esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Physical Properties Methodology
#### 2.2.2 Texture Methodology  
The texture parameters of samples were measured using an Instron Universal Testing Machine (model 5544) (Instron Corporation, High Wycombe, UK). Computer software (Instron 5544, Software IX Series) was used to obtain the indicators. A texture profile analysis (TPA) test was used to measure hardness and cohesiveness (in Newton, N) for SuA, SuM, KA, KM, MA, MM, SaA, SaM, BA, and BM. Samples with cylindrical shapes (1 cm high, 1.25 cm in diameter) were prepared for TPA, which compressed the samples with a compression plate (36 mm in diameter) twice (to 50% of their original height) at a crosshead speed of 50 mm/min. The Warner-Bratzler test was used to measure the shear force (in Newtons, N) and toughness (in kilopascals, kPa) for StA and StM. Samples with a size of 1.0 cm wide, 1.0 cm high, and 2.0 cm long were prepared and tested with a crosshead speed of 80 mm/min. Each value is reported as the mean \(\pm\) standard deviation (SD) of 5 measurements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Types**} & \multicolumn{5}{c}{**Nutritional Values per 100 g**} & \multicolumn{3}{c}{**Ingrediients**} \\ \cline{2-6}  & **Protein** & **Saccharides** & **Fat** & **Salt** & **Fibre** & \\ \hline \multirow{6}{*}{StA} & \multirow{6}{*}{16.0} & \multirow{6}{*}{16.0 including 24 sugars} & \multirow{6}{*}{12.0 including 1.3 saturated fatty acids} & \multirow{6}{*}{1.3} & \multirow{6}{*}{5.5} & \multirow{6}{*}{2.03} & \multirow{6}{*}{2.03} \\  & &
## 2 Materials and Methods  
### Field Sites  
We selected five CO2 flux sites in southeast Australia representing considerable variations in region, climate, and species, including three evergreen broadleaf forests (EBF) and two grassland (GRA) sites (Table 1, Figure 1).  
The Cumberland Plain (CUM) site is located in a dry sclerophyll forest in the Hawkesbury Valley in central New South Wales. The canopy is dominated by _Eucalyptus moluccana_ and _Eucalyptus fibrosa_.  
The Tumbarumba (TUM) site is located in the Bago State Forest in southeastern New South Wales. The forest is classified as wet sclerophyll, and the dominant species is _Eucalyptus delegatensis_. The Wombat Forest (WOM) site is located in the Wombat State Forest, Victoria. The site is a secondary regrowth forest that was last harvested in 1980. The dominant tree species are _Eucalyptus obliquua_ (mesmate stringybark), _Eucalyptus radiata_ (narrow leaf peppermint), and _Eucalyptus rubida_ (candlebark), with an average canopy height of 25 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Site ID** & **Full Name** & **CO\_Flux Years** & **Location** & **V Vegetation** & **Military Grid** & **Annual** & **Cimate** & **Reference System** & **Precipitation** \\  & & & **(Lat, Lon)** & **Type** & **(HLS-Sentinel Tile)** & **(mm)** & **Type** & **(Tope)** & \\ \hline CUM & Cumberland & Jan 2015-Oct 2018 & -33.6152, & EBF & 561 KH & 800 & Cfa & [55] \\ TUM & Tumbarumba & Jan 2015-Oct 2018 & -35.6566, & EBF & 55 HFA & 1000 & Cfb & [56] \\ WOM & Wombat Forest & Jan 2015-Oct 2018 & -37.4222, & EBF & 55 HBU & 600 & Cfb & [57] \\  & & & 144.094 & -36.6499, & GRA & 55 HCV & 650 & Cfb & [58] \\ RIG & Riggs Creek & Jan 2015-Jan 2017 & 145.576 & -34.9893, & GRA & 55 HDB & 465 & Bsk & [59] \\ YNC & Yanco & Jan 2015-Oct 2018 & 146.291 & -34.9893 & GRA & 55 HDB & 465 & Bsk & [59] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on research sites. Climate types follow the Köppen classification system (Cfa—warm temperate fully humid with hot summer, Cfb—warm temperate fully humid with warm summer, BSk—arid steppe cold). EBF and GRA in the vegetation type column represent evergreen broadleaf forest and grassland, respectively. ID—identifier; Jan—January; Oct—October; Lat—latitude; Lon—longitude; HLS—Harmonized Landsat and Sentinel-2 project.  
The Riggs Creek (RIG) site is located in the Goulburn-Broken catchment in northeastern Victoria, with the surrounding area dominated by broadacre farming practices. The vegetation cover is predominantly pasture. The Yanco (YNC) site is located within the Yanco region of New South Wales. The flux tower site in the Yanco area is located in the western plains of the Murrumbidgee catchment and is within a wider research area (\(60\times 60\) km) that supports a network of OzFlux stations.
## 3 Results
### Gap Spacing  
Figure 11 illustrates the impact of gap spacing on both the breakdown voltage and voltage dispersion of the corona-stabilized switch. When the gap spacing is very small (1.5 mm), the difference in breakdown voltage between different frequencies is minimal, but this difference gradually increases as the gap spacing increases. As the gap spacing increases, there is an approximately linear growth trend in the breakdown voltage of the switch. However, as the gap distance continues to increase, the difference between the breakdown voltage of the switch at high- and low-repetition frequencies becomes larger. This indicates that the insulation recovery performance of the switch gradually deteriorates with increasing gap distance.  
When the gap spacing is small, the stability of repetitive breakdowns is generally poor, possibly due to the dominant factor being neutral gas density recovery rather than the corona-stabilization effect. The stability of repetitive breakdowns is slightly improved with an increase in gap spacing. In general, a small gap distance can lead to severe repetitive breakdown voltage dispersion. The large gap distance, although it has a certain improvement effect on repetitive breakdown stability, can also lead to poor insulation recovery performance of the switch. Therefore, the gap spacing selection of the switch needs to be moderate.  
Figure 10: The influence of operating pressure on the repetitive breakdown voltage and its dispersion of the switch (electrode R1, SF\({}_{6}\), gap spacing of 5 mm): (**a**) breakdown voltage; (**b**) voltage dispersion.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### WiscanNet-Lite  
We developed a containerized deployment methodology called WISCANet-Lite [27]. WISCANet-Lite utilizes Podman [71] or Docker [72] to run multiple edge nodes on a single host computer. A control node container is also launched to provide a self-contained and self-configuring software-defined radio network (SDR-N) solution. This container solution is much lighter weight than an equivalent virtual machine, and enables higher performance applications due to reduced overhead. However, it is still primarily limited by the host system's memory and Ethernet or USB bandwidth to consume and transmit samples to the USRPs. This enables researchers to scale the system up and down as necessary for experiments. The WISCANet-Lite containers can also be deployed across multiple machines in a more typical distributed WISCANet architecture. This enables simplified distributed deployment and consistency by leveraging the benefits of containerization. There are also Ansible roles available for deploying WISCANet.
###### Abstract  
This paper aims to apply a performance-based earthquake engineering approach to assess the assurance of flexural frames whose members are jointed together by using new modified RBS connections, namely, semi-rigid welded steel connections, which obey a progressive failure mechanism. First, the structural members and connections are modeled and predesigned in ETABS software, and then, using OpenSees software, a series of nonlinear progressive failure analyses are performed on the built models extracted from ETABS. To this end, three types of multi-story structures with 3, 10, and 15 are modeled. The models are subjected to 15 earthquakes, such as Northridge (1994), Kobe (1995), Chichi (1999), Bam (2003), Tabas (1978), and so on. The connections are modeled by a series of rotational springs whose nonlinear behavior is estimated by a three-line curve that is established based on the modified Ibarra-Krawinkler deterioration model. Finally, obtaining the maximum ground acceleration versus the maximum relative drift of the floors, the fragility curves of the structures for a collapse level (CP) are determined, through which the seismic performances of the models are evaluated. The results show that by reducing the number of structural floors, the ductility of structures was reduced, and by increasing the ductility of structures, higher drifts in structures were achieved at the same seismic level. The average amount of ductility reduction coefficient in extrutures with RBS was 1.06 times those without RBS, which indicates that the energy dissipation capacity in structures without RBS connection is higher than in those with RBS. Local analysis of connections shows a 9% increase in the plastic rotation capacity if RBS connections are used. The ductility of all frames with RBS connection increased slightly compared to frames without RBS.  
reduced beam section (RBS); seismic behavior; incremental dynamic analysis (IDA); performance-based design Article
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Communication Overhead  
Figure 12 shows the performance of communication overhead for various rounds. Because of the above reasons, the proposed method has a lower communication overhead than all other existing protocols. Compared to existing protocols, the parameters of the proposed method minimize the processes required for optimal CH selection and route formation, lowering the proposed protocol's communication overhead. Lowering the  
Figure 11: Packet loss ratio analysis.  
Figure 10: Packet delivery ratio (%) analysis.  
route-building procedure reduces redundant routing packets due to less frequent route disconnection, retransmissions, and congestion. Compared to traditional protocols, decreasing routed packets enhances the network's overall communication overhead efficiency of the proposed protocol.
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation
### Analysis of the ECS  
To evaluate the energy efficiency, we analyze the ECS which can be calculated by  
\[\text{ECS}=\frac{E^{Total}-E^{*}_{total}}{E^{Total}} \tag{21}\]  
where \(E^{*}_{total}\) represents the total energy consumption obtained by the traffic grooming algorithm. The ECS of the DLG, TPTG, DLG_GA, and TPTG_MA under different network sizes is shown in Figure 4. We can see that a significant ECS is achieved when the TPTG_MA and DLG_GA are implemented. This is because numerous traffic requests are aggregated into an identical wavelength leveraging the two algorithms, which decreases the unnecessary port consumption for optical/electric conversion. When the traffic intensity is 100 Erl, the TPTG_MA can achieve about 18%, 34%, and 40% ECS improvement over the TPTG for the 6-node, 12-node, and 22-node topology, respectively, which validates the effectiveness of the proposed MA in decreasing the energy consumption. As the traffic intensity increases, we can observe a decrease in the ECS for TPTG, DLG_GA, and TPTG_MA. Indeed, when the groomed traffic requests are closer to the maximum lightpath capacity, fewer grooming operations are performed, thereby leading to fewer ECS.  
In addition, the ECS highly depends on the network size. That is, as the network size increases, the ECS is greatly improved under the same traffic intensity. Taking the TPTG_MA for example, when the traffic intensity is 100 Erl, the 22-node topology can achieve about 32% and 47% ECS improvement over the 6-node and 12-node topology, respectively. Because there are many available wavelengths in the 22-node topology, where more grooming can be performed.
## 6 Conclusions  
The main body of this study presents a semi-supervised single-image deraining algorithm based on multi-scale wavelet transform (MSWT-SSIDA), aimed at addressing the key issue of rain streak removal in rainy images, particularly enhancing the algorithm's generalization capability in real-world scenarios. By incorporating multi-scale wavelet transform, the algorithm decomposes rainy images into multiple components at different scales, allowing networks of varying sizes to better identify diverse rain streaks and rain-drop types. This enhances the model's ability to capture richer rain features and improves deraining performance. The semi-supervised learning framework further boosts the model by combining supervised learning on synthetic data and unsupervised learning on real data, enabling better identification of complex rain streaks and improving deraining performance in real-world settings. Experimental results show that the MSWT-SSIDA algorithm outperforms existing deraining techniques across multiple benchmark datasets, especially in real-world rainy images, demonstrating its adaptability and robustness. Additionally, incorporating a residual detail restoration network (DRANet) effectively restores image details while removing rain streaks, preserving background detail, and improving image clarity. Overall, the algorithm demonstrates strong performance in both synthetic and real-world scenarios, confirming its effectiveness and superiority.  
X.L.: Conceptualization; formal analysis; funding acquisition; investigation; writing--review and editing. Y.H.: conceptualization; formal analysis; investigation; methodology; validation; writing--original draft; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61462055, with financial support provided by [PERSON] and [PERSON] (funding amount: 500 CNY).  
The datasets used in this study (including Rain1200, Rain1400, and RealRain) are publicly available for research purposes, and some of the experimental datasets are available at [[[https://github.com/hezhangsprinter/DID-MDN](https://github.com/hezhangsprinter/DID-MDN)]([https://github.com/hezhangsprinter/DID-MDN](https://github.com/hezhangsprinter/DID-MDN)), accessed on 9 April 2025] [[[https://xueyangfu.github.io/projects/cvpr2017.html](https://xueyangfu.github.io/projects/cvpr2017.html)]([https://xueyangfu.github.io/projects/cvpr2017.html](https://xueyangfu.github.io/projects/cvpr2017.html)), accessed on 9 April 2025] for access. Some of the data generated in this study are not publicly available due to privacy and ethical considerations. The training and test set data for this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
### IVR-Based Serious Games in the Context of Digital Learning Tools  
Various digital tools have been used in STEM (Science, Technology, Engineering, and Mathematics) education to enhance conceptual learning. These include interactive simulations, educational applications, video-based instructional content, and augmented reality (AR). Each of these tools provides specific benefits, but they also present limitations in addressing the challenges of teaching kinematic concepts such as projectile motion:  
* Interactive simulations (e.g., PHET-based tools) provide visual representations of physics phenomena but typically do not allow for embodied interaction or real-time experimental adjustments [11];
* Educational applications and video-based learning serve as reinforcement tools, but they do not facilitate direct manipulation of physical parameters [12];
* AR technologies enhance visualization, but they generally lack full immersion and the capacity for deep interaction with simulated physical systems [13].  
IVR-based serious games present an alternative framework by integrating immersion, interactivity, and feedback mechanisms that may support both conceptual understanding and engagement. In contrast to traditional digital tools, IVR allows students to manipulate projectile motion parameters (e.g., launch angle and velocity), observe instantaneous effects, and refine their understanding through an iterative process. Furthermore, elements such as progressive task structures and real-time adaptation provide a learning environment that responds dynamically to students' interactions, potentially improving knowledge acquisition and retention [14].  
The integration of IVR-based serious games into formal education requires consideration of pedagogical alignment and collaboration between educators and content developers [15]. Ensuring that these technological resources complement rather than replace conventional instruction is essential for their effective implementation [16]. Additionally, an understanding of physics principles, instructional design methodologies, and learner needs is required for the development of educationally relevant IVR applications [17]. Establishing mechanisms for evaluating the impact of these tools is also necessary, allowing for data-driven refinements that optimize both instructional effectiveness and user experience [18; 19; 20].
## 2 Materials and Methods
### Western Immunoblot  
A375 cells were treated with 1.5, 3.1, 6.2, and 12.5 ug/mL of EO for 24 h. Adherent and floating cells were harvested, and total protein was extracted from the cells using RIPA buffer supplemented with a 1% protease and phosphatase inhibitor cocktail (#78440, Thermo Fisher Scientific, Waltham, MA, USA). A Lowry assay was performed to quantify the protein content in the homogenates. Proteins (20-40 ug) were resolved on 10% or 12% SDS-PAGE gels. Proteins were transferred to a nitrocellulose membrane (Bio-Rad, Hercules, CA, USA). The membranes were blocked with 5% skim milk for 1 h at room temperature and incubated with primary antibodies overnight at 4 \({}^{\circ}\)C. The following antibodies were used: anti-PARP (#9542, 1:1000 dilution), anti-cleaved PARP antibody (#5625, 1:1000 dilution), anti-caspase-8 antibody (#9746, 1:1000 dilution), anti-caspase-9 antibody (#9508, 1:1000 dilution), anti-caspase-3 antibody (#9662, 1:1000 dilution), anti-cleaved caspase-3 antibody (#9664, 1:500 dilution) (all purchased from Cell Signaling Technology, Danvers, MA, USA), and anti-actin antibody (#A2066, 1:2000 dilution; purchased from Sigma-Aldrich). Next, the membranes were incubated with secondary antibodies (anti-mouse IgG-HRP, Sigma-Aldrich, #A9044, 1:5000 dilution, and anti-rabbit IgG-HRP, Sigma-Aldrich, #A9169, 1:5000 dilution) for 1 h at room temperature. The membranes were treated with ECL-HRP substrate (GE Healthcare, Chicago, IL, USA), and the signal was detected using the BioSpectrum Imaging System (Ultra-Violet Products, Ltd., Cambridge, UK).
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Collection
#### 4.1.4 NS-3 Simulation Parameters for Data Collection  
In this section, we provide a summary of the simulation parameters used in NS-3 to establish the foundational knowledge for our learning system. Table 2 presents the experiment parameters utilized in the NS-3 simulations.  
Furthermore, Table 3 demonstrates a sample of the simulation results, highlighting the obtained data.  
Figure 6 illustrates the representation of an obstacle in our urban scenarios.  
Where \(Min_{\rm X}=X_{\rm min}\), \(Max_{\rm X}=X_{\rm max}\), \(Min_{\rm Y}=Y_{\rm min}\), and \(Max_{\rm Y}=Y_{\rm max}\)  
Table 3 demonstrates how each geometric structure of a building impacts the transmission quality. This information can be interpreted through qualitative variables such as SNR and BER.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** \\ \hline Data type & CBR \\ Transport protocol & UDP \\ Simulation duration & 300 s \\ Number of vehicles & 2 \\ Radio propagation & ItuR1411 LosPropagationLossModel, Two-ray Ground \\ Number of bits transmitted & 1500 bytes \\ Mac protocol & IEEE 802.11p \\ Radio range & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment parameters in NS-3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Observation** & **MinX** & **MinY** & **MaxX** & **MaxY** & **SNR** & \begin{tabular}{c} **Number of** \\ **Bits Sent** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Bits Received** \\ \end{tabular} & \
\begin{tabular}{c} **Percentage of** \\ **Bits Received** \\ **(in \%)** \\ \end{tabular} &
\begin{tabular}{c} **Errors Bits** \\ **Rate (in \%)** \\ \end{tabular} \\ \hline
1 & 762 & 830 & 1480 & 277 & 25 & 1500 & 795 & 53 & 47 \\
2 & 163 & 497 & 1435 & 839 & 23 & 1500 & 851 & 57 & 43 \\
3 & 700 &
## 2 Methods  
### Principle of Proposed System  
The schematic diagram of the LVPC is shown in Figure 1, its main circuit is a variable speed pump control motor circuit and a proportional throttle valve (PTV) is connected in parallel to the main circuit through a shuttle valve and forms a bypass leakage path. In the parallel control system, the variable speed pump (VSP) supplies a basic flow, which is varied via the output frequency of the inverter and the individual replenishing device is connected to the return chamber to compensate for leakage and maintain a constant chamber pressure. The flushing device is used to exchange hot fluid in the return chamber and to cool the system.  
The control principle of the LVPC is shown in Figure 2. The hydraulic motor speed is controlled by the VSP and PTV, the flow into the hydraulic motor is equal to the output flow of the pump minus  
Figure 1: Schematic framework of LVPC (leaking valve-pump parallel control) system. 1—Inverter; 2—electric motor; 3—Fixed displacement pump; 4—Shuttle valve; 5—Oil replenishing device; 6—Flushing device; 7—Hydraulic motor; 8—Inertia; 9—Encoder; 10—Loading pump; 11—Proportional relief valve; 12—PTV (proportional throttle valve).  
the bypass flow back to the tank though the control valve. In order to distinguish the role of valve control and pump control in the combined system, the valve-pump weight ratio \(K_{vp}\) is given as  
\[K_{vp}=K_{v}:K_{p} \tag{1}\]  
where \(K_{v}\) is the weight factor of the valve control link and \(K_{p}\) is the weight factor of the pump control link.  
The LVPC system is a multi-input and output control system, is composed of valve control and pump control and each control link is in closed-loop control. The encoder detects the actual speed of the motor and feeds back to the controller. The actual speed is compared with the reference speed to obtain the speed error. This error is assigned to the valve control channel and the pump control channel according to the weight ratio and under the common adjustment of valve control and pump control, the motor reaches a certain desired speed.  
If \(K_{vp}>1\), the role of the valve control is greater than that of the pump control and the system is mainly controlled by the PTV. When \(K_{v}:K_{p}=1\):0, the combined system becomes a conventional valve control system, totally controlled by the valve.  
Conversely, if \(K_{vp}<1\), the role of the pump control is greater than that of the valve control and the system is mainly controlled by the VSP, a ratio of \(K_{v}:K_{p}=0\):1 implying a conventional VSPC system.