esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Quantity of SA, JA, MeSA, and MeJA in Healthy and ALB-Infested Plants  
To determine whether plant hormones are involved in resistance of PaP to ALB, insect-resistance-related hormones were further quantified in healthy and ALB-infested trees (Table 2) (Table S3). The results showed that the content of SA in PaP bark decreased significantly after ALB infestation (\(t=-40.13\), \(df=2\), \(p<0.01\)). The JA content increased after ALB infestation (\(t=6.79\), \(df=2\), \(p<0.05\)). There was no significant difference in the quantity of MeSA before and after ALB infestation (\(t=-3.48\), \(df=2\), \(p>0.05\)), while the quantity of MeJA was significantly higher after ALB infestation (\(3.02\) times greater than healthy trees, \(t=10.06\), \(df=2\), \(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Volatiles**} & \multicolumn{3}{c}{**Concentration per Injection Volume (ng/\(\mu\)L)**} \\ \cline{2-4}  & **Healthy (Control)** & **After Mechanical Injury** & **After ALB Infestation** \\ \hline Z3 HA & 1159.64 \(\pm\) 3.27 a & 577.82 \(\pm\) 2.03 b & 313.26 \(\pm\) 3.11 c \\ Z3H & 2049.12 \(\pm\) 2.05 a & 241.89 \(\pm\) 2.18 c & 521.81 \(\pm\) 2.01 b \\ \hline \hline \end{tabular} Note: Data are means \(\pm\) SE (each group of trees). Data in the same row followed by different lowercase letters were significantly different at \(\alpha=0.05\) following one-way ANOVA. PaP, _Populus alb_ var. _pymmidalis_.  
\end{table}
Table 1: Quantities of Z3 HA and Z3H released from PaP after mechanical injury and ALB infestation.  
Figure 2: Z3 HA, Z3H, and the metabolic pathways of SA, JA, MeSA, and MeJA. Pathways in the red boxes refer to our research targets.
## 2 Related Work  
### Energy Investment Risk Assessment  
This section introduces the related work about investment risk assessment, focusing on the energy field.  
Ref. [5] considers the risk of power plants which focuses on the production of the energy, while Reference [6] chooses to pay attention to the transmission of the energy. Ref. [13] simulates a 2-period model of infrastructure investment to compute its carbon emissions, taking the climate uncertainty into consideration. Ref. [14] analyzes the impact on energy investments stemming from different emission permit classes, like gas and wind power plant. Ref. [15] employs a multistage decision model to analyze the investment in carbon capture and storage retrofits. Ref. [16] aims to research on scaling up investment on clean energy infrastructure and propose policy solutions for climate change mitigation. Ref. [17] proposes to study the impact of tourism's carbon emission based on a life cycle assessment input-output model. Ref. [18] presents an economic work to evaluate the profitability of investments in renewable technologies for the production of electricity.  
The above models only take advantage of the micro-level factors, while the mainstream methods consider the investment risk macroscopically. Moody's, Standard & Poor's, and ICRG publish the rating scores of each country around the world every year, mainly based on three factors, i.e., politics, finance, and economy [19; 20] (also see Ref. [8]), and they have found that geopolitical climate and economic environment are two crucial indexes that should be paid attention to. Ref. [10] includes technical risks as an important factor, such as research and development (R&D) capacity and technology maturity. Ref. [21] stresses that government promotion and policy coordination play a vital role for China's overseas direct investment (ODI). Ref. [22] finds that policy risk is the most important factor as it could directly influence the decision of a project, while market risk will have a bigger role with the policy and technique risks declining. Ref. [23] points out the risk of energy demands that may have a huge influence on the cooperation of the energy project. Ref. [11] focuses on the operational commercial risks and illustrates that law culture should be included, as well. Ref. [24] analyzed the impact of policy and regulatory risks, resource risk, inflation risk, and energy price risk to evaluate the renewable energy investment. Ref. [25] proposes the barriers during different decision-making stages, e.g., preliminary risk scanning stage, iterative economic appraisal process, project development stage, and capital access stage.  
Inspired by the above introduced factors, this paper proposes a new indicator system which considers the energy investment risk on both macro-level and micro-level.
## 2 Methodology
### Methods
#### 2.2.3 Validation of FAPAR  
In this study, the coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), and mean prediction error (Bias) were employed to evaluate the estimation accuracy of FAPAR:  
\[\mathrm{R}^{2}=\left(\frac{\sum_{i=1}^{\mathrm{N}}\left(\mathrm{P}_{i}-\overline {\mathrm{O}}\right)\left(\mathrm{O}_{i}-\overline{\mathrm{O}}\right)}{\sqrt{ \sum_{i=1}^{\mathrm{N}}\left(\mathrm{P}_{i}-\overline{\mathrm{P}}\right)^{2} \sqrt{\sum_{i=1}^{\mathrm{N}}\left(\mathrm{O}_{i}-\overline{\mathrm{O}} \right)^{2}}}}\right)^{2} \tag{6}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{\mathrm{N}}\left(\mathrm{P}_{i}-\mathrm{ O}_{i}\right)^{2}}{\mathrm{N}}} \tag{7}\]  
\[\mathrm{Bias}=\frac{\sum_{i=1}^{\mathrm{N}}\left(\mathrm{P}_{i}-\mathrm{O}_{i} \right)}{\mathrm{N}} \tag{8}\]  
where \(\mathrm{P}_{i}\), \(\mathrm{O}_{i}\), \(\overline{\mathrm{O}}\) represent the predicted, observed, and mean of the observed values of the samples, respectively, and N signifies the number of samples. Given the observation that FAPAR exhibited significant variation across different biome types and that the impact of lower FAPAR biome types might be underestimated when solely RMSE and Bias are utilized for assessment, the mean absolute percentage error (MAPE) and the mean percentage error (MPE), and the ratio of performance to interquartile range (RPIQ) were also evaluated [50; 51].  
\[\mathrm{MAPE}=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}\left|\frac{\mathrm{P }_{i}-\mathrm{O}_{i}}{\mathrm{O}_{i}}\right|\times 100\% \tag{9}\]  
\[\mathrm{MPE}=\frac{1}{\mathrm{N}}\sum_{i=1}^{\mathrm{N}}\left(\frac{\mathrm{P }_{i}-\mathrm{O}_{i}}{\mathrm{O}_{i}}\right)\times 100\% \tag{10}\]  
\[\mathrm{RPIQ}=\frac{\mathrm{Q}_{3}-\mathrm{Q}_{1}}{\mathrm{RMSE}} \tag{11}\]  
where \(\mathrm{Q}_{1}\) is the value below which we can find 25% of the samples; \(\mathrm{Q}_{3}\) is the value below which we find 75% of the samples. This paper evaluates the MODIS FPAR products, the FAPAR calculated using Beer-Lambert's law based on the MODIS LAI products, and the improved FAPAR that uses Beer-Lambert's law by considering FVC. The three FAPARs are abbreviated as FAPAR\({}_{\mathrm{MOD}}\), FAPAR\({}_{\mathrm{LAI}}\), and FAPAR\({}_{\mathrm{FVC}}\), respectively.
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 3 Results
### Which Studies Provide Feedback for Participants Derived from Obtained Results? (RQ5)  
Providing feedback to teachers and students is a key mechanism for translating analytical insights into pedagogical action. In educational settings, feedback serves as a bridge between data and practice, allowing teachers to refine their instructional strategies, students to reflect on their learning behaviors, and both groups to engage in more effective classroom interactions. Without such mechanisms, the potential of audio-based analytics remains largely theoretical, disconnected from the real-world contexts they are intended to support.  
In this question, we examine how studies in our corpus integrate feedback into their design. Specifically, we analyze who receives the feedback, what type of information is shared, and when it is delivered. This approach allows us to map the practical utility of audio-derived data across educational scenarios.  
However, it is important to highlight a critical limitation in the current literature: very few studies discuss the perception, acceptance, or impact of feedback mechanisms from the perspective of the actual stakeholders-teachers and students. While some papers mention user-facing dashboards or post-session summaries, most do not include empirical evaluations of how these outputs were received or used in practice, nor whether they resulted in actual pedagogical changes. This lack of user-centered assessment restricts our ability to draw conclusions about the effectiveness, usability, or educational value of these systems. We return to this gap in the discussion as a key avenue for future research.  
Of the 82 reviewed articles, only 11 (approximately 13%) explicitly report delivering feedback derived from audio features to participants. Despite being a minority, these studies provide valuable insights into the current state of feedback integration in audio-based educational research. To structure our analysis, we organize the findings according to three dimensions: who receives the feedback, what kind of information is shared, and when the feedback is delivered.
## 3 Experimental Results
### Sample Preparation and Experimental Setup
The dielectric constant \(\varepsilon_{\perp}\) was obtained by dividing the measured capacitance by the capacitance of the empty cell measured at the same voltage. In this way, I noticed that \(\varepsilon_{\parallel}\) was not strictly constant, but increased slightly when the electric field was increased (Figure 1). This phenomenon is well known and is due to two effects. The first one is due to a freezing of the director fluctuation modes that leads to a linear increase in \(\varepsilon_{\parallel}\) with the electric field [30]. The second is the Kerr effect which is microscopic in origin. It leads to a quadratic increase in the quadrupolar parameter order [31], and thus of \(\varepsilon_{\parallel}\), as a function of the electric field. This behavior is well verified in my experiments, as shown in Figure 1. In this example, the experimental data are well fitted by a law of the form \(\varepsilon_{\parallel}=\varepsilon_{\parallel}(0)+aE+bE^{2}\)[32]. The same behavior is observed at all temperatures. The temperature dependence of \(\varepsilon_{\parallel}(0)\) and of the fit coefficients \(a\) and \(b\) is shown in Figure 2.  
Measurements of \(\varepsilon_{\perp}\) were performed using a planar cell of thickness \(d=7.5\)\(\upmu\)m. In that case, only measurements at low voltage, below \(V_{c}\), are possible. In this range of voltage, I observed that the capacitance was perfectly constant, meaning that the pretilt angle was indeed equal to 0. The temperature dependence of \(\varepsilon_{\perp}\) is shown in Figure 3. From these measurements, I calculated \(\gamma\) by taking for \(\varepsilon_{\parallel}\) the value \(\varepsilon_{\parallel}(0)\) measured at low voltage. The values calculated in this limit are in very good agreement with those measured by [PERSON] et al. [33] in a thick planar sample of thickness \(d=75\)\(\upmu\)m (Figure 4). Figure 1: Dielectric constant \(\varepsilon_{\parallel}\) measured in a homeotropic sample as a function of the applied voltage. The crosses are experimental points and the solid line is the best fit to a parabola. \(T=35\,\mathrm{\SIUnitSymbolCelsius}\). Figure 3: Dielectric constant \(\varepsilon_{\perp}\) measured below the onset of instability in a planar sample of thickness \(d=7.5\) μm. Figure 2: Variation with temperature of the dielectric constant \(\varepsilon_{\parallel}(0)\) extrapolated at zero voltage (**a**) and of the fit parameters \(a\) and \(b\) (**b**). The solid lines are just guides for the eye.
## 5 Discussion
### Regional Adaptability Challenges in Transfer Learning  
The reliance on a limited training dataset in this study may entail multiple methodological challenges. The model's sensitivity to sample stochasticity increases, whereby extreme values can skew feature importance rankings and potentially misclassify secondary geological factors as key drivers. Additionally, the capacity to identify nonlinear couplings among geological factors is constrained in high-dimensional feature spaces. Small sample sizes also limit model generalization, rendering extrapolation prone to imbalances between overfitting and underfitting, particularly in rare geological scenarios. Although ten-fold cross-validation enhances evaluation stability through iterative sample partitioning, it cannot fully address the fundamental constraint of sample scarcity on model generalization.  
Moreover, the disparities between Songpan (source domain) and Mao County (target domain) impose additional challenges for transfer learning. For instance, Songpan has fewer seismic points concentrated in the northeastern direction, while Mao County's proximity to the Beichuan-Yingxiu and Maowen Fault Zones subjects it to stronger tectonic influence, resulting in a larger number of seismic points that are more dispersedly distributed across its territory. This spatial heterogeneity in seismic activity may disrupt the transferability of fault-related feature associations derived from Songpan's dataset. Human settlement patterns also exhibit stark contrasts: Songpan's population is predominantly clustered along central road networks, whereas Mao County has a more evenly distributed population. Such differences in the spatial configuration of human activities introduce unmodeled confounding factors--for example, the model trained on Songpan data may  
Figure 11: Debris flow susceptibility analysis for Songpan (**a–c**) and Mao County (**d–f**) using SVM, RF, and XGBoost models.  
overemphasize the proximity effects of roads and settlements, which diminishes predictive relevance in the context of Mao County's dispersed settlements.  
Collectively, these factors--including the limited training dataset and the pronounced disparities between Songpan and Mao County--likely underlie the observed decline in prediction accuracy for Mao County.
## 3 Results and Discussion
### Factors That Impact the Filtration Process
#### 3.2.2 Packing Density  
Figure 10 depicts the dendrite morphologies of these four cases under the same mass load per unit area. The predominant diffusion leads to an isotropic distribution of particles around the fiber surface. However, it is apparent that for a fixed fiber shape (aspect ratio), the dendrite structures exhibit similar morphologies for all packing densities. This suggests that the fiber shape (aspect ratio) plays a more significant role in the growth of dendrite structures, while the fiber size (packing density) has no significant impact.  
Figure 11a also indicates an exponential relationship between \(\Delta P/\Delta P_{0}\) and \(M\), and fibers with a high packing density produce a sharp increase in \(\Delta P/\Delta P_{0}\). That is, there is a strong positive correlation between the slope \(k\) and the fiber perimeter, and the linear equation (\(k=0.0384\)_perimeter_\(-1.7885\)) again describes the slope \(k\), except in Case 9, which might be because the perimeter is too large and beyond the scope of application (\(71.7<\)_perimeter_\(<177\)).  
Figure 11: (**a**) Logarithmic scaling of the normalized pressure drop and (**b**) dynamic changes in normalized capture efficiency with different packing densities.  
Figure 10: Dendrite structures with different packing densities: (**a**) Case 7, \(a=2.5\%\); (**b**) Case 2, \(a=5\%\); (**c**) Case 8, \(a=7.5\%\); (**d**) Case 9, \(a=10\%\).  
During the dust-loading process, the deposited particles more easily become part of the filtration medium when there is a higher packing density, and then the fibers' surface geometry changes due to the larger cross-sectional area. Namely, the capture efficiency rises as the particle capture mechanism proceeds. As shown in Figure 12, \(\lambda\) cannot continue to rise as the packing density increases, indicating that there must be a critical maximum of \(\lambda\). Specifically, as the packing density increases from 2.5-6%, the non-steady-state capture efficiency increases relatively rapidly, reaching a critical maximum (approximately 0.58) when the packing density is greater than 6%. This implies that, in the design of filtration media, it is not advisable to increase the packing density too much, as this will increase the pressure drop, rather than enhance the normalized capture efficiency.
# Study on Noise Model of an Automotive Axial Fan Based on Aerodynamic Load Force
## 2 Establishment of Axial Fan Blade Loading Force
### BEM Theory  
Blade element theory assumes that the blade can be divided into many elements along the radius direction and each element is relatively independent of the other, which can be regarded as a two-dimensional model. In this way, the aerodynamic force on each element is calculated based on the local flow conditions. Then, the aerodynamic force on each element along the radius direction is integrated and the total aerodynamic force is calculated. The momentum theory assumes that the pressure or momentum loss on the rotor plane is caused by the flow through the blade element. Each blade element segment connects tangential velocity with tangential force through the conservation of the circumferential moment of momentum and connects axial velocity with axial force using the momentum theorem. The combination of the blade element theory and momentum theory embodies the BEM theory.  
According to the classical blade element theory, the flow tube section is discretized into several annular elements with a height of \(\mathrm{d}r\). For ring elements, the assumptions are as follows: (1) The radial properties of ring elements are independent; and (2) In each ring element, the force exerted by the blade on the flow is constant. In view of this, in a flow tube with radius \(r\) and width \(\mathrm{d}r\), the mass flow rate of air is  
\[\mathrm{d}\dot{m}=\rho v\mathrm{d}\mathrm{A}=2\pi\rho v\mathrm{d}r \tag{1}\]  
The pull force \(\mathrm{d}T\) on the control body is  
\[\mathrm{d}T=(v_{1}-v_{0})\mathrm{d}\dot{m}=4\pi\rho v^{2}r\mathrm{d}r \tag{2}\]  
In the cooling fan, the motor drives the blade to rotate and accelerate the air and the moment of air on the blade is the resistance moment. Using the moment of momentum theorem, the torque \(\mathrm{d}M\) in the \(\mathrm{d}r\) flow tube can be written as  
\[\mathrm{d}M=(\omega_{1}r-\omega_{0}r)r\mathrm{d}\dot{m}=4\pi\rho v\omega_{f}r ^{3}\mathrm{d}r \tag{3}\]  
In the formula, \(\omega_{f}\) is the circumferential induced angular velocity of the fan blade rotation plane.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 3 ID-Based Side-Channel Authentication System
### ID-Based Side-Channel Authentication Scheme
#### 3.1.2 Resistance against Side-Channel Attacks  
One big concern for side-channel authentication is that the shared secret key can be extracted by the attackers using the side-channel leakage. To mitigate the risk of such attacks, the system can apply the following changes. First, the side-channel information near the public data should be protected by side-channel countermeasures such as masking. Second, only the side-channel information that is far from the public data is used in the authentication. For normal AES, we only use the middle round in the authentication. Similarly, for \(n\)-round AES, several rounds near the public data are not used in the authentication.
#### Internet of Remote Things
* Exploring the future of IoRT, this study examines research trends and challenges while also spotlighting promising technologies. Focusing on the 3 GPP (3 rd Generation Partnership Project) standardization roadmap, it highlights the crucial role of upcoming research and standardization efforts in bridging remote connectivity gaps and paving the way for energy-efficient IoRT environments in the 6G era.
# Multiscale YOLOv5-AFAM-Based Infrared Dim-Small-Target Detection
## 3 Experiments
### Experiments and Analysis
#### 3.3.2 Comparison Experiments on Drone Vehicle  
To verify the robustness of the algorithm, we conducted experiments on the DroneVehicle datasets. The calculation indicators are shown in Table 7. The reason for choosing this dataset is that it is also a large infrared small-target dataset captured by drones. Similar to the experiment on the ISVD, we also used 10 algorithms in this experiment. Based on the characteristics of different sizes of targets in the DroneVehicle, we compared and studied the designed algorithms using the three-scale fusion methods designed in the article. We used the most suitable scale fusion scheme and compared the best experimental results as follows: This indicates that our algorithm has outstanding performance compared to YOLOv7, [EMAIL_ADDRESS] only differs by 0.2%, but the parameter quantity is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **P** & **R** \\ \hline YOLOv5s & 0.814 & 0.376 & 0.795 & 0.817 \\ \hline YOLOv5m & 0.824 & 0.381 & 0.825 & 0.818 \\ \hline YOLOv5l & 0.820 & 0.384 & 0.831 & 0.802 \\ \hline YOLOv7 & 0.856 & 0.385 & 0.807 & 0.855 \\ \hline Mask R-CNN & 0.825 & 0.389 & 0.815 & 0.825 \\ \hline RoITransformer & 0.802 & 0.371 & 0.793 & 0.801 \\ \hline RetinaNet & 0.789 & 0.351 & 0.771 & 0.739 \\ \hline Faster R-CNN & 0.821 & 0.392 & 0.818 & 0.823 \\ \hline Cascade Mask R-CNN & 0.819 & 0.387 & 0.815 & 0.818 \\ \hline ours & 0.857 & 0.396 & 0.803 & 0.857 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison experimental results on ISVD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Backbone** & **Params** & **Runtime (fps)** \\ \hline YOLOv5s & CSPMarknet53 & 14.1 M & 73.5 \\ \hline YOLOv5m & CSPMarknet53 & 40.8 M & 67.1 \\ \hline YOLOv5l & CSPMarknet53 & 89.3 M & 54.9 \\ \hline YOLOv7 [33] & CSPMarknet53 & 72.1 M & 16.0 \\ \hline Mask R-CNN [34] & ResNet50 & 242.0 M & 13.5 \\ \hline RoITransformer [35] & ResNet50 & 273.0 M & 7.2 \\ \hline RetinaNet [36] & ResNet18 & 145.0 M & 15.1 \\ \hline Faster R-CNN [14] & VGG16 & 58.3 M & 5.3 \\ \hline Cascade Mask R-CNN [15] & ResNet50 & 368.0 M & 16.1 \\ \hline ours & CSPMarknet53 & 13.2 M & 78.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental models.  
only one-fifth of it. Our algorithm achieves good performance with a low parameter count. Compared to YOLOv5s, [EMAIL_ADDRESS] improved from 58.9% to 63.8%, and [EMAIL_ADDRESS]:0.95 improved from 28.1% to 32.8%. It can be proven that the detection bounding box of the algorithm is more accurate.