esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Visualization Image Based on Anthocyanins Content in Rice  
The effectiveness of the HSI image to visualize the chemical content in powder samples has been proven by several researchers. HSI has been successfully used to detect and quantify apricot in almond powder [43] and detect peanuts and walnuts in wheat powder [44]. In this study, PLS images were generated to visualize the content of anthocyanins in black rice powder and seeds. The chemical image provided the spatial distribution and the anthocyanins concentration of the sample, which are essential to assist in anthocyanins determination. Anthocyanins content in the powder sample was clearly confirmed at a range from 0.08 to 2.07 mg/g by the corresponding color bar shown in Figure 4A, in which the color changes from blue to red following the increase in anthocyanins concentration.  
Figure 4: A visualization of anthocyanins based on their concentration in rice powder (**A**) and seeds (**B**).  
Meanwhile, the color of seeds within a single variety varied, indicating the anthocyanins content variability (Figure 4B). The color gradation based on the concentration could not distinguish the group of seeds based on the anthocyanins content due to the variability of the anthocyanins content within a single variety. In this experiment, the anthocyanins content was examined based on the rice variety. The evaluation of the micro-components of rice could not be done for every single seed due to the minimum weight requirement in the extraction process. Hence, to justify the prediction on a single seed, the average anthocyanins content within each variety was calculated (Figure 5). The average value was compared to the reference value from HPLC and was followed by calculating the root mean square error (RMSE) to evaluate the prediction accuracy.  
The average and the broadest error between mean prediction and reference values were 0.16 and 0.49 mg/g, respectively. The RSME value was 0.21 mg/g, slightly higher than the SEP value of the model of 0.16 mg/g. Among thirty-two varieties, only seven varieties revealed an error higher than the RMSE value, and four of them were varieties with high anthocyanin concentration (more than 1 mg/g). In this experiment, only six varieties contained anthocyanins concentrations of more than 1 mg/g, which possibly affected the model's development. The prediction plot for all varieties is presented in Figure 6.  
Figure 5: Anthocyanins prediction for every seed of the four examined rice varieties.  
Figure 6: Prediction plot of anthocyanins in rice based on the spectrum of a single seed.
## 2 Characterization and Modeling of the Fault Ride-Through Process in Doubly Fed Wind Turbines
### Structured Model of Electromechanical Transient of Doubly Fed Wind Turbine
#### 2.2.3 Fault Ride-Through Operation Active and Reactive Power Control Module
\tag{5}\]  
where \(K_{\text{Id}\_\_\_\text{start}}\) is the active current coefficient of recovery starting point; \(I_{\text{dset}\_\_\_\text{start}}\) is the active current control of recovery starting point; \(I_{\text{d}FRT}\) is the active current during fault ride-through; and \(P_{\text{FRT}}\) is the active power during fault ride-through;  
The mathematical expression for the reactive power control method for fault recovery starting point control is as follows:  
\[\left\{ \begin{array}{l}I^{\prime}_{\text{dref}}=K_{\text{Id}\_\_\text{start} }\cdot I_{\text{d0}}+I_{\text{qset}\_\_\text{start}}\\ I^{\prime}_{\text{dref}}=I_{\text{q0}}\\ Q_{\text{ref}}=Q_{FRT}\end{array} \right. \tag{6}\]  
where \(K_{\text{Id}\_\_\text{start}}\) is the recovery starting point reactive current factor; \(I_{\text{qset}\_\_\text{start}}\) is the recovery starting point reactive current control; and \(Q_{\text{FRT}}\) is the active power during fault ride-through;  
3. Fault Recovery Process Control Model;  
The active control modes of fault recovery process control are immediate recovery without control, specified slope recovery, and recovery by inertia curve; the reactive control modes are immediate recovery without control and recovery control by inertia curve. The mathematical expression of the active control mode of fault recovery process control is as follows:  
\[\left\{ \begin{array}{l}P_{\text{ref}}=K_{\text{slope}}\cdot t+P_{\text{ accept}}\\ P_{\text{ref}}=P_{T_{3}}+(1-\text{e}^{(-\frac{t-T_{3}}{T_{p}})})\end{array} \right. \tag{7}\]  
where \(K_{\text{slope}}\) is the active recovery slope of the fault process; \(t\) is the recovery time; \(P_{\text{icept}}\) is the longitudinal intercept value of the active power of the recovery process; \(P_{T3}\) is the initial value of the active power of the fault recovery process; \(T_{p}\) is the active inertia recovery time constant of the fault recovery process, and \(T_{3}\) is the start time of the fault recovery process. The mathematical expression for the reactive power control method for fault recovery process control is as follows:  
\[Q_{\text{ref}}=Q_{T_{3}}+(\text{e}^{(-\frac{t-T_{3}}{T_{q}})}-1) \tag{8}\]  
where \(Q_{T3}\) is the initial value of reactive power for the recovery process, and \(T_{q}\) is the reactive inertia recovery time constant for the fault recovery process.
## 4 Propeller Theory for HAPSs
### Blade Element Momentum Theory
Taken together, these limitations highlight that, while BEMT can still provide useful first-order estimates for low Reynolds number propeller performance, it must be applied with caution. To mitigate these issues, an enhanced BEMT approach was proposed in [112], validated for Reynolds numbers between \(4\times 10^{4}\) and \(8\times 10^{4}\). Their method integrates locally computed airfoil polars using XFOIL for each blade section and operating point, ensuring that aerodynamic input reflects the actual operating conditions. Additionally, it incorporates a rotational stall delay correction by scaling the lift coefficient using a function of local blade radius and inflow angle: \(C_{L}~{}=~{}C_{l}\cdot f_{sd}(r,\phi)\) where \(f_{sd}(r,\phi)\) represents a stall-delay factor that captures the increased lift observed near the root due to centrifugal and Coriolis effects in rotating systems. To extend the applicability beyond stall, the method also blends XFOIL-derived pre-stall curves with empirically constructed post-stall extensions. The transition is implemented linearly over a \(10^{\circ}\) interval centered at the stall angle, ensuring both numerical robustness and aerodynamic continuity. This treatment is particularly important for propellers with high pitch angles, which may operate at low advance ratios or in static thrust conditions. In such cases, local blade sections can experience angles of attack well beyond the linear regime, even reaching deep post-stall behavior. Accurate modeling in this region is essential for reliable thrust and torque prediction. When direct XFOIL computations fail to converge at high angles of attack--often beyond \(15^{\circ}\)--extrapolation techniques become necessary to complete the polar curves. One established method is that of Montgomery [113], which analytically extends lift and drag coefficients based on geometric and asymptotic assumptions. Another widely used approach is the Viterna-Corrigan model [114], originally developed for wind turbines, which constructs smooth and physically consistent polar extensions using empirical formulations. These models are commonly integrated into BEMT codes to ensure stability and accuracy when simulating propellers under extreme inflow conditions.
## 3 Results
### Analysis of the Results Classified by Inter-Satellite Ground Station  
Figure 11 indicates the temperature variations observed in 2021, using the ground station communication system exclusively with data obtained from SatNOGS. The temperature information collected through this system is similar to the obtained through intersatellite link communication.  
Figure 11a-c illustrates the exterior temperatures recorded in November 2021, and received via radio frequency (RF) through the SatNOGS platform. Comparing it with the information received via GlobalStar in July 2020, the exterior temperature demonstrates a comparable pattern, with minimums and maximums remaining within the range of \(-30\)\({}^{\circ}\)C and \(50\)\({}^{\circ}\)C, respectively.  
Figure 11d illustrates the interior temperature recorded in November 2021, this data was received via radio frequency (RF) through the SatNOGS platform. Comparing it with the information received via GlobalStar in July 2020, the internal temperatures demonstrate a comparable pattern, with minimums and maximums that remain between \(-1\)\({}^{\circ}\)C and \(32\)\({}^{\circ}\)C. The information on the internal temperatures was recorded using sensors located in the OBC, EPS, and radio.  
Figure 12 depicts the behavior of the battery voltage during the month of September 2021. After one and a half years of operation, it can be observed that the charging periods have extended, while the system's current consumption has reduced.  
It is important to note that the information transmitted through GlobalStar and received by SatNOGS is similar, highlighting the reliability of telemetry data obtained through both communication methods. However, in the case of satellite-to-satellite communication (via GlobalStar), it turned out to be better because more points of data were retrieved. In the case of GlobalStar communication, different packet length configurations were utilized. The GlobalStar modem has five options for the packet length starting at 9 bytes per packet. The biggest packet length is 144 bytes per packet.  
Figure 10: Relationship between battery voltage and input and output Current.
## 6 Sensitivity Analysis  
In this study, the cosine amplitude method (CAM) was used in a sensitivity analysis to determine the degree of importance of the input parameters in influencing the collapse settlement and coefficient of stress release parameters of sandy gravel soil. The degree of importance can be estimated through Equation (31):  
\[R_{i}=\frac{\sum_{j=1}^{n}x_{ij}\cdot y_{j}}{\sqrt{\sum_{j=1}^{n}x_{ij}^{2}\cdot \sum_{j=1}^{n}y_{j}^{2}}} \tag{31}\]  
where \(x_{ij}\) is the \(i\)th variable for \(j\)th data point, and \(y_{j}\) is the dependent variable for the jth data point. When \(R_{i}\) is closer to 1, it indicates higher importance for the input parameter in estimating the output parameter, and if \(R_{i}\) is zero, there is no correlation.  
Figures 14 and 15 present the importance degree of importance of the input variables based on the measured and predicted values for the collapse settlement and coefficient of stress release, respectively. As the figures show, SC and Dr were the most and least important parameters for predicting collapse settlement. In contrast, Dr and Sc were the most and least important parameters for predicting the coefficient of stress release. Moreover, the differences in \(R_{i}\) based on the predicted and the measured values of collapse settlement for the SC, \(\sigma_{n}\), SL, and Dr variables were 0.57%, 0.05%, 0.05%, and 0.05%, respectively. These values for the coefficient of stress release were 0.01%, 0.13%, 0.02%, and 0.02%, respectively. The small percentages for these differences, in turn, indicate the high precision of the EPR models in predicting the collapse settlement and coefficient of stress release.  
Figure 14: Importance of the variables based on CAM method for collapse settlement.
# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 3 Reliable Computing Environments
### System Overview  
We assume a typical cloud service provider, Amazon EC2, where a set of resources is available to users via VM instances that may be provisioned and charged per time unit. Typically, the price is defined in such a way that VM instances with the most powerful processors have the highest cost and those with least powerful processors are cheaper. VM instances are charged according to one of the following models: on-demand, spot block, and SIs. The last option represents the cheapest alternative, although the instance can be interrupted with two minutes of warning, causing the early termination of the running task.  
Figure 1 shows the proposed system for building reliable virtual cluster execution environments. In this scenario, users submit their workflows to the cloud service provider (CSP) and specify the budget limit and deadline based on the workflow's longest task. The CSP reserves the necessary resources, in the form of VM instances, from the cloud infrastructure to execute workflow applications with budget and deadline constraints. Instances differ in type (t2.small, etc.) and pricing model (e.g., on-demand, spot block, and spot instances). Workflow deadlines become activated right after submission. The CSP implements four main modules: (a) scheduler module; (b) payment model selector module; (c) container controller module; and (d) interrupt event handler module. The scheduler module has no knowledge of when workflow applications arrive. It decomposes users' workflow applications into a set of tasks and runs the resource allocation algorithm that creates task-to-instance mappings. This module interacts with all the other three, receiving monitoring information and managing the virtual cluster execution environments. The payment model selector gives instructions on behalf of scheduler about the instance model to choose to execute a specific task. It is the responsibility of the container controller module to launch and manage containers on top of VM instances over which tasks will execute. A container has full access to its VM resources. The set of containers will form the user's virtual cluster executing environment. The interrupt event handler module is in charge of monitoring spot interruption events and of informing the scheduler module to find another instance to which the container executing the task will be migrated. It is also the interrupt event handler module's duty to monitor the spot request fulfillments. The objective is to mitigate the unreliable nature of SIs, and control spot request fulfillments. A container encapsulates the task execution environment and is the unit of migration in the system. Each container runs a single task and can be multiplexed among various tasks over time as one task finishes executing and another one is ready to start.
# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 2 Research Approach
### Database and Search Strategy  
The review adopts a concept-centric approach, structuring the analysis around key theoretical models and predictors relevant to educational technology adoption rather than being limited to specific journals or timeframes (cf. [1]). This strategy supports a deeper exploration of key constructs while minimizing biases associated with dominant publication venues or temporal trends.  
To ensure coverage of high-quality, peer-reviewed research, the Web of Science (WoS) Core Collection database was exclusively used, with no restrictions on category or publication date. Both journal articles and conference proceedings were included. This approach enabled the identification of relevant studies that offer insights into the factors, impacts, and practical considerations shaping the integration of GenAI in education.  
The search strategy was designed to capture studies involving generative AI technologies--such as large language models (LLMs), GPT, DALL-E, and other tools capable of producing human-like content--and to focus on theoretical and empirical discussions of their adoption. The search string used was as follows:  
(\"generative AI\" OR \"generative artificial intelligence\" OR \"large language model\"\" OR \"LLM\") AND ((\"theor\"OR \"model\") AND (\"adoption\" OR \"acceptance\")) AND (\"education\" OR \"learn\")  
The search was applied using the \"TOPIC\" filter (title, abstract, author keywords, and Keywords Plus) in the WoS Core Collection. Truncation (e.g., \"LLM\") was used to include multiple relevant term variants.
## 2 Methodology
### Ensemble Model Creation
The weights \(w_{1}\), \(w_{2},...\), \(w_{n}\) can be optimized to the mean absolute percentage error (MAPE) [31] of the ensemble model. The MAPE-based optimization can be formulated as a constrained optimization problem as follows:  
\[\min_{w_{1},\ w_{2},...,\ w_{n}}\frac{1}{m}\sum_{j=1}^{m}\left|\frac{y_{i}- \sum_{i=1}^{n}w_{i}\times f_{i}(x_{j})}{y_{i}}\right| \tag{2}\]where \(y_{i}\) is the actual value of the \(j-th\) observation; \(f_{i}(x_{j})\) is the prediction from the \(j-th\) observation; \(w_{i}\) is the weight assigned to the \(i-th\) base model; and \(m\) is the number of observations. It is constrained to \(\sum_{i=1}^{n}w_{i}=1\) as well as \(w_{i}\geq 0,\ \forall i\). After generating the results of an energy forecast model for a future time horizon, the main goal of an energy trading entity is to minimize the total cost of procuring energy across all hours while also minimizing risk [32]. The total cost occurs after accounting for all the revenues of the underlying markets [33]--such as the Day-Ahead Market, Real-Time Market, Futures Market, Demand Response programs [34], and the penalties for energy shortfalls during the trading procedures [35] as well as the standard deviation minimization in the second term of (3)--to introduce the risk-aversion factor and the correlations of markets. Previous studies regarding futures hedging in electricity retailing [36] depicted benefits of hedging against the volatile spot prices in the future markets [37] and, following this context, the next mentioned objective function could result in an energy mix optimal to the market conditions at each time slot of the market. The cost of energy for a given hour is calculated based on the first term of (3) since the prices at each of the markets consist of a forecast for the future, which is why we should take into consideration the volatility of them based on past data. Summarizing, the primary objective, minZ, targets to minimize both the expected costs and the exposure to price volatility. The summation of \(a\) and \(b\) to \(1\) ensures a proper balance between cost and risk.
## 4 Initial Trajectory Generation
### B-Spline-Based Trajectory Generation
#### 4.7.1 Convex Hull Property  
Among the properties of the B-spline, the convex hull property is the most significant property due to its capabilities for checking the dynamical feasibility and the collision. How convex hull property is incorporated for calculating dynamical feasibility is given in (32). As shown in Figure 3, \(d_{h}>0\) and \(d_{h}>d_{c}-r_{h}\) should be held for a considered point in the trajectory to ensure a collision-free trajectory, where \(d_{c}\) is the distance between a given control point and its closest obstacle position. In \(d\)th-order B-spline, a convex hull is formed by connecting any successive \(d+1\) control points, e.g., \(\mathbf{p}_{i},\mathbf{p}_{i+1},\mathbf{p}_{i+2},\ldots,\mathbf{p}_{i+d}\) or union of all consecutive control points that lie on the corresponding B-spline curve [69]. Moreover, \(r_{h}\) can be substituted with \(d_{ij+1}+d_{i+1,j+2}+d_{i+2,j+3}\) since \(r_{h}\leq d_{ij+1}+d_{i+1,j+2}+d_{i+2,j+3}\), \(d_{h}>d_{c}-(d_{i,j+1}+d_{i+1,j+2}+d_{i+2,j+3})\), where \(d_{i,j+1}=\|\mathbf{p}_{i+1}-\mathbf{p}_{i}\|\), \(d_{i+1,j+2}=\|\mathbf{p}_{i+2}-\mathbf{p}_{i+1}\|\) and \(d_{i+2,j+3}=\|\mathbf{p}_{i+4}-\mathbf{p}_{i+3}\|\). As mentioned in [19], the following condition should hold for collision-free trajectory planning:  
\[d_{i,i+1}<\frac{d_{c}}{3},\quad d_{c}>0,\quad i\in\{1,2,3\}. \tag{33}\]
## Appendix A
If re-representing Equation (11) by using Equation (12), then  
\[b_{1}=\frac{4}{T}\int_{-T/4}^{T/4}p_{b}(t)dt\equiv\frac{4}{T}P_{b} \tag{13}\]  
where \(P_{b}=\left\{f_{b}(c)t+\frac{1}{2}f_{b}^{\prime}(c)(t-c)^{2}+\frac{f_{b}^{ \prime\prime}(c)}{3!}(t-c)^{3}+\frac{f_{b}^{\prime\prime\prime}(c)}{4! }(t-c)^ {4}\right\}\Biggr{|}_{\frac{-T}{4}}^{\frac{T}{4}}\). For the purpose of increasing the accuracy of the obtained result of the constant \(P_{b}\) from Equation (13), we separate the integral region [-T/4, T/4] into two regions where each region has the same interval (i.e., [-T/4 0] and [0 T/4]). The center positions for each region are assigned to be the center point to obtain the Taylor polynomial for each region (i.e., \(c_{1}=-\)T/8 and \(c_{2}=-\)T/8). If integrating these two regions, then the constant \(P_{b}\) can be obtained:  
\[P_{b}=\frac{\gamma_{1}}{\omega}Q_{b}(\chi) \tag{14}\]  
Where  
\[Q_{b}(\chi)=\left\{ \begin{array}{l}\Bigl{(}1.7071^{\chi+2}-0.2929^{\chi+2} \Bigr{)}\Bigl{(}\frac{1}{2\sqrt{2}}-\frac{\pi^{2}}{192\sqrt{2}}\Bigr{)}\pi+ \frac{(\chi+2)\pi^{3}}{384}\Bigl{(}1.7071^{\chi+1}-0.2929^{\chi+1}\Bigr{)}\\ +\frac{(\chi+2)\pi^{3}}{384}(\chi+1)(1.7071^{\chi}-0.2929^{\chi})\end{array} \right\}\]  
and substituting the result of Equation (14) into Equation (13), then  
\[\therefore\,b_{1}=\frac{2}{\pi}\gamma_{1}Q_{b}(\chi) \tag{15}\]  
Re-rearranging the results in Equations. (10) and (15) after expanding the results, then  
\[a_{1}=A^{\chi+2}\kappa_{1}(\chi),b_{1}=A^{\chi+2}\kappa_{2}(\chi) \tag{16}\]  
Where  
\[\kappa_{1}(\chi)=-\frac{4}{\pi}\frac{R}{(\chi+2)(\chi+3)},\kappa_{2}(\chi)= \frac{1}{\pi}\frac{R}{(\chi+1)(\chi+2)}2^{-\chi}Q_{b}(\chi) \tag{17}\]  
As previously stated, the value of constant \(\chi\) is located within \(-1\) to \(0\), and therefore, the values of \(\kappa_{1}\) and \(\kappa_{2}\) in Equation (17) have negative and positive values, respectively.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
###### Abstract  
Object detection remains a pivotal aspect of remote sensing image analysis, and recent strides in Earth observation technology coupled with convolutional neural networks (CNNs) have propelled the field forward. Despite advancements, challenges persist, especially in detecting objects across diverse scales and pinpointing small-sized targets. This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. First, the introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the network's parameter count, thereby expediting the detection process. To tackle multi-scale object detection, the paper proposes the SEF module, an enhancement based on SEConv. Second, an ingenious Efficient Multi-Scale Attention (EMA) mechanism is integrated into the network, forming the SPFFE module. This addition augments the network's feature extraction capabilities, adeptly handling challenges in multi-scale object detection. Furthermore, a dedicated prediction head for tiny object detection is incorporated, and the original detection head is replaced by a transformer prediction head. To address adverse gradients stemming from low-quality instances in the target detection training dataset, the paper introduces the Wise-IoU bounding box loss function. YOLO-SE showcases remarkable performance, achieving an average precision at IoU threshold 0.5 (AP50) of 86.5% on the optical remote sensing dataset SIMD. This represents a noteworthy 2.1% improvement over YOLOv8 and YOLO-SE outperforms the state-of-the-art model by 0.91%. In further validation, experiments on the NWPU VHR-10 dataset demonstrated YOLO-SE's superiority with an accuracy of 94.9%, surpassing that of YOLOv8 by 2.6%. The proposed advancements position YOLO-SE as a compelling solution in the realm of deep learning-based remote sensing image object detection.  
object detection; remote sensing images; multi-scale; loss functions +
Footnote †: journal: Applied  
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Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).