esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 2 Materials and Methods
### Datasets
#### 2.2.2 GOSIF Data  
Sun/solar-induced chlorophyll fluorescence (SIF) can be used to describe photosynthesis of vegetation. The spatial-temporal resolution of GOSIF dataset used in this study is monthly and 0.05\({}^{\circ}\), and the time range is 2001-2020 ([[https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)]([https://globalecology.unh.edu/data/GOSIF.html](https://globalecology.unh.edu/data/GOSIF.html)), accessed on 20 June 2021). The dataset is developed through data-driven use of discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data [35]. GOSIF is highly correlated with the GPP of 91 FLUXNET sites (R\({}^{2}\) = 0.73, \(p\) \(<\) 0.001), and its spatial-temporal distribution is consistent with the SIF data aggregated directly from OCO-2 SIF. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record [35]. GOSIF is useful for assessing terrestrial photosynthesis and ecosystem function and benchmarking terrestrial biosphere and earth system models [35; 36].  
Figure 1: Location of the Yellow River Basin, China, and elevation.
## 2 Materials and Methods
### Testing  
For microscopic studies, the JCM-6000 (JEOL, Tokyo, Japan) desktop scanning electron microscope (SEM) equipped with an energy dispersive X-ray (EDX) microanalyzer was utilized. X-ray studies were carried out on an X'PERTPRO diffractometer (PANalytical, Almelo, The Netherlands) in the Bragg-Brentano \"reflection\" geometry using CuK\({}_{\alpha}\) radiation (\(\lambda\) = 1.54 A) with a Ni \(\beta\)-filter. Previously, the powder was thoroughly rubbed. X-ray photoelectron spectroscopy (XPS) utilizing the Specs spectrometer (Specs, Berlin, Germany) equipped with Al and Mg anodes was utilized to determine the chemical composition of the surface layer and to measure the valence band of the samples. The spectrometer is metrologically verified. In this work, we utilized Al anode excitation. The anode material was chosen in such a way that the useful signal did not overlap with the Auger lines. The spectra were recorded in the binding energy range from 0 to 1200 eV. The binding energies were calibrated over the C-C line of the C1s spectrum (E\({}_{\text{q}}\) = 284.6 eV). The etching was conducted with argon ions with energy of 4 keV for 2 min. To determine the band gap by UV/VIS spectroscopy, a spectrometric complex based on the MDR-41 monochromator (NPO Spektr, Novocherkassk, Russia) was utilized. The material powder was poured onto a special holder and compacted thoroughly. Diffuse reflectance spectra were taken in the wavelength range of 200 to 500 nm.  
To study the photocatalytic activity of the synthesized materials, ZnO powder (25 mg) was placed in aqueous solutions of 2,4-dinitophenol (DNP) and ciprofloxacin (CIP) (volume 50 mL with a concentration of 5 mg/L), stirred in the dark for 30 min, then the suspension was irradiated with light with constant stirring. A solar radiation simulator (xenon lamp, 100 mW/cm\({}^{2}\)) was utilized as a light source. The DNP concentration was determined spectrophotometrically based on the absorption maximum at 358 nm (Shimadzu UV-1800 spectrophotometer).
## 5 Conclusions  
In summary, a THz modulator with the hybrid structure of metallic metamaterial and graphene is analyzed theoretically and demonstrated experimentally. Theory and simulation indicate that the resonance is mainly along the side of the square slit ring and lead to the resonant frequency being strongly sensitive to the side length, which could be used to change the working frequency. The narrow slit width with a higher resonant quality factor is beneficial in achieving a high modulation depth. In the theoretical result, the thickness of the dielectric layer has a large tolerance in fabrication. The measured transmission spectrum exhibits the same general tendency as the theory result and the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Graphene Based THz Amplitude Modulation** & **Amplitude Modulation Depth** \\ \hline Graphene/SiO\({}_{2}\)/pSi [16] & 15\% \\ Graphene/Al\({}_{2}\)O\({}_{3}\)/pSi [26] & 22\% \\ Metallic circle ring/Graphene/SiO\({}_{2}\)/pSi [23] & 50\% \\ Metallic square ring/PMMA/Graphene/PET (this work) & 65.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of graphene based THz modulators. PMMA: polymethyl methacrylate.  
Figure 4: (a) Microscope photography of the fabricated metamaterial. (b) Measured transmission spectrum at different modulation voltage and the corresponding modulation depth.  
amplitude modulation depth of 65.1% is obtained when the dynamic range of the modulation voltage is 5 V.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] designed and conceived this study. [PERSON] and [PERSON] performed the experiments. [PERSON] wrote this paper. All authors approved this manuscript.  
This research was funded by National Natural Science Foundation of China (61775213, 61875196, 51703227) and Scientific and Technological Research Projects of Chongqing Education Commission (KJ1601211).  
The authors declare no conflict of interest.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 4 Discussion
These results confirm that deep learning effectively stabilizes classification performance under conditions of increased structural complexity caused by salt crystallization. The ability of CNN models to capture hierarchical spatial correlations strengthens their robustness in these conditions and establishes them as a suitable complement to lacunarity-based classification. Methodologically, the study demonstrates that smaller box sizes (\(\epsilon=0.01\), 0.025, and 0.05) improve the sensitivity of lacunarity analysis to structural differences [33]. In parallel, deep learning exhibited greater stability across NaCl concentrations, driven by its ability to detect complex spatial correlations beyond those quantified by lacunarity. Despite the promising results, certain limitations must be considered. This study focused exclusively on two types of milk (whole and lactose-free), both from a single supplier and production batch, which may limit the applicability of the findings to other dairy products, such as powdered milk or processed derivatives. Only two fixed NaCl concentrations (2% and 4%) were evaluated, which restricts our ability to explore the full response landscape. While NaCl enhanced structural differentiation in adulterated samples, the highest classification accuracy for milk type differentiation was observed in the absence of added salt. This result indicates that the effect of NaCl depends on the classification task, and a universal concentration to optimize performance is unlikely. Furthermore, our research did not evaluate other additives, contaminants, or broader adulteration levels. Future research should address these aspects. Additionally, future studies could explore the drying process under controlled environmental conditions, such as varying humidity and temperature, to assess their role in deposit formation and classification accuracy. From a computational perspective, refining deep learning architectures to incorporate multimodal data, such as texture features from lacunarity analysis combined with spectral or morphological descriptors, could improve classification robustness. Expanding the dataset with larger sample variations and real-world adulteration scenarios would further validate the generalizability of the proposed methodologies. Finally, translating these findings into practical applications, such as rapid screening techniques for quality control in dairy products, represents an important step toward bridging fundamental research with industrial application.
## 7 Conclusions  
This paper has comprehensively addressed the joint dynamic task offloading and resource scheduling problem in LEO satellite edge computing networks. The proposed system model incorporates both data service transmission and computational task offloading and is framed as a long-term cost function minimization problem with constraints. Key contributions include the development of a priority-based policy adjustment algorithm for handling transmission scheduling conflicts and a DQN-based algorithm for dynamic task offloading and computation scheduling. These methods are integrated into a joint scheduling strategy that optimizes overall system performance. Simulation results demonstrate significant improvements in average system cost, queue length, energy consumption, and task completion rate, compared to baseline strategies, highlighting the strategy's effectiveness and efficiency. Future work will extend the framework to more complex network scenarios and explore the integration of advanced machine learning techniques to further enhance security performance [56; 57].  
Conceptualization, J.L. and R.C.; methodology, J.L. and K.G.; software, K.G. and R.C.; formal analysis, J.L.; writing--original draft preparation, J.L.; writing--review and editing, R.C. and C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Innovation Key R&D Program of Chongqing grant number : CSTB2023 TIAD-STX0025.  
Access to the experimental data presented in this article can be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.
## 6 Conclusions
### Best Performing Imager/Algorithm Combinations for BGA Estimation  
We found that several established algorithms for the estimation of BGA values performed well ([PERSON]'s r\({}^{2}\)\(>\) 0.6; \(p\)\(<\) 0.001) for most of the real aircraft and synthetic satellite datasets considered here. The Be162 BsubPhy, SI052 BDA, Be162B700 sub601, Be16 NDPhyI, Gi033 BDA, Da052 BDA, SM122 BDA, Ku15 PhyCI, MM092 BDA, MI092 BDA, Wy08 CI, Zh10 FLH, and MM12 NDCI BGA algorithms worked well with CASI imagery. The Be162 BsubPhy, SI052 BDA, Am092 Bsub, Be16 NDPhyI, Mi092 BDA, and MM12 NDCI BGA algorithms worked well with WorldView-2 and -3; the Be162 Bsub algorithm with Sentinel-2; and the MM092 BDA, Be16 NDPhyI, MM12 NDCI, Go04 MCI, Ku15 PhyCI, Wy08 CI, SI052 BDA, Be162 BsubPhy, and Hu103 BDA BGA algorithms with MERIS-like imagery. Be162 BsubPhy was the most widely applicable BGA algorithm (Table S4). The Be16 FLHviolet \"greenness\" algorithm yielded the best (albeit poor) BGA estimates with simulated Landsat-8 imagery.  
Algorithm/imager combinations listed in the preceding paragraph that include the phycocyanin reflectance minimum at 620 nm had slightly better performance with regard to BGA estimation than Chl-a algorithms for CASI and WorldView-2 and were only slightly less effective than Chl-a algorithms for Sentinel-3/MERIS/OLCI as predicted by [PERSON] et al. [7] and [PERSON] et al. [11] (Table S4). For imagers without a narrow band at 620 nm, we used Chl-a proxy algorithms to estimate BGA with good results for simulated Sentinel-2 and poor results for simulated Landsat-8 and MODIS for this water body at the time of hyperspectral image acquisition.
## 4 Discussion
* Application of the EEG/VO2 ratio approach in exercise physiology and medicine. The application of the EEG/VO2 ratio approach in exercise physiology and medicine offers a novel method for understanding the complex relationship between neural activity and metabolic demand during physical exertion. This innovative approach integrates EEG to monitor brain activity and oxygen consumption measurements to assess metabolic function, providing a comprehensive picture of how the brain and body respond to exercise. By analyzing the EEG/VO2 ratio, researchers and clinicians can better understand the cognitive and neural mechanisms underlying physical performance, fatigue, and recovery. This method has significant implications for optimizing athletic training, as it makes it possible to identify the mental states that correlate with peak performance and periods of reduced efficiency. For example, understanding how brain activity changes at different VO2 max levels can help to design more effective training regimes that improve both physical and cognitive endurance. Furthermore, in the medical field, the EEG/VO2 ratio can be used to adapt rehabilitation programs for patients suffering from cardiovascular or neurological disorders, ensuring that both the cognitive and physical aspects of recovery are taken into account. This dual approach can improve patient outcomes by enhancing not only physical capacity, but also mental resilience and cognitive function. This approach also makes it possible to study the effects of environmental stress factors, such as high altitude, extreme temperatures, or hypoxic conditions, on brain function and overall performance. By monitoring how the brain adapts to these challenging conditions, it is possible to develop strategies to mitigate their adverse effects, thereby improving safety and performance in extreme environments. In this way, the EEG/VO\({}_{2}\) ratio represents a powerful tool for advancing our understanding of the dynamic interaction between the brain and body in both health and disease, paving the way for more integrated and effective approaches in exercise science and clinical practice.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## 4 Experiment and Results
### Verification of Virtual Force Sensor  
To verify the virtual force sensor, this paper analyzed the external force exerted by the robotic arm through the experiment of gr  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MAE (Nm)** & **RMSE (Nm)** & \(\gamma\) \\ \hline Axis 1 & 14.7371 & 19.9986 & 0.9915 \\ Axis 2 & 9.4744 & 14.1449 & 0.9895 \\ Axis 3 & 6.2997 & 9.6843 & 0.9770 \\ Axis 4 & 5.0730 & 6.2901 & 0.9894 \\ Axis 5 & 1.8219 & 2.0906 & 0.9940 \\ Axis 6 & 0.6542 & 0.8158 & 0.9964 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of the dynamic model.  
Figure 10: Comparison of the external force between senor and virtual sensor in the Z direction.  
Figure 9: (**a**) Grinding experiment; (**b**) motion trajectory of the grinding experiment.  
Based on the above arguments, the system could not accurately estimate the external contact force of 20N or lower because the virtual force sensor based on the dynamic model was limited by the accuracy of the model, the noise of the driver and the influence of the inaccurate estimation of the static state. Therefore, this system is only suitable for detecting external contact force and lower precision force control applications.
## 5 Results and Discussion
### VSWT Hybrid WDPS Performance Under Normal Conditions
#### 5.1.2 Random Response  
Wind is intermittent by nature. Therefore, wind speed is not steady but varies with time. To reflect this event, random wind speed is considered a real-world scenario. In [101],  
Figure 19: VSWT response against gradually increasing wind speed.  
Figure 20: Blade pitch angle response against ramp or gradually increasing wind speed (Figure 19).  
the wind power-generated signal was considered a random response for simulation. However, to justify the performance of the hybrid WDPS, the random wind power response was considered a wind fluctuation in this study. Therefore, the low wind power fluctuation was converted into wind speed because wind power is directly proportional to the cube root of \(\rm{V_{w}}\). In addition, during very low winds, the beta and power coefficient may not affect this proportionality relation. Then, this trend was scaled up to cover the range of low, medium, and high wind speeds, as shown in Figure 21. Different parameters against wind fluctuations are shown in Figure 22, which indicate that the modified VSWT hybrid WDPS performed efficiently in wind variations. The DPP frequency seems to be stable in such events. However, the frequency deviations shown at 800 s and 2000 s result in a sudden loss of wind power generation. Wind generation drops because the wind speed drops below the rated wind speed. This results in a sudden increase in load demand to the DPP shown in Figure 22. Similarly, the effectiveness of inertial and proportional control and blade pitch angle control is presented in Figure 23. Figure 24 shows a detailed graphical representation of that load demand event. The results clearly depict the contribution of the proposed VSWT inertial and proportional control of almost 0.15 p.u during the reduction in wind generation. It is also concluded that the contribution of proportional control, \(\rm{K_{pn}}\), is much higher compared to \(\rm{K_{dn}}\). In other words, the VSWT efficiently contributes to the
###### Abstract  
This paper investigates the effectiveness of various data augmentation techniques for enhancing Arabic speech emotion recognition (SER) using convolutional neural networks (CNNs). Utilizing the Saudi Dialect and BAVED datasets, we address the challenges of limited and imbalanced data commonly found in Arabic SER. To improve model performance, we apply augmentation techniques such as noise addition, time shifting, increasing volume, and reducing volume. Additionally, we examine the optimal number of augmentations required to achieve the best results. Our experiments reveal that these augmentations significantly enhance the CNN's ability to recognize emotions, with certain techniques proving more effective than others. Furthermore, the number of augmentations plays a critical role in balancing model accuracy. The Saudi Dialect dataset achieved its best results with two augmentations (increasing volume and decreasing volume), reaching an accuracy of 96.81%. Similarly, the BAVED dataset demonstrated optimal performance with a combination of three augmentations (noise addition, increasing volume, and reducing volume), achieving an accuracy of 92.60%. These findings indicate that carefully selected augmentation strategies can greatly improve the performance of CNN-based SER systems, particularly in the context of Arabic speech. This research underscores the importance of tailored augmentation techniques to enhance SER performance and sets a foundation for future advancements in this field.  
Article  
Audio speech emotion recognition; data augmentations; convolutional neural networks
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 2 Materials and Methods
### Healthcare Data Quality Conceptual Model Development
#### 2.1.1 Completeness  
Completeness is a criterion for evaluating whether data are missing in the process of expressing real-world data as a system. For example, if a patient visits a hospital and undergoes an examination, the patient, examination, and visit information should not be omitted from the data's point of view.
## 2 Materials and Methods
### Pig Behavior Monitoring
#### 2.3.3 Video Monitoring  
Following the heat stress and cooling experiment, wireless indoor video security cameras (Model ZM-SH75D001-WA, Zmodo Technology, Shenzhen, China) were installed in each farrowing room for another study. These cameras, integrated with Zviewer software (version 2.0.1.6) on the OSCS computer, facilitated real-time video surveillance and recording. The video data, timestamped in alignment with other online sensor data, provided an additional layer of observation for assessing sow and piglet behaviors in conjunction with sensor detection signals. This integrated and multifaceted approach enabled a comprehensive analysis of the monitored behaviors, enhancing the depth of the study's findings.
## 5 Lifetime and Weight Co-Optimization
### Approximation Model  
The Kriging approximation model could take the place of the finite element model to describe the relationship between design variables and optimization objectives, which was explained in my previous publications [22; 23]. In this paper, the other 10 sample points obtained from Latin hypercube sampling method were used to testify the accuracy of the approximation model, shown in Figure 25. If the coefficient of determination \(R\) is closer to 1.0, it means the fitting model has the best accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Run** & \(x0\) & \(x1\) & \(x2\) & \(x3\) & \(x4\) & \(x5\) & \(x6\) & \(x7\) & \(x8\) & \(x9\) & \(x10\) \\
**Counter** & **(mm)** & **(mm)** & **(mm)** & **(MPa)** & **x4** & \(x5\) & \(x6\) & \((\times 10^{8}\)N)** & \((\times 10^{8}\)N) & \((\times 10^{8}\)N) & \((\times 10^{8}\)N) \\ \hline
1 & 0 & 18 & 27 & 1311.8 & \(-\)0.0995 & 0.119 & \(-\)0.2788 & \(-\)1.89 & 0.86 & 1.01 & \(-\)3.83 \\
2 & 10 & 22 & 31 & 1098.1 & \(-\)0.1287 & 0.169 & \(-\)0.3002 & \(-\)1.96 & 1.02 & 0.88 & \(-\)3.66 \\
3 & 20 & 16 & 29 & 1430.5 & \(-\)0.1313 & 0.197 & \(-\)0.2228 & \(-\)1.65 & 1.07 & 0.81 & \(-\)5.01 \\
4 & 30 & 24 & 35 & 1003.1 & \(-\)0.1135 & 0.168 & \(-\)0.2575 & \(-\)1.85 & 1.18 & 1.14 & \(-\)4.07 \\
5 & 40 & 19 & 36 & 1193.1 & \(-\)0.1376 & 0.125 & \(-\)0.3242 & \(-\)1.87 & 0.99 & 0.95 & \(-\)4.43 \\
6 & 50 & 15 & 26 & 1169.3 & \(-\)0.1439 & 0.132 & \(-\)0.2067 & \(-\)2.04 & 1.11 & 1.31 & \(-\)4.03 \\
7 & 60 & 21 & 37 & 1406.8 & \(-\)0.1414 & 0.187 & \(-\)0.2362 & \(-\)1.81 & 0.92 & 1.01 & \(-\)4.28 \\
8 & 70 & 20 & 34 & 920.0 & \(-\)0.0982 & 0.189 & \(-\)0.2815 & \(-\)2.25 & 1.22 & 1.03 & \(-\)4.97 \\
9 & 80 & 17 & 35 & 1371.2 & \(-\)0.1351 & 0.155 & \(-\)0.2468 & \(-\)2.49 & 0.83 & 1.06 & \(-\)3.29 \\ \(\cdots\) & & & & & & & & & & & \\
50 & 100 & 23 & 38 & 1121.85 & \(-\)0.1300 & 0.164 & \(-\)0.2175 & \(-\)2.22 & 0.84 & 0.84 & \(-\)4.93 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample points from the Latin hypercube method.  
Figure 24: Chart of optimization process.  
The Kriging approximation model for both weight and lifetime met the accuracy requirement for this engineering optimization problem.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 5 Conclusions and Future Work  
### Conclusions  
The rapid advancement of generative artificial intelligence has introduced new challenges in discerning synthetic visual content from authentic media. In this study, a hybrid deep learning model--SE-ResNet-50--was proposed to address these challenges by combining the hierarchical feature extraction capabilities of ResNet-50 with the dynamic feature recalibration of Squeeze-and-Excitation (SE) attention mechanisms. Extensive experiments conducted on the CIFAKE dataset demonstrated that the proposed hybrid model outperforms standard CNN-based architectures such as VGGNet and DenseNet and even improves upon the baseline ResNet-50 model. Comparative analysis with transformer-based architectures shows our approach achieves competitive performance while maintaining computational efficiency. The SE-ResNet-50 model achieved a classification accuracy of 96.12%, a precision of 97.04%, a recall of 88.94%, an F1-score of 92.82%, and a high ROC-AUC score of 0.9862--confirming its robust ability to distinguish real from AI-generated images. Additionally, the inclusion of the SE attention mechanism enhanced both the discriminative power and interpretability of the model. This feature enables transparency in decision-making, which is crucial for sensitive applications such as digital forensics, identity verification, content authentication, and regulatory compliance. While current limitations include evaluation on a single dataset and the absence of adversarial testing, future work will address these constraints through cross-dataset validation and robust augmentation strategies. By presenting a high-performing, interpretable, and computationally efficient solution, this research contributes to the growing field of AI-generated content detection. The proposed SE-ResNet-50 model shows strong promise for practical deployment in real-world scenarios, including edge devices, mobile applications, and secure cloud-based platforms.