esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelength-Dependent Nonlinear Absorption in Palladium Nanoparticles
## 1 Introduction
studied the electrochemistry and surface plasmon resonance spectroscopy of a sputtering film of Au/Pt [10]. In 2019, [PERSON] et al. used palladium film to realize SPR excitation of fiber grating, which provides a promising technology for hydrogen detection [11]. At present, the synthesis [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], characterization [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] and linear optical properties [13; 14; 15; 17] of PdNPs have been studied a lot. And several groups have already conducted investigations on the nonlinear optical absorption of PdNPs [7; 8; 9]; these studies were performed only at the wavelengths of about 500 nm and 800 nm. In fact, nonlinear optical absorption properties are also wavelength-dependent and energy-dependent. Therefore, it is necessary to systematically study the broadband nonlinear optical absorption of PdNPs in view of its applications and theory. In this contribution, we studied the nonlinear absorption of PdNPs using a broadband wavelength laser (ranging from 500 nm to 650 nm), which allowed us to know the dependence of nonlinear absorption on wavelength. This conclusion can provide theoretical support for the application of PdNPs as a broadband saturable absorber and optical limiter.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 4 Experiment and Analysis
### Experimental Environment and Data  
The configuration of this experimental design used an AMD Ryzen 7 5800H (CPU) and an NVIDIA GeForce RTX 3060 laptop GPU; the development environment was Windows 11, Pycharm2022.1.1, using a Torch1.8.0+Cu111 framework, and Pandas 1.1.5 and other software packages, and the development language was Python3.8. To verify the practicability of the proposed method in this paper, the logistics transportation supply data of a less-than-truckload (LTL) logistics transportation company between 29 April 2018 and 6 August 2022, for a transportation route from W city to K city, were introduced, and anomalies and missing data were deleted for a total of 1320 days. The datasets were sorted by day as a time axis, and the datasets were divided into training sets, test sets, and validation sets in a ratio of 7:2:1.
# Experimental Study on Torsional Shear Testing of
###### Abstract  
In order to research investigations on the shear behavior of asphalt mixture, a new shear testing device is developed which can apply torque to a prismatic specimen. This test configuration incorporates a loading application and instrumentation systems to measure and record the response of these mixtures. The loading application can be subjected to individual or combined axial and torsional loads; in particular, the axial load can be dynamically controlled to remain constant. The paper first uses the mechanical theory to analyze the stress state of a prismatic specimen under a torsional load in unconfined compression and confined compression, respectively, and illustrates the influence factor, the shear strength parameter, and the failure criterion for the torsional shear test of the asphalt mixture. Then, the size and the preparation procedure of specimen are explained, and the experimental plan is described. Finally, the torsional shear test apparatus is used to conduct two types of shear tests of asphalt mixtures. The type I test in unconfined compression consists of two conditions: under a constant loading speed (2.4 rad/min) at four temperatures (30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C), and under a constant temperature (40 \({}^{\circ}\)C) at three loading speeds (2.4 rad/min, 4.0 rad/min, and 8.5 rad/min). The type II test in confined compression is performed under a loading speed of 2.4 rad/min and a temperature of 40 \({}^{\circ}\)C, at 0.125 MPa, 0.200 MPa, 0.355 MPa, 0.465 MPa, and 0.570 MPa normal stress levels, respectively. The results prove that (1) temperatures, loading speeds, and normal stress levels are the issues to be considered on torsional shear testing; (2) the pure shear model can be realized by the prismatic specimen, therefore, the cohesion average value obtained is 0.519 MPa; (3) the compression-shear model can be achieved by the prismatic specimen similarly, so the cohesion and the friction angle are simulated based on the Mohr-Coulomb failure criterion, which are 0.546 MPa and 44.15\({}^{\circ}\), respectively; and (4) at the high temperature and low normal stress level, the Mohr-Coulomb failure criterion does not agree well with measured data, so the nonlinear failure envelope should not be ignored.  
Keywords:asphal mixture; shear failure stress; method of torsional shear test; compression-torsion analysis of the _Acticle_
## 3 Results and Discussion
### Applications of SPME-IMS System
#### 3.2.3 Effect of Extraction Time  
SPME is an equilibrium-based technique, and there is a direct relationship between the extraction amount and the extraction time. In our work, the effect of extraction time on the extraction efficiency of the CWAs was investigated. The extraction time was varied in the range of 0-20 min while other parameters were held constant. All experiments were done in duplicate to ensure reproducibility. The results (Figure 5) indicated that the extraction efficiency increased with the increased extraction time from 1 to 15 min, and then remained almost constant after 15 min. Therefore, the extraction time of 15 min was selected.  
Figure 4: Effect of ion strength on extraction efficiency.  
Figure 5: Effect of extraction time on extraction efficiency.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Electrochemical Sensors
#### 3.3.1 Selective Applications  
Electrochemical sensors have mainly been used for the selective detection of gas biomarkers. To this end, conventional, polymeric and hybrid materials have been studied. For example, Prussian Blue electro catalyst-modified carbon electrodes on wearable, paper-based sensor have been developed for H\({}_{2}\)O\({}_{2}\) detection which serves as a lung-disease biomarker [164]. As an example of polymeric sensing films, cylindrical nano-porous semiconducting polymers have permitted NH\({}_{3}\) detection in ppb levels via a redox reaction [165]. Hybrid materials, especially the ones containing 2D nanosheets, are also reported in the literature for sensing oxygen-compounds. Nonanal detection for instance has been achieved using SnO\({}_{2}\) nanosheets decorated with SnO\({}_{2}\) NPs as well as noble metal catalysts (Pt, Au, and Pd), aiming at LC early-diagnosis [59]. Solid proton-conducting electrolyte based on sulfonic acid co-functionalized cellulose NFs and GO nanosheets, has been developed for ethanol detection via oxidation with a LOD of 25 ppm [166]. More recently, Au NPs-decorated MoS\({}_{2}\) nano-flakes were used for oxygen-based VOCs detection such as the diabetes biomarker acetone, with sensor responsiveness and selectivity being increased due to electron-donation from Au NPs to MoS\({}_{2}\) (Figure 10) [69].  
Figure 10: Schematic representation of Au-MoS\({}_{2}\) nano-flakes sensing mechanisms for (**a**) hydrocarbons using MoS\({}_{2}\), leading to small signal alternation, due to dipole scattering through the electron clouds of -CH\({}_{2-}\) groups; (**b**) oxygen-based VOCs using MoS\({}_{2}\), which donate electrons to the MoS(**left**) and are oxidized by the adsorbed oxygen species, releasing electrons to MoS\({}_{2}\) (**right**); (**c**) oxygen-based VOCs using MoS\({}_{2}\) decorated with Au NPs, which increase electron concentration and, thus, oxygen species adsorption on MoS\({}_{2}\); (**d**) returning more electrons upon interaction with the oxygen-based VOCs. Reprinted with permission from ref. [69]. Copyright © 2019, American Chemical Society.
## 4 Analysis and Discussion
### Quantitative Assessment Using the Similarity-Based Indicator
In addition, the sensitivity of seven features fusion methods was calculated by referring to the calculation method of feature sensitivity in [65], and the results are shown in Table 10. The higher the sensitivity, the better the recognition effect of the feature's fusion methods. In Table 10, the sensitivity of the seven features fusion methods is ranked as follows: \(2>1>4>3>5>6>7\), indicating that Scheme 2 had the best recognition effect and Scheme 7 had the weakest recognition effect. A comparison between the interlayer debonding and Euclidean distance versus time by using scheme 2 was analyzed to investigate the relationship between them, and the results are shown in Figure 23. In Figure 23, the black and red curves represent the variation of debonding and the Euclidean distance with time, respectively. The value of the debonding increased slowly with time, indicating that the interlayer debonding was expanding. The Euclidean distance showed an increasing trend, indicating that the Euclidean distance increased with the interlayer debonding. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{5}{c}{**Weights (\%)**} \\ \cline{2-6}  & **Time-Domain Feature** & **Frequency-Domain Feature** & **Time–Frequency-Domain Features** \\ \hline
1 & 18.63 & 10.27 & 14.82 & 19.43 & 36.85 \\
2 & 16.29 & 8.57 & 15.29 & 17.54 & 42.31 \\
3 & - & 10.97 & 18.27 & 22.93 & 47.83 \\
4 & 19.27 & - & 20.82 & 19.26 & 40.65 \\
5 & 22.52 & 9.69 & - & 24.51 & 43.28 \\
6 & 23.05 & 10.29 & 22.49 & - & 44.17 \\
7 & 36.28 & 15.84 & 21.57 & 26.31 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Weights of different features of seven features fusion methods. Figure 22: Euclidean distance of seven features fusion methods.
## 2 Related Work
### Red-Teaming and Adversarial Simulation  
Red-teaming and adversarial simulation have become increasingly common approaches to evaluating cyber resilience through emulated attacks and exploratory defense testing. Originating in military practice, these methods are now widely used to assess the resilience of mission-critical systems and national infrastructure. The core concept involves deploying a red team acting as a simulated attacker to probe system vulnerabilities in a controlled environment [18]. This process helps identify hidden weaknesses, evaluate operational robustness, and test defense mechanisms under near-realistic conditions [19]. Organizations such as the RAND Corporation have published practical guidelines for conducting red-team exercises in defense and critical infrastructure contexts [20].  
Recent efforts have enhanced the rigor of red-teaming by integrating structured adversarial taxonomies, such as the MITRE ATT&CK framework [21]. This integration supports more systematic and repeatable simulations by linking specific threat techniques to known system vulnerabilities [22; 23]. As a result, red-teaming can better inform threat-aware defense strategies and connect theoretical resilience concepts with practical system exposures.  
Despite their realism, red-teaming exercises have notable limitations. Results often depend heavily on the specific attack vectors, tools, and tactics used, limiting generalizability and repeatability. Furthermore, the effectiveness of such evaluations may vary across systems and environments, making it difficult to establish standardized benchmarks for resilience.
## 4 Results and Discussion
### Effect of Ionic Concentration  
Since the generated ionic current is induced by the ionic migration of anions and cations, the properties of the electrolyte solution, such as ionic concentration, should influence the generated ionic current. Therefore, the effect of ionic concentration on the ionic current signal is examined by contacting the gold sheet with the copper working electrode (Figure 4; experimental data can be seen in Table A3 of Appendix A). It was found that, from DI water (containing no NaCl) to 5.1 mM NaCl solution, the magnitude of the ionic current signals increases with the ionic concentration and reaches a peak value when the ionic concentration is about 5.1 mM, which is about 250 times higher than that in DI water. It is known that a higher ionic concentration of NaCl solution means more Na\({}^{+}\) and Cl\({}^{-}\) ions in the electrolyte solution, as well as on the contacted metal or the electrode/electrolyte solution interfaces. Therefore, the equivalent resistance of the water phase (\(R_{s}\)) decreases with the increase in the ionic concentration of the electrolyte solution. According to Equation (4), the ionic current signal increases with decreasing \(R_{s}\), hence the increase in ionic concentration.  
However, when the ionic concentration was further increased from 5.1 mM to 7.0 mM, the magnitude of the ionic current signal started to decrease. It is reported that with the increase in ionic concentration, the increasing ionic conductivity has a peak value from 3.5 mM to 4.5 mM [41], while in our experiment, the peak value of the ionic current was 5.1 mM. It is possible that the amount of mobile Na\({}^{+}\) and Cl\({}^{-}\) ions attracted onto the interface of the two metals reaches a saturation point [42]. As the ionic concentration further increases higher than this saturation value, the interaction (such as mutual collision) between the ions at a high concentration is greatly enhanced, which hinders the ion migration, as well as the conduction of ions [43].
# Multigraphene Prepared by One-Pot Pyrolysis of Diatomite/Polypropylene Composites
## 2 Materials and Methods
### Multigraphene Synthesis by One-Pot Pyrolysis  
Pyrolysis experiments were carried out under an inert argon atmosphere with a flow rate of 200 standard-state cubic centimeters per minute (sccm) in a quartz tube placed in an electrically heated furnace (50 \(\times\) 700/10K-26C, Shanghai Yifeng Electrical Furnace Co., Ltd., Shanghai, China). The temperature of the furnace was programmed with a heater controller. Prior to the pyrolysis process, the DM/PP samples were put into the quartz boat and were placed in the heating zone of the furnace. An Argon flow with a rate of 50 sccm was introduced into the quartz tube to expel air from the tube for at least 30 min, and the flow rate was then adjusted to the experimental value for pyrolysis. The pyrolysis process was performed as follows: DM/PP was heated at 20 \({}^{\circ}\)C/min from room temperature to the pyrolysis temperature (850 \({}^{\circ}\)C, 900 \({}^{\circ}\)C, 950 \({}^{\circ}\)C, 1000 \({}^{\circ}\)C, 1050 \({}^{\circ}\)C, and 1100 \({}^{\circ}\)C) with a holding time (100 min), and the furnace was then cooled to room temperature in argon. The cooled channel residue was collected as the initial products from the quartz boat, and the residue was then immersed in hydrogen fluoric (HF) etching solution (molar ratio of HF:H\({}_{2}\)O:ethanol = 7:30:5) at room temperature overnight to eliminate any remaining DM and amorphous carbon. After being completely rinsed with ethanol and water, purified graphene was obtained after drying. The pyrolysis products were prepared by various conditions and were denoted as G\({}_{\text{a-b}}\), where the a and b referred to the DM content and the pyrolysis temperature, respectively.
## 2 Methods and Materials  
The materials and methods are briefed in this section. The data collection method is described and the theory covering signal propagation through free space is presented. The Random Forest (RF) [43; 44; 45; 46] and Particle Swarm Optimization (PSO) [38; 47; 48] methods are described as well. Additionally, the Hybrid RF-PS path loss modeling, Radial Basis Function (RBF) networks [49], the proposed hybrid path loss prediction modeling approach and performance indicators are described briefly.
## 3 Results
### Evaluation with Real-World Data
#### 3.3.1 Evaluation with Forest Fires Data  
The forest fires data set contains 517 records with 12 predictors containing meteorological and other information to predict the burned area of forest fires. A total of 27 predictors were used in the regression analysis, with the variables pertaining to the month and day transformed to dummy variables.  
The plot in Figure 5 shows a plot similar to Figure 5 of [4] using the same data and parties. We also plot the parameter estimates for the centralized analysis (which [4] was demonstrated to be almost identical to BCD with 450 iterations).  
For a relatively small privacy budget of \(\varepsilon=1\), the average coefficients are similar to those from the centralized setting. For \(\varepsilon=10\), the distributions are narrower, which is in line with the synthetic data results. The closeness of the sampling distributions to the centralized setting is likely affected by the low correlations between the predictors (with an absolute average and median of 0.08 and 0.05, respectively).  
The \(R^{2}\) values are quite low, due to the fact that the centralized \(R^{2}\) is only 0.07. Because \(R^{2}\) values for DP-BCD are generally lower than BCD, all median \(R^{2}\) values are negative for the forest fires data. The y-axis in Figure 6 is cut off at \(-0.5\), because negative \(R^{2}\) values are not informative, but that for \(\varepsilon=1.0\) and \(\varepsilon=2.0\) the median \(R^{2}\) values are \(-4.07\) and \(-0.94\), respectively. Thus, for a centralized model that already has low predictive power, adding differential privacy generally results in a complete loss of predictive power.  
Figure 5: Centralized parameter estimates (black) for forest fire analysis, with average coefficients and 95% confidence intervals for \(\varepsilon=1\) (orange) and \(\varepsilon=10\) (green), for \(\gamma=1.2\), 1000 repetitions. Parties are separated with background shading.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(N\) & **Mean** & **Median** \\ \hline
100 & 7.68 & 1.46 \\
250 & 3.82 & 0.95 \\
1000 & 2.30 & 0.47 \\
5000 & 0.85 & 0.22 \\
10,000 & 0.59 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean and median proportional absolute error over \(N\).
## 3 Metaverse Classroom: A Layered Architectural View
### Management and Support Layer
#### 3.7.3 Technical Support  
Technical support in a Metaverse classroom is essential to ensure smooth operations, as virtual learning environments rely heavily on functioning technology [16]. In-platform technical support provides students, teachers, and administrators with direct access to troubleshooting resources, helping resolve issues quickly to minimize disruptions [215]. For example, if a student has trouble accessing a virtual simulation, tech support can assist in real time, allowing the student to rejoin the lesson promptly [71]. Real-time troubleshooting, such as live chat or virtual help desks, is particularly valuable during collaborative activities, ensuring technical issues don't hinder engagement [56]. For instance, if a student's avatar becomes unresponsive during a group project, support can intervene immediately to keep the student involved. This real-time assistance helps maintain a stable, reliable learning environment [18]. Self-help resources and tutorials within the platform empower users to solve common technical issues independently, promoting autonomy and digital literacy [216]. These resources may include step-by-step guides, instructional videos, and FAQs covering topics like VR headset calibration or adjusting user settings [1]. For example, students unfamiliar with VR setups can follow a tutorial to troubleshoot minor issues without contacting support [217]. By reducing the reliance on live support, self-help tools build user confidence and contribute to a smoother classroom experience [218].  
Proactive maintenance and system monitoring are critical to preventing issues before they disrupt classroom activities [16]. Automated tools within the platform track system performance, detect irregularities, and alert support teams to potential problems [201]. For instance, if the system detects connectivity lags or server overload risks, support teams can take preventive measures to stabilize performance [219]. Proactive maintenance reduces the likelihood of major technical failures during critical learning moments, ensuring consistent, high-quality virtual interactions [16]. Finally, specialized support for educators is crucial to help teachers feel confident leading immersive digital lessons [15]. Training sessions can cover troubleshooting, managing virtual classrooms, and optimizing platform settings to prepare educators for potential technical challenges [16]. In addition, additionally, dedicated tech support can assist with tasks specific to teaching, such as setting up breakout rooms, managing virtual labs, or tracking student analytics [1]. This targeted support allows educators to focus on delivering quality instruction without being distracted by technical issues [17]. By integrating real-time assistance, self-help resources, proactive maintenance, and specialized support for educators, the Metaverse classroom ensures that technology enhances rather than disrupts the learning experience.
## 1 Introduction
In order to explore and evaluate the performance of different VIs for estimating winter wheat growth, researchers optimized the published and modified indices by using original spectrum and first-order differential processing and random band combinations. The results showed that first-order differential processing improved the correlation between the red edge band and winter wheat growth; three-band VI can break through the limitation of the number of bands on target information extraction, alleviate the saturation problem of two-band VI, and improve the monitoring accuracy of winter wheat growth [16]. However, each of the aforementioned factors only captures a certain facet of crop development data, making it challenging to accurately assess the presence or absence of nitrogen deficiency in a qualitative and quantitative manner. This constraint hinders the analysis process. Researchers have recently introduced a nitrogen nutrition index that integrates two biochemical parameters, namely plant nitrogen concentration and biomass. This index is highly responsive to changes in crop nitrogen nutrition and outperforms previous diagnostic indicators [17]. This paper aims to integrate and examine low altitude UAV hyperspectral images at the \"surface\" with ground non-imaging spectral data at a \"point\" scale. We will identify the most effective spectral indices for determining the nitrogen nutrient index of winter wheat and will establish an empirical model to describe their relationship. Our objective is to gather expertise in utilizing UAV hyperspectral technology for the inversion of the winter wheat nitrogen nutrition index.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 2 The Theory of Angle Beam Transducers for Single-Mode Excitation  
Angle beam transducers (ABTs) and wedges are generally used to excite and receive single-mode guided waves. Let \(c_{w}\) be the velocity of the pressure waves in the wedge, \(c\) be the phase velocity of the desired wave mode at a selected frequency in a composite laminate, and \(\theta\) be the incident angle of the pressure waves impinging on the structure (also called the tuning angle), as shown in Figure 1. According to [PERSON]'s law, a wave mode with a phase velocity of \(c\) will be enhanced through phase matching [29], much more so than waves of any other phase velocity, if the following condition is satisfied:  
\[sin\theta=\frac{c_{w}}{c} \tag{1}\]  
In order to adjust the tuning angle \(\theta\) to excite different wave modes, an adjustable wedge (ABWX-2001) manufactured by Olympus was used. The adjustable wedge allows the user to change the tuning angle from \(0^{\circ}\) to \(70^{\circ}\) to generate various pure-mode guided waves. In this study, an experimental setup of ABT-ABT was used, in which one ABT was used as a transmitter and the other ABT was used as a receiver. Both ABTs were set at the same tuning angle to excite and receive pure-mode guided waves in composites.