) is not feasible. The main objective of the computer simulation, then, was to find operation ranges to DCs quantities and injection times for which the therapy reduces the tumor significantly. To that end, an exhaustive search of operative protocols was performed.  
Now to confirm the numerical results a non-linear analysis of the model was performed. It reveals that without the DC therapy tumor cells cannot stay under non-lethal  
Figure 4: Hypothetical therapies results. The _red curves_ represent the growth of tumor cells with immunotherapy and _blue curves_ represent the growth of tumor cells without immunotherapy. Figure 4 all simulation result implements a therapy with infusion size of \(10^{7}\) DCs injected every 168 h by 3 weeks. The subfigures (**A,B**) using different carrying capacity with \(K=10^{6}\) and \(K=10^{11}\) respectively. For cases (**C,D**) is varied the rate tumor growth parameter in 0.001 and 0.1. The subfigures (**E,F**) the dendritic cells infused is varied in \(10^{3}\) and \(10^{9}\) every 168 h by 3 weeks.  
bounds. The latter was done analyzing the stability of fixed points of the Equations (1)-(4) and showing the existence of periodic orbits, as it is shown in the following. Then, we show that a pulsed periodic therapy can prevent tumor relapsing when the doses and period times lie within a certain range. For this purpose, we transformed to a periodically pulsed system to account for the multiple bolus injections over fixed periods of time. We used those results to find a condition of stability for a fixed point when \(T=0\) using periodically pulsed immunotherapy. This fixed point (\(T=0\)) could be used to analyze the success of the immunotherapy after a surgery in which the tumor is extracted. That is, we give conditions in which the immunotherapy is effective for the remaining tumor cells after such a procedure.
## 4 Network Functionality of Emergency Response  
In the post-earthquake chaotic environment, the transportation network is considered as one of the most significant infrastructure systems to support emergency response activities, and the disruption of transportation networks could lead to additional casualties, economic losses and secondary disasters [43; 68; 69; 70; 71]. Maintaining effective connectivity between affected areas and the emergency facilities has always been recognized as the immediate priority following a major earthquake [40; 72; 73; 74]. In this paper, we utilize an improved connectivity-based model to evaluate the post-earthquake network functionality of emergency response [75].