esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 4 Prospects of Nano-Expansion Technology
Functional Janus-structured nanomaterials, organic-inorganic core-shell composites, and surface-charge-tunable nanoformulations can effectively mitigate issues such as interfacial deactivation, phase separation, and mechanical degradation under harsh conditions. For instance, zirconia-coated mesoporous silica composites exhibit enhanced structural stability in high-temperature, high-salinity environments. Research on Environmentally Friendly Nano-Flooding Technologies: Establishing a green and sustainable nano-flooding system requires breakthroughs in the following areas: (1) developing biodegradable nanomaterials from bio-based raw materials such as lignin and chitosan; (2) optimizing low-environmental-risk formulations by replacing traditional organic solvents with supercritical fluid synthesis techniques; (3) constructing a recycling system for spent nanomaterials through magnetic separation and surface regeneration technologies to improve material utilization efficiency. Carbon footprint assessment models should guide R&D processes to ensure lifecycle environmental management of the technology. Potential Integration with Other EOR Technologies: Innovative synergistic technologies combining nanofluids with gas flooding, bio-enzyme flooding, and electromagnetic assisted methods should be explored. Key applications include (1) designing nanoemulsion-supercritical CO\({}_{2}\) composite systems to synergistically reduce oil viscosity and enhance microscopic sweep efficiency in tight reservoirs and (2) developing hybrid schemes integrating magnetic nanoparticles with low-frequency pulse waves to improve oil mobilization in fracture networks. A multidimensional techno-economic evaluation framework is recommended to validate feasibility through field trials and establish industrial standards for nano-flooding applications.
# Low Thermal Expansion Machine Frame Designs Using Lattice Structures
## 4 Discussion  
The CTE results for the design in this paper showed that the planar CTE of the proposed lattice design can be tailored by changing the ratios of \(D/L\) and \(\alpha_{2}/\alpha_{1}\). The selection of \(D\) and \(L\) was carried out by modifying the geometry with CAD software. The parameters \(\alpha_{1}\) and \(\alpha_{2}\) were varied by the selection of the lattice outer part and the cylindrical inner part materials. However, \(\alpha_{2}\) should be greater than \(\alpha_{1}\) to make the deformation mechanism properly work.  
Based on the relationship of the cylindrical inner part and the lattice outer part using parametric constraints, when the size of the cylindrical inner part is increased, the internal geometry of the outer part must increase to maintain physical contact. This leads to varying quantities of material in each part, as shown in Figure 11. In turn, this will affect the manufacturing cost of the lattice structure, since AM processes are relatively costly, especially LPBF [7]. It is clear that the cost can be reduced by designing the lattice to have a ratio of \(D/L\) as large as possible in this structure; however, for a target CTE of zero, this places restrictions on the CTEs of the selecting materials.  
Using a similar assembly technique to that examined here, [PERSON] et al.'s structure [16] provided near-zero CTE, while the design in this paper can provide a wider range of CTE including near-zero, achieved with \(D/L=0.73\) and \(\alpha_{2}/\alpha_{1}=1.83\). Although other polymer-based structures could provide wider ranges of CTEs than the lattice in this paper (i.e., \(-434\times 10^{-6}\) K\({}^{-1}\) to \(396\times 10^{-6}\) K\({}^{-1}\) from [PERSON] et al. [12] and \(-300\times 10^{-6}\) K\({}^{-1}\) to \(1000\times 10^{-6}\) K\({}^{-1}\) from [PERSON] and [PERSON] [13]), the internal geometries of those structures were more complex, required additional topology optimisation software to model the structures, and still needed an advanced multi-material AM process to fabricate. The structure with the chosen parameters could provide a CTE of \(1\times 10^{-9}\) K\({}^{-1}\), which was a much lower CTE than many commercial instruments, such as \(23\times 10^{-6}\) K\({}^{-1}\) for aluminium 7075's CTE and \(10\times 10^{-6}\) K\({}^{-1}\) for stainless steel 431's CTE [25] of the working surface of commercial optical breadboards.  
Moreover, the 1 st pattern cell tessellations composed of even numbers in both the X- and \(Z\)-axis are recommended for low-CTE applications; however, further investigations are required to identify and ideally exploit the cause. An added advantage of charactering CTE via the fitted surface shown in Figure 9 is that adjustments can be made for variations in material properties or geometrical accuracy with a small number of calibration experiments.  
Figure 11: The total volume of the (\(10\times 10\times 10\)) mm unit cell by varying the ratio of \(D/L\) between 0.3 and 0.73.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 4 Results
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Literature** & **Research Methods** & **Acc (\%)** & **Se (\%)** & **Sp (\%)** & **PPV (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{This paper (multimodal)} & Resnet34-Feature Level Fusion & 95.85 & 97 & 95 & 95 & 91 \\ \cline{2-6}  & Cross-Modal Attention-Post Fusion & 94.48 & 95 & 93 & 94 & 90 \\ \cline{2-6}  & MLLSTM-Model Level Fusion & 92.51 & 92 & 93 & 93 & 88 \\ \cline{2-6}  & ADFECGDB (Resnet34) & 97.89 & 97 & 98 & 98 & 93 \\ \hline \multirow{4}{*}{Comparative experiment of this paper (unimodal)} & LSTM-Time Domain & 85.42 & 81 & 90 & 89 & 80 \\ \cline{2-6}  & CNN-Frequency Domain & 88.41 & 91 & 86 & 87 & 84 \\ \cline{2-6}  & FCN-Feature Value & 85.09 & 87 & 83 & 84 & 81 \\ \cline{2-6}  & ResNet-Time–Frequency Domain & 94.05 & 94 & 94 & 94 & 89 \\ \hline \multirow{4}{*}{Ablation study} & Remove time domain & 92.84 & 93 & 92 & 92 & 88 \\ \cline{2-6}  & Remove frequency domain & 94.36 & 94 & 95 & 95 & 90 \\ \cline{2-6}  & Remove feature values & 94.45 & 94 & 94 & 95 & 90 \\ \cline{2-6}  & Remove time–frequency domain & 84.56 & 83 & 86 & 85 & 80 \\ \hline \multirow{2}{*}{[PERSON] et al. [12]} & ICA + LMS & — & 89.41 & — & 90.42 & 89.19 \\ \hline \multirow{2}{*}{[PERSON] et al. [13]} & AIPE & — & 90.77 & — & 91.32 & 90.95 \\ \hline \multirow{2}{*}{[PERSON] et al. [23]} & QRStree & — & 61.5 & — & 61.7 & 61.6 \\ \hline \multirow{2}{*}{[PERSON] et al. [24]} & CNN + Post-processing & — & 89.1 & — & 92.8 & — \\ \hline \multirow{2}{*}{[PERSON] et al. [25]} & OctConv + ResNet & 91.8 & 90.3 & — & — & 91.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of experimental results. Figure 7: The comparison of accuracy across different methods (**a**) and the ROC curve for the feature-level fusion method based on ResNet (**b**). the robustness of the proposed methods, providing important references for their potential application in clinical and wearable health monitoring systems.
## 5 Simulation and Experimental Results
### Performance Analysis Concerning Malicious Activities

Table 9: Performance Analysis of Energy Efficiency, Packet Delivery Ratio, overhead, and Packet Loss concerning increase in malicious vehicles. performance so that the efficiency of the proposed ECC-GA-PASR is lowered by around 4%, and the earlier works such as S-AODV lowered by around 23%, ES-AODV lowered by around 21% and ECC-ACO-AODV lowered around 9%. In the earlier works, the performance is reduced from 9% to 23%, but in the proposed ECC-GA-PASR, it is only 3%. This proves that the efficiency of the proposed ECC-GA-PASR is higher than the earlier works. According to the increase in malicious activities from 0 to 50 vehicles, the packet delivery ratio of the proposed ECC-GA-PASR varies from 99% to 97%, whereas earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV vary from 74% to 55%, from 83% to 63%, and from 92% to 84%, respectively. An increase in malicious vehicles affects the performance so that the packet delivery ratio of the proposed ECC-GA-PASR is lowered by around 3%, and the earlier works such as S-AODV lowered by around 19%, ES-AODV lowered by around 20%, and ECC-ACO-AODV lowered around 9%. The packet delivery ratios of the earlier works are affected by the malicious activities, and it gets lowered by around 9-20%, but for the proposed work, it is only 2%. This proves that the proposed work performed better when compared with the others. According to the increase in malicious activities from 0 to 50 vehicles, the overhead of the proposed ECC-GA-PASR increased from 43 packets to 209 packets, whereas earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV varied from 1567 packets to 4937 packets, from 1056 packets to 3674 packets, and from 754 packets to 2286 packets), respectively. Due to the increase in malicious vehicles, the network overhead is increased from around 1500 packets to 2500 packets for the earlier works. In the case of the proposed ECC-GA-PASR, it is only 150 packets. This proves that the performance of the proposed ECC-GA-PASR is better than others. According to the increase in the malicious activities from 0 to 50 vehicles, the packet loss of the proposed ECC-GA-PASR increased from 45 packets to 169 packets, whereas earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV varied from 156 packets to 496 packets, from 102 packets to 379 packets, and from 56 packets to 201 packets, respectively. Due to the increase in malicious vehicles, packet loss is increased. In the earlier works, the packet loss is increased from 150 packets to 300 packets, but for the proposed ECC-GA-PASR, 120 packets. Comparatively, it is better than the earlier methods.
# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
###### Abstract
The requirements for pedestrian models increase with each level, with the initial model requiring detection, the higher model requiring pedestrian recognition and tracking, and the full interaction model requiring psychological and social understanding to interact in any situation. Figure 1 depicts the SAE levels and pedestrian model requirements. A pedestrian behavioral model is a level 3 of automation [10]. This means that the pedestrian model must take into account factors that influence pedestrian behavior and  
Figure 1: Levels of an AV vs. Pedestrian Model Requirement. may influence pedestrian intention and action. Incorporating and considering such factors during the design and implementation phases of algorithms is a critical requirement of behavioral models. Most early research focused on AV technology trust and adoption, communication modalities, and general AV safety perception [11; 12], but very few research efforts have been made to investigate pedestrian behavior in the context of AV interaction. Based on human driver perceptions of pedestrians and public perceptions of AVs, this cross-country survey is designed to elucidate behavioral analysis of pedestrians around vehicles. Table 1 lists the various research questions that must be answered, as well as their corresponding motivations. This study was carried out to find answers to these questions. The primary goal of this research is to understand pedestrian behavior in interactions with AVs and to investigate the factors that influence this interaction. Humans exhibit a wide range of behaviors. Age, gender, social status, local norms, and awareness all influence behavior. A survey was conducted in 27 countries between February and May 2021 with the participation of 1000 people in three languages--English, Arabic, and Urdu--to understand variation in attitudes and behaviors. While extensive research was conducted to determine the factors that influence an individual's perception of AVs, intention to adopt AVs, and awareness of AVs, the majority of the findings have been restricted to homogeneous samples from developed countries. The contributions of our paper include an in-depth analysis of AV-pedestrian interaction in various scenarios, factors that influence this interaction, and international compassion for pedestrian behavior in various regions. Concerning the research structure, the succeeding section depicts related work in AV awareness, safety perception, and trust in AVs. The theoretical framework and applied research methodology are explained in Sections 3 and 4, respectively. The data analysis and results are presented in Section 5, followed by a discussion of the research findings in Section 6, and finally, future work and conclusions are presented.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Model  
A series of tests are run using the MATLAB tool to evaluate our proposed techniques thoroughly. The tests are run on an Intel Core i5 dual-core CPU with a clock speed of 2.3 GHz, 4 GB of RAM, and the Windows 7 operating system. The simulation environment consists of 300 sensor nodes and a sink node in a 1000 m \(\times\) 1000 m squared area. All sensor and sink nodes are expected to be deployed randomly in a field area and remain stationary after deployment. The sink node is likewise assumed to be placed at (1000, 0) m. The data flow is generated using a Poisson process with a mean parameter, \(\lambda\). Furthermore, the WSN lossy connections model used in this research is described in [32]. The simulation parameters are summarized in Table 2.  
We employed the energy consumption model of [32] in our experiments.
## 6 Experiment Setup and Results  
For the experiment result, we operated two experiments: (1) coefficient approximation-based predistortion modeling and (2) BLSTM application. Note that we do not compare both approaches directly in this paper since each has a different assumption.  
To produce the results of VLC performances with coefficient-approximation-based predistortion, given measured static transfer functions of white and infrared LEDs, a quadratic polynomial of the LED in Equation (2) was applied as the general model of the transfer function. This polynomial approximation has been widely popular to model nonlinearity characteristics of LED or laser diodes [19]. Each graph shows two QAMs in the simulation: 16 and 64.  
For BLSTM experiment settings, we considered the input data as the distorted IFFT signal and target output data as the IFFT signal after the LED shown in Figure 7. We utilized our distortion model (2) to generate the distorted IFFT signal, but any distortion model or existing data based on an LED can be applied for the training. After our initial investigation, we confirmed that the BLSTM length must be more than input data size, which was 32 in our case. We also had to consider the limitation of computing resources and model complexity as well. With considering our investigation and limitation, we set the length of the Bidirectional LSTM as 70 and 90 for the first and second layers. Since we considered the indoor mobility service as an operation environmental condition, we considered the Rician fading with K factors from 4 to 20. For details of the BLSTM experiment setting, the complete list of properties and experiment settings is in Table 1. In Table 1, note that we used at least more than 25 million samples for experiment.
Common spatial light modulators are e.g., digital micromirror devices (DMD), which are capable of binary amplitude modulation using millions of switchable mirrors or liquid crystal on silicon (LCoS) devices, which allow for a manipulation of the light phase using millions of liquid crystal cells. One SLM-based technique, which fulfills all points to an extent is generalized phase contrast (GPC). It is an extension of [PERSON]'s phase contrast microscopy and utilizes a phase-only SLM as well as a stationary phase mask to display speckle-free two-dimensional intensity distributions [25; 26]. Extending this with holographic beam shaping will also enable cell stimulation in a volume [27]. When investigating a larger field of view, the instrumental effort of GPC is not necessary and the popular approach of holographic illumination can be applied. Here, an SLM displays computer-generated phase holograms. Optical reconstruction of this hologram with a coherent light source will then lead to the desired illumination pattern. Using this approach, all of the abovementioned requirements can be addressed in a volume and theoretically even through scattering media [28; 29]. In optogenetics, holographic illumination has mainly been used in conjunction with two-photon stimulation for the excitation of neurons in deep tissue, like mouse brains, but mostly using SLMs with only tens of frames per second [30; 31]. Also, fast ferroelectric SLMs were used in the past for the stimulation of blinded mouse retinas [32], albeit using the inherent modulation speed only for speckle reduction with shift-averaged holograms. A review of optical techniques for optogenetics can be found in [24]. In this paper, we present a setup that integrates the possibilities of extracellular electrical recording and fast holographic optogenetic cell stimulation for neural network analyses using a ferroelectric liquid crystal on silicon spatial light modulator. Optically, we obtain a minimal focus width of 8.4 \(\upmu\)m and a minimal temporal resolution of less than 0.6 ms based on the frame rate of our SLM in a field of view of 1.5 \(\times\) 1.5 mm\({}^{2}\). We show optogenetic single-cell excitation of action potentials in a neural network comprising neurons that are derived from human induced pluripotent stem cells, using this setup as a pathway to a deeper understanding of such neural networks.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Optimal Estimation Algorithm
#### 3.2.5 NLS-4 DVar Data-Assimilation Algorithm  
Obtaining accurate CO\({}_{2}\) concentrations and vertical profiles from satellite measurements remains challenging [8], with some algorithms providing reliable results only under very clear skies. This difficulty arises from the limitations in parameterization and simplification in the algorithms, such as the time-consuming burden of initial guesses for CO  
Figure 15: Schematic of the two-step machine learning model.  
profiles, atmospheric parameters, and their vertical distributions, as well as the simplification of calculating the weighting function matrix. [PERSON] et al. (2012) proposed a new algorithm called the Nonlinear Least Squares Four-Dimensional Variational Data-Assimilation (NLS-4 DVar)-based CO2 Retrieval Algorithm (NARA). This algorithm utilizes parameters from the LIDORT radiative transfer model and the ACOS algorithm in the forward model, optimizing state vectors to compute accurate carbon dioxide column averages, constrained by satellite measurements and prior information. The NLS-4 DVar method simplifies the process by reformulating the cost function as a nonlinear least squares problem, which eliminates the need to compute the weighting function matrix and its transpose. This reduction in computational complexity is crucial for handling large data volumes in satellite retrievals while maintaining accuracy.  
The NARA algorithm consists of data preparation, XCO2 retrieval, and bias correction. The NARA algorithm flowchart is shown in Figure 16.  
The NARA algorithm is effective for retrieving XCO2 and CO2 profiles from any GHG satellite, though it requires modifications based on satellite instrumentation. The relatively large daily standard deviation in XCO2 retrievals, especially over oceans, results from differences in observation modes, surface properties, and varying accuracies of prior values. Land and ocean surface reflections are modeled differently--Lambertian over land and Cox-Munk with a Lambertian component over water. Factors contributing to the large standard deviation include the complex ocean surface modeling with the Cox-Munk model,  
Figure 16: Flowchart of the NARA algorithm.  
variations in observation modes and satellite instrument sensitivity, and inaccuracies in prior values like initial state vectors and ensemble forecasts. Environmental variability, such as weather and surface roughness, also increases retrieval variability. Future work will focus on optimizing surface reflection models, improving observation modes, and enhancing prior value accuracy to boost the algorithm's robustness over marine areas.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Incidence Angle, Path Length, and Reflection Coefficient  
Using the mirror image method, the incidence angle, reflection coefficient, and path length of the ray for each valid reflection are calculated, and the effective reflected field strength is calculated at the receiving point [23]. Take the reflective surfaces \(m_{2}\) and \(m_{3}\) as an example, where the ray from point P arrives at point \(R\) after one valid reflection.  
Figure 9 shows that the ray emitted by the relay node \(P\) reaches the receiving point \(R\) with one reflection from the reflecting surface \(m_{2}\); the acute angle between the vector \(\overrightarrow{PH}\) and the normal vector \(\overrightarrow{n}\) of the reflecting surface \(m_{2}\) is then the angle of incidence \(\theta_{i}\), i.e., the cosine value is  
\[\cos\theta_{i}=\frac{\left|\overrightarrow{PH}\cdot\overrightarrow{n}\right|} {\left|\overrightarrow{PH}\right|\cdot\left|\overrightarrow{n}\right|}. \tag{9}\]  
Figure 9: Three-dimensional primary valid reflection map.  
The path length of one valid reflection is the sum of the distance between the relay node \(P\) and the reflection point \(H\), and the distance between the reflection point \(H\) and the receiving point \(R\), which can be determined according to the formula for the distance between two points:  
\[\left|\overrightarrow{PH}\right|=\sqrt{\left(x_{h}-x_{p}\right)^{2}+\left(y_{ h}-y_{p}\right)^{2}+\left(z_{h}-z_{p}\right)^{2}} \tag{10}\]  
\[\left|\overrightarrow{HR}\right|=\sqrt{\left(x_{r}-x_{h}\right)^{2}+\left(y_{ r}-y_{h}\right)^{2}+\left(z_{r}-z_{h}\right)^{2}} \tag{11}\]  
When a homogeneous plane wave is incident on the interface, any direction of the incident wave electric field vector should be divided into two components: one parallel and one perpendicular to the incident surface. The reflection coefficients \(H_{\parallel}\) of the horizontally polarized wave and \(H_{\perp}\) of the vertically polarized wave are calculated according to [PERSON]'s reflection law, respectively, and their expressions are  
\[H_{\parallel}=\frac{\cos\theta_{i}-\sqrt{\frac{1}{\epsilon}-\frac{\sin^{2} \theta_{i}}{\epsilon^{2}}}}{\cos\theta_{i}+\sqrt{\frac{1}{\epsilon}-\frac{ \sin^{2}\theta_{i}}{\epsilon^{2}}}} \tag{12}\]  
\[H_{\perp}=\frac{\cos\theta_{i}-\sqrt{\epsilon-\sin^{2}\theta_{i}}}{\cos\theta _{i}+\sqrt{\epsilon-\sin^{2}\theta_{i}}} \tag{13}\]  
In (12) and (13), \(\epsilon\) is the relative dielectric constant of the roadway wall.