esa-sceva/satcom-chunk-collection huggingface dataset:
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 4 Data Analysis
### Statistical Analysis
#### 4.2.3 Inspecting the Inner Structural Model  
Standardized root means square residuals (SRMR) were used to evaluate the overall saturation of the XRTBM and the fit between the factors. The results showed that the saturation model was 0.022, while the estimated model was 0.058, both less than 0.08, indicating a good model fit [37]. The structural variance inflation factor (VIF) values assessed by the collinearity test were all between 1.5 and 2.3, below the threshold of 5.0 [45; 46]. There was a highly correlated structure. The internal structure model was tested on a bootstrap subsample of 5000 with unsigned options using bias correction and the BCA bootstrap method. Differences were compared at the 0.05, 0.01, and 0.001 significance levels. The results of the PLS-SEM hypothesis tests are presented in Table 7 and Figure 3. For XRU, XREU (H1: B = 0.544, \(p\); 0.001; H2 = 0.263 B, \(p\); 0.001) was positively correlated with the hypothesized ITASG. Additionally, hypothesized ID, IM, ON, and UP had highly significant p-values for XRU, XREU, and ITASG, respectively, with \(p\)-values less than 0.01. Except for IM, UP values for XREU were slightly greater than 0.01, but all were less than 0.05, also consistent with good significant relationships.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Latent Construct** & **Original Sample (O)** & **Sample Mean (M)** & **Bias** & **2.50\%** & **97.50\%** \\ \hline ID \(>\) ITASG & 0.252 & 0.249 & \(-\)0.003 & 0.171 & 0.340 \\ ID \(>\) XREU & 0.243 & 0.238 & \(-\)0.004 & 0.119 & 0.374 \\ ID \(->\) XRU & 0.346 & 0.342 & \(-\)0.003 & 0.244 & 0.461 \\ IM \(>\) ITASG & 0.134 & 0.135 & 0.002 & 0.050 & 0.212 \\ IM \(->\) XREU & 0.153 & 0.154 & 0.000 & 0.025 & 0.277 \\ IM \(->\) XRU & 0.171 & 0.173 & 0.001 & 0.048 & 0.285 \\ ON \(->\) ITA & 0.198 & 0.201 & 0.003 & 0.126 & 0.265 \\ ON \(->\) XREU & 0.291 & 0.292 & 0.001 & 0.185 & 0.401 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hetero-Trait-Mono-Trait (HTMTinference).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Latent Construct** & **Original Sample (O)** & **Sample Mean (M)** & **Bias** & **2.50\%** & **97.50\%** \\ \hline ON \(>\) XRU & 0.223 & 0.227 & 0.004 & 0.123 & 0.319 \\ XREU \(->\) ITASG & 0.263 & 0.263 & 0.000 & 0.138 & 0.391 \\ XRU \(->\) ITASG & 0.544 & 0.544 & 0.000 & 0.420 & 0.663 \\ UP \(->\) ITASG & 0.123 & 0.123 & 0.000 & 0.051 & 0.197 \\ UP \(->\) XREU & 0.136 & 0.136 & 0.000 & 0.033 & 0.248 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The outcome of the Structural Model Examination.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 7 Experimental Results  
In this section we report the results we obtained during the second stage of the proposed framework. During this stage, a custom-built UAV was used to acquire images of fire scenes and then YOLO-NAS was used for detecting and locating the region in the images. The depth was then estimated, as explained in the above section.
## 4 Method
### Parametric Matrix Based Fusion  
ADNet combines the predictions of closeness and periodicity by first weighting them with learnable parameters using element-wise multiplication, then summing the results toobtain the final prediction. This output is subsequently normalized to a range between 0 and 1 using the sigmoid function. This approach allows the model to adaptively balance the importance of closeness and periodicity, which may vary depending on time and location, thereby improving prediction accuracy. The features extracted through \(L\) dense blocks are reduced to match the output dimension. The closeness and periodic predictions, \(X_{\mathrm{C}}^{L+1}\) and \(X_{P}^{L+1}\), respectively, pass through a total of \(L+1\) convolution layers and are weighed using the learnable parameters \(W_{\mathrm{C}}\) and \(W_{P}\). The final prediction is computed as follows:  
\[\hat{X}_{t}=\sigma\Big{(}W_{\mathrm{C}}\odot X_{\mathrm{C}}^{L+1}+W_{p}\odot X _{P}^{L+1}\Big{)}, \tag{9}\]  
where \(\odot\) denotes the element-wise multiplication, and \(\sigma\) represents a sigmoid function. The final predicted value \(\hat{X}_{t}\) serves as the model's output at time \(t\).
## 2 Optical Coherent Detection Technique
### Optical Homodyne Detection  
Even the well-behaved lasers, such as the Nd:YAG laser and He: Ne laser, show frequency fluctuations due to thermal change and vibrations. One solution for this problem is to use the same laser for the local oscillator and the signal beams. This type of system is called a \"homodyne\" system. The main advantage of this system is its insensitivity towards frequency fluctuations when the path length of the two arms are equal. The optical homodyne detection technique requires interference of the two optical beams. The first beam is the input optical carrier signal with frequency \(f_{s}\), while the second optical beam is provided by a local oscillator with frequency \(f_{LO}\), as shown in Figure 2. The optical signal is demodulated directly to the baseband. It requires a local oscillator whose frequency matches the carrier signal and whose phase is locked to the incoming signal (\(f_{s}=f_{LO}\)). Information can be transmitted through amplitude, phase, or frequency modulation [28].  
The input carrier signal is given by,  
\[E_{s}=A_{s}Exp\ [-j(\omega_{s}t+\Phi_{s})] \tag{4}\]  
and the local oscillator signal is given by,  
\[E_{LO}=A_{LO}Exp\ [-j(\omega_{LO}t+\Phi_{LO})] \tag{5}\]  
The output power of the photodetector is,  
\[P(t)=P_{s}+P_{LO}+2\sqrt{P_{s}P_{LO}}\cos(\omega_{IF}t+\Phi) \tag{6}\]  
Figure 2: Optical homodyne detection.  
and the power of the modulated signal is given by,  
\[P_{s}=\frac{A_{z}^{2}}{2} \tag{7}\]  
while the power of the local oscillator signal is given by,  
\[P_{LO}=\frac{A_{LO}^{2}}{2} \tag{8}\]  
The intermediate frequency signal is given by \(\omega_{IF}\), which is the difference of the carrier signal and local oscillator signal frequency.  
\[\omega_{IF}=\omega_{s}-\omega_{LO} \tag{9}\]  
The overall phase shift observed at the detector is the difference of the phase of input carrier signal and the local oscillator signal. The overall phase shift is given by,  
\[\Phi=\Phi_{s}-\Phi_{LO} \tag{10}\]
## 1 Introduction
Similarly, [PERSON] (2018) developed a hybrid intelligent model combining a Discrete Grey Wolf Optimizer (DGWO) combined with a Support Vector Machine (SVM) to predict outcomes in SC-CO2 fracturing. The model's performance was evaluated using correlation coefficients and root mean square error, while sensitivity analysis was conducted using the Mean Impact Value method. Subsequently, [PERSON] et al. (2019) developed a hybrid AI model integrating a Backpropagation Neural Network, an Extreme Learning Machine, and SVM, using input factors such as vertical and horizontal principal stresses, injection volume, tensile strength, elastic modulus, and Poisson's ratio to predict breakdown pressure for SC-CO2 fracturing. Given the complexity of the factors influencing breakdown pressure in SC-CO2 fracturing, traditional mathematical models often fail to provide accurate predictions. In contrast, deep learning offers a promising alternative by automatically learning hierarchical representations of data, enabling complex functions to be mapped from inputs to outputs without predefined assumptions ([PERSON] and [PERSON] (2018)). By capturing the intricate relationships between multiple factors, deep learning models can identify key variables influencing SC-CO2 breakdown pressure and provide reliable predictive outcomes. In this study, we first conducted fracturing experiments to obtain breakdown pressure, considering influencing factors such as fracturing fluid type, flow rate, temperature, and confining pressure. We then compared experimental data with values calculated from breakdown pressure models based on elasticity theory. Key influencing factors were identified, and additional indoor fracturing data from the literature were incorporated with our experimental results to create a comprehensive dataset. A fully connected neural network was then applied to predict breakdown pressure and assess the relative importance of each factor. This integrated methodology significantly enhances our understanding of breakdown pressure in SC-CO2 fracturing, and provides valuable insights for optimizing fracturing operations.
## 1 Introduction  
Wireless sensor networks (WSNs) [1; 2] have attracted much interest in recent years. The use of WSNs in numerous applications, such as forest monitoring, disaster management, space exploration, factory automation, secure installation, border protection and battlefield surveillance [3], is on the rise. WSN technology is the basis of the future network \"Internet of Things\" (IoT) [4; 5]. The WSNs nodes are usually battery powered, deployed either randomly or according to a predefined statistical distribution [6] over a environment. Nodes are possibly prone to unexpected failures and malicious attacks. For example, in environment surveillance, the sensor node is exposed to an unsafe situation, making nodes likely to suffer various kinds of damage. On the other hand, the sensor node itself is very vulnerable due to the unreliable wireless connection and resource constrained features, such as limited transmission power, computing ability, storage space, _etc_.
# 3-D SRIM Simulation of Focused Ion Beam
###### Abstract  
Ion beam sputter etching has been widely used in material surface modification and transmission electron microscope (TEM) sample preparation. Due to the complexity of the ion beam etching process, the quantitative simulation of ion beam sputtering is necessary to guarantee precision in surface treatment and sculpting under different energies and beam currents. In this paper, an application-oriented incident ion beam model was first built with aberrations and Coulomb repulsion forces being considered from the Ga ion source to the sample. The sputtering process of this model on the sample was then analyzed and simulated with an improved stopping and range of ions in meter (SRIM) program. The sputtering performance of this model, the point-like incident beam and the typical Gaussian incident beam was given in the end. Results show that the penetration depth of Ga ions having 30 keV energy in silicon is 28 nm and the radial range is 29.6 nm with 50 pA beam current. The application-oriented model has been verified by our focused ion beam-scanning electron microscopy (FIB-SEM) milling experiment and it will be a potential thermal source in simulating the process of FIB bombarding organic samples.  
ion beam sputtering; Coulomb repulsion forces; Gaussian distribution ion beam; SRIM
# Highly Controlled Integration of Graphene Oxide into PAN Nanofiber Membranes
## 1 Introduction  
Nanoscale polymer fibers have been intensively investigated to implement their properties associated with high surface area, flexibility, and tunable porosity in many technical fields [1; 2]. Electrospinning is one of the common approaches to prepare polymer nanofibers due to high product rate, ecofriendly manufacturing process, and low production cost [1; 2; 3]. This process involves the generation of ultrathin fibrous threads from a polymer precursor solution in electric fields, but the resulting materials prepared from bare polymers typically exhibit inherently weak chemical and physical characteristics, often limiting their practical applications. As such, these nanofibers are often modified with organic and inorganic fillers to regulate their overall properties.  
Although incorporating filler materials into nanofibers can render controlled chemical physical properties, the preparation of diverse polymer composite nanofibers by electrospinning requires extensive optimization via trial and error [4; 5]. Even under optimized conditions, the systematical integration of excess fillers over 10 wt % into nanofibers is still challenging [6]. Here we demonstrated the capability of loading excess graphene oxide (GO) fillers into relatively hydrophilic polyacrylamideonitrile (PAN) nanofibers without significant destruction of their original structures, allowing for examining the overall properties of composite membranes impacted by the excess filler loading. Given the functional groups of GO, its surface can be easily modified with a positively charged surfactant, which makes GO more compatible in a polymer precursor solution [7]. This simple modification allowed for the reliable integration of excess GO into PAN nanofibers under the same electrospinning conditions. The resulting composite membranes did not show any notable defects, possibly providing an opportunity to understand their overall properties as a function of GO content beyond typical limits.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
###### Abstract  
Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.  
Keywords:STARFM; Landsat; MODIS; time-series; data fusion; accuracy assessment; NDVI +
Footnote †: journal: Remote Sensing, 10
# Seismic Response of Container Crane under Near-Field and Far-Field Ground Motions
## 4 Results and Discussions
### Acceleration Response  
The seismic acceleration response of the scale container crane was recorded at three locations designated as A1, A2, and A3 (see Figure 2). The maximum acceleration response is summarized in Figure 10. The maximum acceleration responses of the scale container crane at locations A1, A2, and A3 are compared with the peak ground acceleration (PGA) of matched input ground motions given in Figure 3b. As shown in Figure 10 clearly, the maximum acceleration responses under the near-field Gazli USSR Iran ground motion increased compared to the input peak acceleration. Specifically, the maximum acceleration response at locations A1 (0.5 g), A2 (0.44 g), and A3 (0.63 g) were 47%, 29%, and 85% higher than the maximum acceleration of input ground motion (PGA = 0.34 g). However, the maximum acceleration responses under the far-field ground motions decreased compared to the input peak acceleration. In detail, the maximum acceleration of the locations A1 (0.35 g), A2 (0.40 g), and A3 (0.37 g) when subjected to the far-field Mineral Virginia earthquake were 33%, 23%, and 29% lower than its PGA (0.52 g). For the far-field Nahanni Canada earthquake, there was a marginal or no decrease in the seismic acceleration response of the A3 and A2 locations. The maximum acceleration of location A3 and A2 was 0.42 (g) and 0.44 (g), respectively, as compared to the PGA of 0.44 (g). The maximum acceleration response of location A1 (0.34 g) was 23% lower than its PGA (0.44 g). Therefore, it can be concluded that the near-field ground motions have a larger effect on the seismic response of the container crane than the far-field input ground motions.  
Figure 10: Maximum acceleration response.  
Figure 9: Time history of portal drift: **(a)** Gazli USSR Iran earthquake; **(b)** Nahanni Canada earthquake; **(c)** Mineral Virginia earthquake.
###### Abstract  
Over the years, the development of adaptable monitoring systems to be integrated into soldiers' body gear, making them as comfortable and lightweight as possible (avoiding the use of rigid electronics), has become essential. Electrospun microfibers are a great material for this application due to their excellent properties, especially their flexibility and lightness. Their functionalization with graphene nanoplatelets (GNPs) makes them a fantastic alternative for the development of innovative conductive materials. In this work, electrospun membranes based on polycarbonate (PCL) were impregnated with different GNPs concentrations in order to create an electrically conductive surface with piezoresistive behavior. All the samples were properly characterized, demonstrating the homogeneous distribution and the GNPs' adsorption onto the membrane's surfaces. Additionally, the electrical performance of the developed systems was studied, including the electrical conductivity, piezoresistive behavior, and Gauge Factor (GF). A maximum electrical conductivity value of 0.079 S/m was obtained for the 2%GNPs-PCL sample. The developed piezoresistive sensor showed high sensitivity to external pressures and excellent durability to repetitive pressing. The best value of GF (3.20) was obtained for the membranes with 0.5% of GNPs. Hence, this work presents the development of a flexible piezoresistive sensor, based on electrospun PCL microfibers and GNPs, utilizing simple methods.  
GNPs; PCL; electrospinning; piezoresistive behavior; electrical conductivity; flexible sensors +
Footnote †: journal: Applied Sciences
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Methods  
The major steps involved in mapping irrigated and rainfed wheat areas include the extraction of winter wheat areas, estimation of seeding and maturity dates and the determination of kernel functions and parameters used in the SVM algorithm (Figure 2). The ground information included the latitude, longitude and water management practices (irrigated or rainfed) for the survey sites, which is an essential part during the establishment of SVM classification model. As the corresponding maximum NDVI and TIN can be extracted based on the information of latitude and longitude, the SVM classification model can be established considering the water management conditions (irrigated or rainfed).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensor** & **Year** & **Date of Imagery** \\ \hline HJ-1A/B CCD & 2010 & 9/11, 9/17, 9/27, 10/5, 10/15, 11/3, 11/13, 11/19, 11/28, 12/8, 12/16, 12/26 \\  & 2011 & 1/12, 1/24, 2/23, 3/10, 3/24, 4/9, 4/24, 5/12, 5/28, 6/7, 6/28, 7/8 \\ Landsat 8 & 2013 & 11/18, 11/27, 12/11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landsat 8 and HJ-1A/B images used in this study.  
Figure 2: Flowchart of irrigated and rainfed wheat classification.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Data Privacy  
CPS has reached unprecedented levels of performance and efficiency. However, it does not address security and privacy problems properly. Researchers have tried to balance system availability and data privacy through their studies.  
To protect the privacy of a sensor's raw observation data, [PERSON] and [PERSON] [98] introduced the concept of privacy implications and an optimization framework considering both data and inference privacy. The authors proposed a two-stage local privacy mapping for each sensor and investigated the relationship between observation data and inference privacy metrics. The simulation and experimental results proved that such an architecture could protect privacy information and local differential privacy with affordable budgets.  
[PERSON] et al. [99] proposed an approach, namely, attacker location evaluation-based fake source scheduling (FSSE), to protect source location privacy in CPS. FSSE first builds a backbone to form a source-sink path regarding the source location privacy and transmission delay. Then, it selects nodes as the fake source to assess probable attacker positions. Through analysis and simulation, the authors showed that this method performed better than compared algorithms in stable privacy level, transmission efficiency, and energy consumption.