esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, an intelligent analysis of users' electricity consumption behavior based on improved k-means and LSTM is proposed, which can divide scattered and irregular original electricity consumption data according to the effective features. Then, the labels corresponding to the data can be given to form a feature dataset. A deep neural network trained based on the existing data can predict the user's type based on new electricity consumption characteristics so that it can intelligently provide users with targeted electricity consumption strategies or marketing services. This effectively solves the problem of a single analysis method not being able to easily classify and judge the new electricity data.  
Nine features were extracted based on power big data, which could comprehensively characterize users' electricity consumption behavior. The improvement of a k-means algorithm greatly improved the efficiency of cluster analysis. The clustering results and features formed an effective dataset.  
Compared with the method of directly training a neural network with original data, the calculation time of the proposed algorithm was reduced, and the classification results of new electricity data were more accurate. The analysis results can provide support for grids to formulate targeted marketing services. However, the classification model selected only considered the time dependence of electricity data and the seasonal correlation of features. In the future, the applicability of other deep learning models will be further analyzed.  
Methodology, H.L.; validation, H.L., B.H. and X.L.; investigation, Z.W., Y.L.; data curation, B.Y.; writing--original draft preparation, H.L., Z.W. and B.Y.; writing--review and editing, B.H., G.L. and X.L.; supervision, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Fundamental Research Funds for the Central Universities (N2004030), and the Liaoning Revitalization Talents Program (XLYC1902090).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline LSTM & 96.71\% & 95.15\% & 98\% & 96.65\% \\ SVM & 90.85\% & 90.15\% & 95\% & 90.81\% \\ KNN & 83.53\% & 80.32\% & 88.82\% & 84.36\% \\ ELM & 86.47\% & 86.74\% & 90.83\% & 88.74\% \\ BP & 73.53\% & 67.07\% & 98.24\% & 79.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation of different classifiers.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
###### Abstract  
This study investigating the accuracy of turbulence model simulations of the exhaust manifold using computational fluid dynamics (CFD) carries significant implications. By modeling and analyzing the flow of emissions, we aim to identify areas of high stress and pressure, minimize the pressure drop, and maximize the flow of exhaust gases. This not only enhances engine performance, reduces emissions, and improves the durability of the manifold but also provides a unique opportunity to predict and analyze the flow and performance of the exhaust manifold, both quantitatively and qualitatively. This paper aims to provide a detailed comparison of five turbulence models that are commonly used in CFD to offer valuable insights into their accuracy and reliability in predicting the flow characteristics of exhaust gases. The results show that the k-kl-\(\omega\) model showed the highest maximum velocity and the most comprehensive temperature range, efficiently capturing the transitional flow effects. The K-\(\omega\) STD and SST transition models displayed significantly higher turbulent kinetic energy (TKE) values, indicating their enhanced effectiveness in modeling complex turbulent and transitional flows. Conversely, the Reynolds stress and RNG k-epsilon models displayed lower TKE values, suggesting more subdued turbulence predictions. Despite this, all models exhibited similar pressure drop trends, with a noticeable increase near the midpoint of the manifold. These quantitative findings provide valuable insights into the suitability of different turbulence models for optimizing exhaust manifold design.  
computational fluid dynamics; turbulence model; exhaust manifold; velocity; pressure; temperature +
Footnote †: journal: Applied Physics Letters
## 3 Results  
### Overall Structure of Ras-Raf-RBD_CRD Complex  
The Raf-RBD and CRD interfaces are contiguous surfaces spanning Ras residues 23 through 48, including the C-terminal end of the Ras helix \(\alpha\)1, switch I, \(\beta\)2 and loop 3 (Figure 1b). The Raf-RBD interacts with Ras at switch I and the N-terminal portion of \(\beta\)2 (residues 27-41), as previously described [7], and the short linker connecting the Raf-RBD and Raf-CRD (residues 133-137) is partially disordered in our structure, with poor electron density for residues 134-136. The Raf-CRD interaction involves hydrogen bonds and hydrophobic contacts with C-terminal residues in Ras \(\alpha\)1 (residues L23-N26) and \(\beta\)2, extending to loop 3 in the interswitch region (residues K42-G48), with direct and allosteric connections to \(\alpha\)5 (Figure 1c). The Raf-CRD interface involves residues near the C-terminal end of the CRD in the stretch from F172 to T182, and nearby N-terminal residues H139, F141, R143, as well as F163. This interface includes the helix adjacent to the first Zn\({}^{2+}\)-binding site and extends toward the C-terminus, where the second Zn\({}^{2+}\) ion is coordinated by residues C184 as well as by the interface residue H139, linking the N and C-terminal ends of the Raf-CRD. The position of the CRD observed in our crystal structure is as recently reported [20] and consistent with published NMR chemical shift perturbation (CSP) data (Figure 1c and Figure S2a), and with Ras N26G and V45E mutations shown to disrupt the Ras-Raf-CRD interaction [19; 24]. Given its close contact with the interswitch loop 3 on Ras, the Raf-CRD is allosterically connected to \(\alpha\)5 through salt bridges that form between Ras D47 and E49 on loop 3 and R161 and R164 on \(\alpha\)5 (Figure 1c). This binding mode of the Raf-CRD to Ras is distinct from that described in the NMR data-driven nanodisc-bound KRas-CRaf-RBD_CRD complexes (Figure S2b), where the Ras-Raf-CRD interaction was modelled utilizing chemical shift perturbation data that are also consistent with our model (Figure S2a) and paramagnetic relaxation enhancement (PRE) data with placement of the probe at a cysteine engineered in place of Q43 [24], a residue with multiple interactions at the Ras-Raf-CRD interface (Figure 1c). Only the RBD-CRD configuration in the crystal structure is consistent with the expected location of the Raf-RBD when superimposed on the cryo-EM structure for the inactive BRaf/MEK1/14-3-3 complex (PDB ID 6 NYB), in which the Raf-CRD is sandwiched between the Raf-kinase domain and 14-3-3 dimer and the Raf-RBD is solvent exposed, absent from the final cryo-EM reconstruction [32] (Figure S2c).
## 3 Results and Discussion  
### Results  
The two satellite datasets provide different estimates of annual GPP. With the ALOS/AVNIR-2 data, annual GPP varies from 0.13 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\) to 2,586.18 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\) with a mean of 836.23 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\). With the Aster satellite, GPP shows a minimum of 0.14 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\), a maximum of 2,595.26 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\), and a mean of 776.83 gC \(\mathrm{m}^{-2}\)\(\mathrm{yr}^{-1}\). Based on ALOS/AVNIR-2 data, total GPPper year in Denpasar is 52,421.46 tC yr\({}^{-1}\) and covers an area of 6,267.56 ha; the equivalent measures based on Aster satellite data are 59,355.49 tC yr\({}^{-1}\) and 7,647.84 ha (Table 2). The GPP pixel value distribution from the ALOS/AVNIR-2 satellite data is dominated by low-GPP pixels (\(<\)250 gC m\({}^{-2}\) yr\({}^{-1}\)), which cover an area of 1,236.62 ha. The area covered decreases with increasing GPP; the highest GPP pixels (2,250-2,587 gC m\({}^{-2}\) yr\({}^{-1}\)) cover an area of 17.17 ha (Table 3). Similarly, the GPP pixel distribution from the Aster satellite data is also dominated by low-GPP pixels (\(<\)250 gC m\({}^{-2}\) yr\({}^{-1}\)), in this case covering an area of 1,694.56 ha, and area again decreases with increasing GPP. At the high end (GPP of 2,250-2,595 gC m\({}^{-2}\) yr\({}^{-1}\)), these pixels cover an area of 6.59 ha (Table 3). Maps of the GPP distribution from ALOS/AVNIR-2 and Aster are shown in Figure 2. In the Denpasar area, the maximum GPP measured by both ALOS/AVNIR-2 and Aster is smaller than the maximum GPP derived from the MOD17 (2,707.8 gC m\({}^{-2}\) yr\({}^{-1}\)).
## 2 Related Concepts
### ACDE-SVR Prediction Model  
One important factor affecting the prediction accuracy of the SVR model is the selection of model parameters. Based on its strong stability, superior global optimization performance and higher convergence speed, this study uses the ACDE algorithm to optimize the parameters of the SVR trajectory prediction model with the penalty factor \(C\), insensitive factor \(\varepsilon\) and kernel function parameter \(g\). Figure 2 is the flow diagram of ACDE-SVR algorithm. The steps of the ACDE-SVR algorithm are as follows:  
Step 1: The trajectory prediction sample is input after de-noising and the current number of iterations is set to \(k\). The ACDE parameters including population size \(D\), maximum number of iterations \(M\), adaptive scaling factor \(F(k)\) and crossover probability \(cr\) are set according to the actual situation.  
Step 2: The optimal range of parameters \((C,\varepsilon,g)\) is set and the population is initialized randomly and uniformly.  
Step 3: The SVR algorithm is used to predict each individual. The \(MSE\) (mean square error) between the predicted value \(y_{i}\) and real value \(Y_{i}\) is calculated according to: \(MSE=\frac{1}{l}\sum\limits_{i=1}^{l}{(y_{i}-Y_{i})^{2}}\). The \(MSE\) is used as the fitness function [38]. The fitness value, individual extremum of each individual \(Y_{\text{individual}}(i)\), global extremum \(Y_{\text{global}}\) and global extremum points \(X_{\text{global}}\) are recorded.  
Step 4: If the number of iterations is less than the maximum number of iterations, that is, \(k<M\), the algorithm proceeds to the next step. Otherwise, it proceeds to step 7 and outputs the global extreme point as the searched optimal parameter, \((C^{*},\varepsilon^{*},g^{*})\).  
Step 5: According to Equations (17)-(22), mutation, crossover, selection and chaotic fine search operations are carried out to generate new populations. The fitness of individuals within the population, individual extremum \(Y_{\text{individual}}(i)\), global extremum \(Y_{\text{global}}\) and global extremum \(X_{\text{global}}\) are calculated.  
Step 6: Iterations are set as \(k=k+1\) and the algorithm returns to step 4.  
Step 7: The SVR prediction model is built based on the calculated parameters.  
Figure 2: Flow diagram of adaptive chaos differential evolution-support vector regression (ACDE-SVR) algorithm.
## 4 Evaluation
### Communication Cost  
Figure 9 presents the communication time measured under varying numbers of nodes and subset sizes, using 70 GB and 100 GB files. The \(x\)-axis represents the number of nodes, and the \(y\)-axis shows the communication time in seconds required to transfer encrypted subsets from all slave nodes to the master via non-blocking MPI. As shown in the figure, the communication time remains consistently low across all configurations. For the 70 GB, the time ranges from 8.88 to 12.57 s, depending on the number of nodes and subset size. With 4 MB, the time ranges from 10.89 s (for 2 nodes) to 11.33 s (for 4 nodes), showing only a 1.04\(\times\) difference. With 8 MB, the time range is from 11.66 s (for 4 nodes) to 12.57 s (for 8 nodes), corresponding to a 1.08\(\times\) difference. For 16 MB, the time varies from 8.88 s (for 8 nodes) to 10.21 s (for 2 nodes), resulting in a 1.15\(\times\) difference. For the 100 GB case, communication time ranges from 15.43 to 18.69 s. With 4 MB, the time ranges from 16.30 s (for 2 nodes) to 18.69 s (for 4 nodes), a 1.15\(\times\) difference. With 8 MB, the cost ranges from 17.89 s (for 2 nodes) to 18.66 s (for 6 nodes), showing a 1.04\(\times\) difference. For 16 MB, the time ranges from 15.43 s (for 2 nodes) to 16.06 s (for 4 nodes), also showing a 1.04\(\times\) difference.  
Across all subset sizes and file sizes, the variation in communication time remains minimal, demonstrating that communication overhead is both stable and scalable. This stability stems from our use of non-blocking MPI, which allows each slave node to transmit  
Figure 8: Node-Level encryption and communication time across different node configuration (1 master, all other nodes as slaves).  
pre-encrypted subset data independently and in parallel. This is because all data is securely encrypted before transmission, eliminating the need for additional encryption or secure channel protocols during communication. Our design preserves security guarantees while keeping the communication layer lightweight and efficient.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 4 Slot Bonding Implementation
### PHYs  
The implementation supports the 2 sub-GHz PHY configurations shown in Table 1, that is, the 2-GFSK modulation with 200 kHz channel spacing and 50 kbps data rate and the 4-GFSK modulation with 1667 kHz channel spacing and 1000 kbps data rate. PHY  
Figure 3: Illustration of the distributed implementation of the heuristic in the RPL routing layer. Node 3 uses the heuristic to select a new PHY and parent, by using the received \(x\) scores to the root node 0 and the new reliability values (based on the calculated RSSI values). After calculating the lowest possible \(x\) score from node 3 to node 0 (and thus also selecting its preferred parent and PHY), this score is broadcast throughout the network. Normally, all RPL DIO messages are broadcast to all nodes, but this is omitted for node 0, 1 and 2 to make the figure clear.  
layers in the sub-GHz spectrum are used because of the better propagation characteristics compared to the 2.4 GHz spectrum [23]. Both CC1200 radio configurations are by default available in the Contiki-NG repository. The implementation can be extended to support more PHY radio configurations. As indicated in the work of [PERSON] et al. and as seen in Table 2, taking into account the strict regulation of the sub-GHz spectrum, 44 channels of 200 kHz and 4 channels of 1667 kHz are possible (with frequency overlap) in the 7 sub-GHz bands divided over the European Union 863-870 MHz and 873-920 MHz spectra [4; 24]. Such a channel allocation with frequency overlap actually avoids wasting bandwidth and allows for a rich frequency diversity. However, the frequency overlap can also result in inter-PHY interference and possible duty cycle saturation for certain frequency bands. The alternative is allocating channels for the different PHYs without frequency overlap (thus reserving different parts of the wireless spectrum for different PHYs), with the consequence of limiting the frequency diversity.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment
### Comparison with State-of-the-Art Technology (BD)  
We further compared the LNLCN model with eight state-of-the-art SR models with blur descent (BD) kernels: SRCNN [13], FSRCNN [40], VDSR [14], RCAN [15], IRCNN [54], RDN [50], FPAN [51], SAN [20], and HAN [52]. Table 4 shows the degradation model reconstruction metrics of these models using a scaling factor of three. Our LNLCN model consistently outperforms other models in terms of reconstruction accuracy. Under the condition of blur descent (BD), our proposed learnable nonlocal contrastive attention module can aggregate image-related information more effectively than traditional nonlocal modules, thus enabling the network to better recognize effective information. In addition, the self-similar function setting endows the network with more flexibility. On the Urban100 dataset, the PSNR gain of the LNLCN model is as high as 0.3 dB compared with the RCAN model, which fully verifies the superiority of our LNLCN model.
## 3 Materials and Methods
### FEM Simulation  
To evaluate the performance of the proposed method, the FEM simulation was developed using the software framework FEBio [72], and results were compared for the deformation and the pressure within the tissue. Particularly, the model was the one introduced by [PERSON] and [PERSON] [7]. Muscle is modeled as a fiber-reinforced composite with transversely-isotropic material symmetry. The model uses an uncoupled for of strain energy to simulate the nearly-incompressible behaviour of muscle tissue. This model was selected because it is capable of representing complex muscle geometry and architecture from MR images, it considers muscle fiber orientation and arrangement, and the predicted muscle shape was compared to MR images of the same movement, obtaining less than 5 mm of distance error for large muscles. The parameters used with the model can be seen in Table 3.  
Figure 1: Integrated point-based tissue model for the triceps. (**A**) shows the initial state of the tissue, (**B**) shows the muscle after being innervated with a stimulus current, and (**C**) shows the muscle returning to its initial shape after the current was removed. The color of the particles represents its displacement with respect to its original position.  
Figure 2: Integrated point-based tissue model for the vastus lateralis. (**A**) shows the initial state of the tissue, (**B**) shows the muscle after being innervated with a stimulus current, and (**C**) shows the muscle returning to its initial shape after the current was removed. The color of the particles represents its displacement with respect to its original position.  
One of the drawbacks of the FEM is the preprocessing steps needed before any computation can be performed. In this case, a mesh with finite elements had to be created, and muscle material properties, including fiber distribution, were integrated. Boundary conditions were similar to the meshless simulation: the ends of the tissue were fixed in space. A tetrahedral mesh was used, and six resolutions were selected: 1 thousand, 2 thousand, 5 thousand, 10 thousand, 20 thousand, and 40 thousand finite elements. Figure 3 shows the mesh with around 40 thousand tetrahedral elements for the FEM simulation.  
FEM simulations consist on the geometry being activated for 250 time steps, and then removing the activation and running for 250 time steps. A step size of 0.001 was selected to avoid convergence errors. As for boundary conditions, we selected 10% of the nodes around the origin and insertion of the muscles, respectively, and fixed their displacement in X, Y, and Z. A pressure load of 200 Pa was applied using a load curve, which ramps the pressure value from zero, to the rest of the nodes. Fiber direction was created similarly to the meshless solution: instead of each particle having a direction, each node had a direction vector which represented the fiber direction. Figure 4 shows the deformation in millimeters of the triceps using the 40 thousand node resolution, while Figure 5 shows the deformation of the vastus lateralis for the 40 thousand node resolution.
## 2 Experimental Details
### Experimental Procedure
#### 2.3.2 Adsorption Tests  
A standard solution of lead (1000 ppm) was prepared by dissolving a weighed amount of lead nitrate salt in distilled water, and the pH was adjusted by adding 0.1 mol/L HCl. The adsorption performances of G-26 and MTS 9570 for Pb(II) ions were investigated by batch experiments at different temperatures. Batch adsorption experiments were conducted at a constant volume of 20 mL metal ions solution and using different doses of dry resins, i.e., from 0.1 to 0.5 g/L. The adsorption behavior of metal ions by the adsorbents was studied in the pH range of 1.5-5.5. Samples of metal solution and resins in 125 mL flasks were shaken in an electric shaker at different contact times ranging from 5 to 180 min and at the fixed speed of 150 rpm. After reaching the equilibrium stage, the solution was filtered and analyzed with XRF. The batch adsorption experiments were performed in triplicate and adopted the average value. The errors of the measurements were within 10%. The adsorption capacities of G-26 and MTS9570 exchangers and the removal percentage of Pb(II) were calculated using the following Equations (1) and (2), respectively.  
\[Q=\frac{(C_{o}-C_{e})V/1000}{M} \tag{1}\]  
and % removal of Pb (II) ions = \(\frac{(C_{i}-C_{o})}{C_{i}}\ \times\ 100\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **G-26** & **MTS9570** \\ \hline Matrix & Styrene-DVB & Polystyrene-DVB \\ Ionic form as shipped & H\({}^{+}\) form & H\({}^{+}\) form \\ Functional group & Sulfonic & Phosphonic and sulfonic \\ Appearance & Uniform particle size & Spherical beads \\ Structure & gel & Macroporous \\ Capacity & 2.0 eq/L & 18 g/L eq/L \\ Bed size & 0.65 mm \(\pm\) 0.05 & 0.315-0.850 mm \\ Uniformity coefficient & 1.1 & 1.4 \\ Water retention & 45\%–52\% & 55\%–70\% \\ Specific gravity & 1.22 g/mL & 1.12 g/mL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and chemical properties of resins [32].  
where \(Q\) is the adsorption capacity in mg/g at time t; \(C_{i}\), \(C_{o}\), and \(C_{e}\) are the initial, final, and equilibrium concentrations of Pb(II) in mg/L, respectively; \(V\) is the volume of Pb (II) solution in mL, and M is the total amount of resin in grams (g).