esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 7 Conclusions  
To address the challenge of real-time interactive control in visualizing large-scale flow-field data, this paper proposes a data-partitioning strategy based on grid segmentation and a real-time interactive method for large-scale flow fields. This effectively overcomes the technical obstacles in the parallel visualization of large-scale flow fields. Firstly, we employ geometric model metadata to create file viewports for each process and reconstruct the grid topology through metadata. This involves mapping the new grid topology and coordinate points and resolving the mapping table for attribute-variable retrieval. Secondly, by managing network communication at different levels within the cluster system, we achieve coordination between synchronous and parallel rendering. We design experiments to compare our data-partitioning strategy with serial methods and the approach used in ParaView, validating the effectiveness of the communication management method. The experimental results demonstrate that our data-partitioning strategy improves the loading efficiency of datasets with ten million grid cells by a factor of seven, ensuring load balancing in the system and correctly supporting visualization rendering and computation. Compared to ParaView, our approach exhibits superior performance, avoiding bottlenecks and delays caused by single-point I/O, and addressing the challenge of visualizing large-scale flow fields due to the sheer volume of data. It holds significant value for large-scale flow-field visualization. In interactive scenarios involving 500 million and 800 million grids, our system achieves millisecond-level response times, fully demonstrating the effectiveness of our communication management method. This method enables complex visual feature computation and rendering driven by visualization terminals on supercomputers, enhancing real-time and interactive capabilities in visualization tasks that require a rapid response and feedback, such as exploratory analysis, parameter adjustment, and dynamic demonstrations.  
Methodology, Z.H. ([PERSON]) and Z.H. ([PERSON]); Software, C.C.; Validation, Z.H. (Zhouqiao He) and X.T.; Formal analysis, W.Z.; Resources, Q.C.; Data curation, Z.H. (Zhouqiao He); Writing--original draft, Z.H. ([PERSON]); Writing--review & editing, Z.H. ([PERSON]); Supervision, Y.W.; Project administration, Y.W.; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Numerical Windtunnel Project (NNW2019 ZT6-A17), the National Defense Basic Research Project (JCKY2022404C001), and the Innovation Fund of Postgraduate, Sichuan University of Science & Engineering (Y2022178).  
Not applicable.  
The authors declare no conflict of interest.
# Effects of Viewing Displays from Different Distances on Human Visual System
## 3 Results
### EEG Signals Response Analysis
#### 3.2.1 Relative \(\beta\) Power  
The relative \(\beta\) power band (\(\beta\) rhythm) is expressed as relative \(\beta\) power band = 100 \(\times\) (power of \(\beta\)/(power of \(\alpha\) + power of \(\beta\) + power of \(\theta\))). The results showed that the viewing time has a significant effect on the relative \(\beta\) power of the EEG, _F (7, 448)_ = 3.982, _p_! 0.0001, partial eta squared = 0.059. Moreover, the display type has a significant effect on the relative \(\beta\) power of the EEG, _F (1, 64)_ = 9.332, _p_! 0.003, partial eta squared = 0.127. In addition, the viewing distance in terms of the display-type interaction had a significant effect on the relative \(\beta\) power of the EEG, _F (1, 64)_ = 8.526, _p_! 0.005, partial eta squared = 0.118.  
Figure 3 shows the relationship between the EEG relative \(\beta\) power and viewing time. The results show that the relative \(\beta\) power of EEG decreases slightly with the increase in viewing time from previewing to thirty minutes (session 4), and subsequently, remains stable for up to fifty minutes (session 6). Thereafter, it significantly decreases from session six until the end of the movies, _p_! 0.05.  
Figure 4 shows the effect of the viewing distance (3H and 6H) with respect to the display type (2D and 3D) on the relative \(\beta\) power of the EEG. As shown in the Figure 4, the relative \(\beta\) power is significantly higher when watching videos in the 2D display from a short viewing distance when compared with watching the videos in 3D, _F (1, 38)_ = 23.662, _p_! 0.0001, partial eta squared = 0.384. Moreover, there is no significant difference between watching the videos in 2D or 3D from a long viewing distance (6H). Moreover, when the viewing distance is changed from 3H to 6H, the relative \(\beta\) power was increased in case of 3D-display, while it was decreased in case of 2D-display. The difference between watching the 3D display from near or far viewing distance is insignificant (3H or 6H), _F (1, 38)_ = 2.564, \(p\) = 0.118, partial eta squared = 0.063. In addition, there is a significant difference between watching the 2D display from different viewing distances (3H or 6H), _F (1, 38)_ = _7.097, \(p\) = 0.011; partial eta squared = 0.157. The results show that watching the 3D display from a short viewing distance resulted in a high visual fatigue while watching the 2D display from a short viewing distance resulted in a low visual fatigue.  
Figure 3: Effect of viewing time on relative \(\beta\) power (%).
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 3 Materials and Methods
### Historical Information Exchange  
Each node must be aware of the historical movement trajectory of other nodes in order to predict its own movement path. The historical movement trajectory of a node is defined as a quintet (\(nodeID,P,W,T_{cur},Location_{cur}\)), where \(P\) is the transfer probability matrix of the node, \(W\) is a chain of dwell times at each location, \(T_{cur}\) is the time to update the quintet and \(Location_{cur}\) is the current location of the node. The historical movement information spreads epidemically throughout the network. When two nodes meet, the nodes first store each other's movement trajectory locally and update the information of the other node in the local cache if it already exists. When exchanging the history traces of other nodes stored by a node, the quintet with the latest update time replaces the old quintet based on the comparison of the \(T_{cur}\) stored by both sides. As a result of the remarkable regularity of student movement on campus, the \(P\) matrix will be sparse, demonstrating the strong predictability of student node trajectories.
# Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems
## 4 Experimental Studies
### Experimental Results
#### 4.2.2 Case2: Considering the Disturbance  
Since the SMC is insensitive to parameter uncertainties and external disturbances, we wanted to verify the relevant features of the STA, which is a special form of second-order SMC. Moreover, we used adaptive gain, whose effects also need to be verified. To this end, we added a board on the nominal MWIP system to greatly increase the uncertainty of the system. The system is shown in Figure 3b, in which the height of the MWIP system is increased by 10 cm and the weight is increased by 0.5 kg. The parameters of the controller remain the same as in the previous case.  
The experimental results are shown in Figures 8-11. It turns out that the MWIP system can still be balanced on flat ground. The figures show that the stability of the system can be guaranteed despite the increase in external disturbances. Compared with the previous case, the adaptive coefficients obviously increased faster, and finally reached a relatively large value. This result is consistent with the fact that large values of the switching gains can keep the states of the system sliding on the sliding surface, but it leads to serious chattering at the same time for the SMC-based strategy. From Figure 9, we see that the amplitude of the sliding surface also increases. The inclination angle can still be maintained at zero, as shown in Figure 8. This proves that the adaptive parameters of the algorithm are adjusted effectively according to the disturbance, and the proposed algorithm is robust to large model uncertainties. The control torque shown in Figure 11 concurs with the analysis of other variables, which further supports our conclusion. The comparison of RMS error in this case provides more support for our conclusion, as shown in Table 4.  
Figure 10: Comparison of the adaptive gains in Case 2.  
Figure 9: Comparison of the sliding mode variables in Case 2.
## 3 Results and Discussion  
### Preparation of the Poly(DEAEMA-co-DVB) Microspheres  
As shown in Figure 1, the poly(DEAEMA-co-DVB) microspheres were prepared using a SiO\({}_{2}\)-stabilized Pickering emulsion polymerization method. By self-organization of SiO\({}_{2}\) at the interface of oil (monomers and porogens) and water, the stabilized oil-in-water Pickering emulsion droplets were formed and used as polymerization vessels to obtain the polymer/SiO\({}_{2}\) composites by thermal radical polymerization. The SiO\({}_{2}\)-covered spherical particles were then etched using hydrofluoric acid to remove SiO\({}_{2}\), and the poly(DEAEMA-co-DVB) microspheres were finally obtained. Here, the monomers DEAEMA and DVB afforded the weak anion-exchange and hydrophobic functionalities, respectively, and a combination of them successfully constructed the mixed-mode weak anion-exchange medium.  
Figure 1: Schematic illustration for preparation of poly(DEAEMA-co-DVB) microspheres.  
Characterization of the Poly(DEAEMA-co-DVB) Microspheres  
A dose of 120 mg for silica nanoparticles was adopted with the aim of optimized uniformity and particle size. The synthesized solid material was subsequently characterized by SEM, nitrogen physical absorption, and FT-IR measurements. The SEM results shown in Figure 2 verify that most of the particles have spherical shape with the particle size range of 15-30 \(\upmu\)m.  
The N\({}_{2}\) sorption-desorption isotherm gives the porous structure of poly(DEAEMA-co-DVB), as shown in Figure 3. The obvious hysteresis loop between absorption and desorption for capillary condensation demonstrates a mesoporous structure of the poly(DEAEMA-co-DVB). The pore size distribution analyzed by BJH is presented in Figure 3, which further proves that the mesopore size ranges from 10 to 30 nm.  
As presented in Table 1, the BET surface area and pore volume were calculated as 575 m\({}^{2}\)/g and 0.94 cm\({}^{3}\)/g, respectively, which is very beneficial for the adsorption of pharmaceutical molecules in solution. Additionally, the FT-IR was also carried out for identify the functional groups, and the results are given in Figure 4. The peak at 1066 cm\({}^{-1}\) is ascribed as the stretching vibration of tertiary amine in poly(DEAEMA-co-DVB). The DVB as the copolymerized monomer is approved by the peaks at 1604 and 1510 cm\({}^{-1}\) attributed to C=C, and the peaks at 796 and 710 cm\({}^{-1}\) are attributed to =C-H of benzene ring. Moreover, the signal at 1727 cm\({}^{-1}\) is testified as the C=O stretching of ester, which is different from the fact that carbonyl signal of -COOC=C appears in DEAEMA monomer, strongly implying that  
Figure 3: (**a**) N2 adsorption–desorption isotherms; and (**b**) pore size distribution of poly(DEAEMA-co-DVB) microspheres.  
Figure 2: SEM images of poly(DEAEMA-co-DVB) microspheres.  
the vinyl-copolymerization process of DEAEMA and DVB was successful due to the disappearance of vinyl group. In addition, the existence of 1205 and 1118 cm\({}^{-1}\) for C-O-C stretching was also assigned to the ester group. Thus, the IR test confirms the correct synthesis of poly(DEAEMA-co-DVB) material.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
###### Abstract  
Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry's volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sunddays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model's validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.  
deep learning; gated recurrent unit; hyperparameter tuning; long short-term memory; recurrent neural network; short-term demand forecasting +
Footnote †: journal: Applied sciences
## 8 AM Techniques for Crash Boxes
### Mjt  
Only a few researchers have conducted investigations using MJT AM technology to improve crash boxes' energy absorption capabilities.  
[PERSON] et al. [105] proposed regular square honeycomb (RSH) and square hierarchical honeycomb (SHH) lattice structures, as shown in Figure 19a. These specimens were fabricated using VeroWhitePlus material with the MJT technique. The study investigated the impact of substructure numbers on energy absorption performance. The results revealed that SHH exhibited improved \(SEA\) and \(CFE\) compared to an RSH of equal mass, with SHH-5 showing improvements of 126.7% in \(SEA\) and 53.6% in \(CFE\) compared to RSH-5. In a related study, [PERSON] et al. [106] investigated the energy dissipation characteristics of Beetle Elytron Plates (BEPs), inspired by the microstructure of beetle elytra, and compared these with honeycomb plates (HPs) of equivalent wall thickness, as shown in Figure 19b. The specimens were manufactured using MJT with DSM Somos 14120 resin (manufacturer: Royal DSM; manufacturing facility: Elgin, IL, USA). The results showed that BEPs achieved an energy dissipation capacity five times higher than that of HPs, indicating a significant improvement in energy absorption performance.  
Figure 19: MJT: (**a**) hierarchical honeycomb structures [105]; (**b**) beetle elytron-inspired structures [106].
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 4 The Proposed Approach
### Ensemble Feature Selection Technique
\[f^{\prime}=\Big{(}A\bigcap B\Big{)}\leftrightarrow x_{1}\epsilon A\wedge x_{2 }\epsilon B\wedge x_{1}=x_{2},\quad\text{where}\quad y_{1}\geq\text{{th1}} \wedge y_{2}\geq\text{{th2}}\]  
ICMPv6-Based Attack Detection  
This phase trains the LSTM to build detection models that detect ICMPv6 flooding DDoS attacks based on the selected features (\(f^{\prime}\)). The target function \(f(x)=y_{i}\), where \(y_{i}=\) [normal, attack] is proposed as the attack detection process. This phase employs the features chosen during the ensemble feature selection phase. The training model is the result of this phase. Once deployed online, it will detect ICMPv6 flooding DDoS attacks. It is worth noting that the proposed approach is not limited to detecting ICMPv6 flooding DDoS attacks, but could also be applied to other types of datasets for detecting different attacks, or even datasets from different fields or areas.
# A Decentralized Receiver in Gaussian Interference
## 3 Bounds on Communications Rates  
The system's capacity \(C\) can be upper bounded by considering a stronger channel where all helpers are informed of each other's observations. Compute:  
\[I(\mathbf{X};\mathbf{Y}_{0},\mathbf{U}|\mathbf{Q}) =I(\mathbf{X};\mathbf{U}|\mathbf{Y}_{0},\mathbf{Q})+I(\mathbf{X};\mathbf{Y}_{0}|\mathbf{Q}) \tag{12}\] \[\leq H(\mathbf{U}|\mathbf{Q})+I(\mathbf{X};\mathbf{Y}_{0})\] (13) \[\leq T\cdot\left(L+\log_{2}\left[1+\|h_{0}\|^{2}/\mathbf{\Sigma}_{0,0} \right]\right), \tag{14}\]  
where the first inequality follows since \(\mathbf{Q}\to\mathbf{X}\to\mathbf{Y}_{0}\) is a Markov chain (Equation (6)). If the LAN constraint were not imposed and the base had access to the helpers' receptions in full precision, the receive side would be equivalent to a multi-antenna Gaussian receiver. By noisy channel coding [15], the capacity of this stronger channel is \(\max_{p_{X}:E[|\mathbf{X}|^{2}]<1}I(X;\mathbf{Y})\) (here, \((X,\mathbf{Y})\) are variables at a single timepoint) which, by a derivation given in [16], simplifies to the familiar upper bound:  
\[\log_{2}\left[\frac{|\mathbf{\Sigma}+\mathbf{h}\mathbf{h}^{\dagger}|}{|\mathbf{\Sigma}|}\right]. \tag{15}\]  
Taking the minimum of this bound and Equation (14) yields an upper bound for the original system. This is an instance of a general _cut-set upper bound_ from [17].  
**Remark 1**.: _Any achievable rate \(R\) must satisfy_  
\[R\leq\min\biggl{\{}\log_{2}\left[\frac{|\mathbf{\Sigma}+\mathbf{h}\mathbf{h}^{\dagger}|}{ |\mathbf{\Sigma}|}\right],L+\log_{2}\left[1+\|h_{0}\|^{2}/\mathbf{\Sigma}_{1,1}\right] \biggr{\}}. \tag{16}\]  
Proof.: Justified by the preceding discussion.  
The rest of the section is dedicated to four communication strategies for the system in order of increasing complexity and diversity utilization. The strongest bound is presented in Theorem 7. It is described using the context of the preceding two bounds.
## 4 Method Validation
### Numerical Results  
Taking TF1 as an example, the optimization process is described as follows:  
The Latin hypercube was chosen to carry out 200 experiments. Next, 150 poor designs of sample points were removed, and the remaining sample points were then selected to generate the snapshot matrix. Subsequently, only one principal component was retained after POD analysis, and 50 new sample points were then added around the regions of five initial optimum designs. Figure 9 shows the corresponding distribution of the sample points. Finally, one POD coefficient acted as the new design variable, and GA was used in the following optimization process. Table 2 shows the parameter settings of M-POD. Table 3 is a compilation of results from the POD, M-POD, and RBF. The same total number of sample points was used to compare the accuracy of the three methods. Additionally, the sample points generated by the Latin hypercube are random since a different random seed was used. The f-value in Table 2 is the average of 10 runs to reduce random variation in the results.  
Figure 9: Corresponding distribution of the sample points.  
The optimization process of TF2 and TF3 is similar to TF1. Therefore, the optimization process is not described, and only the parameter settings and results are listed. The comparison results clearly show that M-POD is more accurate than POD and RBF and thus obtains better solutions. It is demonstrated that M-POD is a better promising approach when the number of sample points is not enough to make the surrogate model obtain accurate results.
## 4 Materials and Methods
### Field Experiment  
A two-year trial (2018 and 2019) was conducted in a commercial vineyard located north of Lafayette, Oregon. The vineyard was planted in 2000 with _Vitis vinifera_ L. 'Pinot noir' clone 777 grafted on 3309C rootstock with vine spacing of 1.5 m rows and 2.4 m between rows with N-S orientation on a south-facing slope at 100 m above sea level on Goodin and Witham silty clay loam soils. Vines were came pruned and trained to a bilateral Guyot system with vertical shoot positioning. Each season, the vineyard was managed using standard disease and canopy management practices for the region and was not irrigated. Grapevines within a 1.1-hectare (2.73 acres) block were tested and identified as being positive for grapevine red blotch virus (GRBV). Individual vines along grid sampling (\(n=96\)) per plot were sampled and tested for GRBV in summer 2018 using leaf tissues, but were tested again to confirm virus status using dormant cane tissue following the crop year. All samples were analyzed at the USDA-ARS Horticultural Crops Research Unit Corvallis, OR using polymerase chain reaction (PCR) procedures [7].  
Two treatments (ABA application and unsprayed control) were applied to whole vineyard rows. Each plot consisted of six rows, with five field replicates in a randomized complete-block design. The ABA treatment was sprayed with 300 mg/L Protone SG (ProTone SG, Valent Biosciences Corp, Libertyville, IL, USA) and 0.01% surfactant (Kinetic, Helena Agri-Enterprises, LLC, Collierville, TN, USA) using a canopy airblast sprayer with nozzles directed to the cluster zones only. The spray was applied until dripping at the onset of veraison (BBCH 80) and again two weeks later. Applications occurred on 11 August and 25 August in the 2018 season and on 9 August and 23 August in the 2019 season.