esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 4 Results
### RQ_3  
As mentioned in Section 3.4, the values of the average accuracy for all 1-shot tasks, considering all datasets and the number of epochs, were compared to the respective values obtained for 5-shot tasks. The Shapiro-Wilk test for the 1-shot set resulted in a statistic of 0.982878 and \(p\)-value of 1.914196 \(\times\) 10\({}^{-2}\). Even though the test statistic suggests the data are nearly normal, the low \(p\)-value indicates that this difference from normality is statistically significant. As for the 5-shot set, the values are as follows: statistic = 0.956710 and \(p\)-value = 1.325552 \(\times\) 10\({}^{-5}\). The same reasoning as in the previous case applies here. Thus, we reject the null hypothesis that the sets were drawn from a normal distribution.  
Figure 6 shows the Q-Q plots for both sets to confirm this conclusion. Recall that a Q-Q plot is a graphical tool used to compare the distributions of two datasets or to check if a dataset follows a specific distribution, such as the normal distribution. The 45-degree line (the red one in the plots) represents the expected quantiles if the data follow a normal distribution. If the data points (in blue) closely align with the 45-degree line, this suggests that the data are normally distributed. It is clear by looking at Figure 6 that these are not the cases and this supports the rejection of the null hypothesis.  
The application of the non-parametric Wilcoxon test, with the alternative _greater_ and paired samples d = 5-shot \(-\) 1-shot, gave the following results: statistic = 18,528 and \(p\)-value = 1.466873 \(\times\) 10\({}^{-33}\). The mean value of the 5-shot set is 0.703370 while for the 1-shot set is 0.602152. Thus, we conclude that there is strong statistical evidence that the values in the 5-shot set are consistently and significantly greater than those in the 1-shot set. Figure 7 presents the average accuracies for all samples from the 5-shot and 1-shot sets.  
Figure 6: Q-Q plots for the 1-shot and 5-shot sets. (a) 1-shot set. (b) 5-shot set.  
Figure 7: Average accuracies: 5-shot and 1-shot sets.
# Diabetic Retinopathy Improved Detection Using Deep Learning
## 3 Fundus Oculi Proposed Classification
### Retina Images Dataset  
For this work, two datasets were used as a cross-testing approach to check the model's ability to acquire complex features for the different classes mentioned in Section 2.2 and the advantages of each dataset. The first dataset is APTOS, publicly available in the Kaggle platform ([[https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data)]([https://www.kaggle.com/c/aptos2019-blindness-detection/data](https://www.kaggle.com/c/aptos2019-blindness-detection/data)), accessed on 12 Novovember 2020), containing 3662 labeled images for training and 1928 unlabeled images for testing purposes. The second dataset is available by registering on the site that manages the Messidor dataset ([[https://www.adcis.net/en/third-party/messidor2/](https://www.adcis.net/en/third-party/messidor2/)]([https://www.adcis.net/en/third-party/messidor2/](https://www.adcis.net/en/third-party/messidor2/)), accessed on 12 February 2021), containing 1744 unlabeled images for training.  
The Messidor dataset ([PERSON], 2021) comprises 872 DR examination images of two maculcentered eye fundus images from both left and right eyes. The ground truth information for the images was from a third-party source. All the images present the same format of the fundus oculi; in the center of a wide image, the eyes are presented with a black background. The APTOS dataset was published as a competition under the same platform, with fundus oculi images taken under different conditions and sizes. For this case, the dataset authors provide the ground truth data only for the training subset, hence, we do not use the testing subset. Both datasets are high-resolution images and with an unbalanced number of images for each of the 5 classes. More details about classes distribution are in Table 1. As preprocessing, all the images in both datasets were cropped square from the center, leaving just the most important part of the image, the retina's fundus. Additionally, they were resized to \(224\times 224\) pixels, defined to fit the pretrained DenseNet model's input, including the preprocessing method as proposed in their original work ([PERSON] et al., 2017).
## 3 Results and Discussion
### Spectrophotometric Assays in Propolis Residue
#### 3.2.2 Antioxidant Activity (DPPH Assay)  
The DPPH assay results confirmed the significant antioxidant capacity of propolis residue, with a value of 2921.61 mg TE/kg DW; it is important to recognize that in vitro assays like DPPH may overestimate antioxidant capacity due to the contribution of compounds that are not necessarily bioavailable in vivo. This activity aligns with the high concentrations of phenolics and flavonoids, demonstrating propolis residue's efficacy in neutralizing free radicals and mitigating oxidative stress. Our findings coincide those of [49], who observed a direct correlation between phenolic content and antioxidant activity in winemaking by-products, although they reported a lower antioxidant activity (500-1500 mg TE/kg DW) than those observed in propolis residue.
## 4 Experimental Research
### Experimental Process
#### 4.2.3 Evaluations of Different Pre-Stress Deviation Adjustment Schemes  
To compare the effects of different pre-stress deviation adjustment schemes, the results of pre-stress deviations and shape deviations produced by the following five adjustment schemes were further analyzed and compared in this paper. The five schemes were as follows (as shown in Figure 5):  
1. Scheme 1: 12 DC1 were used to adjust the deviations of the 12 measuring points, as mentioned above.
2. Scheme 2: Only 10 DC1 (No. 2, 10, 18, 26, 42, 50, 58, 66, 82, and 90) were used to adjust the deviations of the 12 measuring points.
3. Scheme 3: Only 8 DC1 (No. 2, 10, 26, 34, 50, 58, 74, and 82) were used to adjust the deviations of the 12 measuring points.
4. Scheme 4: Only 6 DC1 (No. 2, 18, 34, 50, 66, and 82) were used to adjust the deviations of the 12 measuring points.
5. Scheme 5: Only 4 DC1 (No. 2, 26, 50, and 74) were used to adjust the deviations of the 12 measuring points.  
The adjustment results were presented in Table 4, as follows:  
1. When 12 DC1 were used for adjustment, the number of adjustment cables equaled to the number of measuring points. Then Equation (11) can be used to obtain the unique length adjustment, \(\delta\epsilon_{0}^{k}\) (as shown in Table 2), and the only accurate correction value of internal force, \(\delta\hat{t}^{k}\), can be further obtained (as shown in Table 4) with \(\eta=0\). As the number of adjustment cables decreased, that is, the number of adjustment cables was  
Figure 5: Five different schemes with different adjustment cables; (**a**) scheme 1; (**b**) scheme 2; (**c**) scheme 3; (**d**) scheme 4; (**e**) scheme 5.  
lower than that of the measuring points, the approximate solution, \(\left(\delta\epsilon_{0}^{k}\right)^{\#}\), can only be obtained by using the least square method and Equation (12).
2. The effects of pre-stress deviation adjustment decreased with the decrease of the number of adjustment cables. The adjustment effects evaluation index of internal force, \(\eta\), gradually increased from 0 to 0.4439 when the number of adjustment cables were reduced from twelve to four.
3. As the number of adjustment cables decreased, the overall structural deformation caused by the pre-stress deviation adjustment in this paper also gradually decreased. The deformation index, \(\phi\), gradually decreased from 94.049 \(\times\) 10\({}^{-4}\) to 1.384 \(\times\) 10\({}^{-4}\) when the number of adjustment cables decreased from twelve to four. It indicated that the pre-stress deviations of the twelve measuring points were adjusted to zero through twelve adjustment cables, yet it generated larger deformation in comparison with the schemes that had fewer adjustment cables.
4. In addition, this study revealed that the same number of adjustment cables with different adjustment sequences would lead to different pre-stress deviations and shape deformations; however, the adjustment results remained the same. In this case, it was necessary to further optimize the adjustment scheme that produced lower peak values of internal force and shape change during the adjustment process.
## 4 Calculation Results and Discussion  
### Evaluation Metrics  
To evaluate the forecasting method more intuitively and effectively, we choose four indicators, including accuracy (\(AC\)), mean absolute percentage error (\(MAPE\)), root mean square error (\(RMSE\)), and goodness of fit (\(R^{2}\)), as the evaluation basis [24]. The expressions are:  
\[AC=\left[1-\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}\bigg{(}\frac{E_{Ri}-E_{Fi}}{E_{Ri }}\bigg{)}^{2}}\;\right]\times 100\% \tag{13}\]  
\[MAPE=\frac{1}{n}{\sum_{i=1}^{n}}\bigg{|}\frac{E_{Ri}-E_{Fi}}{E_{Ri}}\bigg{|} \times 100\% \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}(E_{Ri}-E_{Fi})^{2}} \tag{15}\]\[\left\{\begin{array}{c}R^{2}=1-\frac{\sum\limits_{i=1}^{n}(E_{Ri}-E_{Fi})^{2}}{ \sum\limits_{i=1}^{n}(E_{Ri}-\overline{E_{R}})^{2}}\\ \overline{E_{R}}=\frac{1}{n}\sum\limits_{i=1}^{n}E_{Ri}\end{array}\right., \tag{16}\]  
where \(E_{Ri}\) and \(E_{Fi}\) are the true and forecasted daily power generation values, respectively, and \(n\) is the total number of days used for testing the model results.
## 3 The Role of Energy Storage in Micro-Grids
### Non Lithium Battery Technologies for Micro-Grids  
Besides lead acid batteries, other electrochemical technologies are being used as storage systems in stationary applications. Sodium-sulfur and sodium-metal halide batteries (also known as ZEBRA batteries [58]) have been applied in installations beside utility factories and PV fields [59]. These technologies may also be useful in smaller stationary applications, such as in micro-grids, because of the intrinsic safety of the technology, which does not require the accurate and safe management of the cells. The technologies are based on the conduction of sodium ions through a beta-alumina electrolyte, and thus, employ a low-cost material as sodium that is abundant in nature. The only disadvantage is the necessity to operate the battery at an inner temperature of at least 250 \({}^{\circ}\)C to allow the liquefaction of the sodium and the ion conduction. Therefore, heaters consuming useful energy are required to maintain the operation of the battery. An interesting feature of sodium-metal halide batteries is that when a cell fails, it results in a short circuit that maintains the continuity and the operation of a series connected string, even if with a slightly lesser overall voltage. Unfortunately, the technology is promising but has not reached full development yet, in order to deeply penetrate the market. Further improvement in cell reliability and BMSs are needed to fully exploit the possible technology advantages [60].
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 3 Control Algorithms
### Controller Implementation  
The proposed controller was implemented in MATLAB/Simulink to generate a C code. The controller code was uploaded to the PLC in order to conduct wind tunnel experiments. Then, data were compiled in a dynamic link library (DLL) for simulation in the commercial aero-elastic program known as 'DNV-Bladed'.  
Figure 4 shows the overall block diagram of the fuzzy inference-based \(H_{\infty}\)-fuzzy control algorithm. The proposed \(H_{\infty}\)-fuzzy control algorithm was implemented with the fuzzy inference algorithm, the wind speed estimator, and the \(H_{\infty}\) controllers for three different wind speed regions. In addition, we applied the selection of set points, the state scaling required for state feedback control, and the reference bias control (RBC) algorithm to respond to transient responses in terms of the impact of pitch and torque control in the rated wind speed region [23]. The reduced nominal plant used to control the target wind turbine consisted of four state vectors \(x\) (blade pitch \(\beta\), generator torque \(T_{g}\), generator speed \(\Omega_{g}\), and nacelle acceleration \(\ddot{x}\)), two control commands \(u\) (a pitch command \(\beta^{c}\) and torque command \(T_{g}^{c}\)), and an output vector \(y\) (generator speed \(\Omega_{g}\)).  
Figure 4: Block diagram of the fuzzy inference-based \(H_{\infty}\) control algorithm.  
Figure 3: Block diagram of fuzzy inference algorithm for proposed \(H_{\infty}\) controller.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### Implementation of the Physical Component of the Simulator (GQ4, FQ6-7)
#### 3.5.2 Sensors Types (GQ4)  
In addition to the use of position sensors for the tracking of tools and anatomical structures, in [40; 42] force sensors are used to measure the puncture force during percutaneous renal access [40] and the retracting force of soft biological tissues [42].
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Benefit People Who Need to Travel for Business Purposes
#### Flying Taxis Provide Transportation to International Destinations  
It is possible that flying taxis could provide transportation to international destinations, but this would depend on a variety of factors, including regulatory approval, infrastructure, and market demand. It is likely that flying taxis would initially be deployed in urban areas and for shorter distances, but as the technology and infrastructure develop, it could become feasible to use them for longer distances and to reach international destinations. However, this is still a relatively new and evolving technology, and it remains to be seen how it will be adopted and regulated in different countries and regions around the world [168].
## 2 Literature Review
### Factors Affecting Waiting Times  
A number of studies have identified factors that influence the duration of waiting times. [PERSON] et al. [20] underscored that service frequency and route reliability are key factors for assessing public transport quality in Hangzhou, China. They found that bus systems exhibited greater variability in headways due to disruptions such as traffic, whereas the metro had more regular intervals owing to its predetermined schedule. These findings highlight the importance of considering both frequency and reliability in planning to decrease waiting time variability and improve users' perception of service.  
In central areas of public transport networks, waiting times tend to be shorter due to higher service frequencies, particularly during peak hours. In contrast, peripheral areas experience longer waiting times, which lead to higher generalized travel costs and reduced accessibility [12]. This pattern underscores the importance of service frequency as a determining factor in public transport system planning.  
[PERSON] et al. [21] found in India that waiting times significantly influence transport mode choice. Their analysis revealed that paratransit services, offering higher frequencies, are more attractive to users on short journeys, whereas buses tend to be preferred for longer trips, where waiting time is less critical.  
In their study on accessibility in Santiago, Chile, [PERSON] et al. [22] noted that peripheral areas suffer from worse access to the transport network, including prolonged waiting times, which affect users' ability to access essential services such as education. Lower service frequencies in these areas increase waiting times and deepen inequalities in transport access.  
[PERSON] et al. [23] showed that prolonged waiting times in transfers posed a significant barrier for women in Kolkata, India, who faced heightened vulnerability due to unsafe transfer stations.  
[PERSON] et al. [13] identified insufficient coverage of the public transport system in Amman as another factor contributing to extended waiting times. The lack of efficient connections among different transport modes exacerbated this issue, highlighting the need for integrated models to enhance accessibility and reduce waiting times.  
[PERSON] and [PERSON] [14] indicated that waiting times are directly influenced by the quality of the information provided and coordination among operators, factors that facilitate more efficient system use and reduce uncertainty for users during transfers.  
[PERSON] and [PERSON] [16] underscored that the key factors affecting waiting times are service frequency, operator coordination, and transfer efficiency. Through their model based on door-to-door weighted times, they suggest that these elements be considered in the planning of accessibility and in optimizing public transport systems.
## 2 Materials and Methods
### Study Design  
The study adopted a mixed-methods research design to obtain a comprehensive understanding of farmers' perceptions and willingness regarding drought-related issues. This approach involved the integration of qualitative and quantitative data collection methods, allowing for triangulation of findings and a more robust analysis. Qualitative data were collected through in-depth interviews and focus-group discussions with farmers. These qualitative methods enabled researchers to explore farmers' attitudes, beliefs, and motivations in-depth. Open-ended questions and interactive group discussions provided rich insights into the complexities of farmers' experiences with drought and their perceptions of adaptive measures. The qualitative data were analyzed using thematic analysis techniques to identify key themes and patterns. Quantitative data were collected through structured surveys administered to a larger sample of farmers. The surveys consisted of closed-ended questions that captured farmers' demographic information, attitudes towards drought, perceived barriers and facilitators of adaptation, and willingness to adopt specific drought-resilience measures. The quantitative data were analyzed using statistical techniques such as descriptive statistics and inferential analysis to examine the relationships between different variables. Integration of the qualitative and quantitative data occurred at multiple stages of the research process. During data collection, insights from the qualitative interviews and discussions informed the development of survey questions and response  
Figure 1: Map showing the location of the ten villages that participated in the survey.  
options. This ensured that the quantitative survey captured relevant dimensions of farmers' perceptions and willingness. The findings from the qualitative and quantitative analyses were then compared and integrated during the data-interpretation phase. The qualitative themes provided context and nuance to the quantitative results, helping to explain and expand upon the statistical findings. This integration allowed for a more comprehensive understanding of the research topic and increased the validity and reliability of the findings. Triangulation of findings occurred through the comparison and convergence of results from the qualitative and quantitative analyses. Consistent patterns and themes across both data sets provided stronger evidence and increased confidence in the findings. In cases where there were discrepancies or divergent findings, these were carefully examined and discussed to identify potential explanations or explore additional factors that may have influenced the results. Overall, the integration of qualitative and quantitative data in this study provided a more holistic and nuanced understanding of farmers' perceptions and willingness regarding drought-related issues. The triangulation of findings enhanced the validity and reliability of the study and allowed for more robust conclusions and recommendations.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Ablation Studies
#### 4.2.5 BDDM versus Static Fusion Methods  
To demonstrate the advantages of our Bi-Modal Dynamic Decision Method over conventional fusion approaches, we compared it with fixed-weight strategies similar to those used in StrongSORT [19] and other trackers. Table 4 presents the results of this comparison on the MOT17 dataset.  
The experimental results clearly illustrate the limitations of relying solely on either appearance or motion information. The appearance-only approach, while achieving reasonable MOTA and IDF1 scores, suffers from a high number of identity switches, particularly in occlusion scenarios where appearance features become unreliable. Conversely, the motion-only approach shows the lowest performance across all metrics, especially in IDF1 (71.4%), highlighting its inadequacy in maintaining long-term identity consistency.  
Fixed-weight fusion strategies offer improvements over single-modality approaches, with the 70:30 weighting (favoring appearance over motion) performing better than the balanced 50:50 configuration. This suggests that appearance features generally provide more discriminative information than motion cues. However, our proposed BDDM significantly outperforms all fixed-weight strategies, achieving improvements of 0.9% in MOTA and 1.3% in IDF1 compared to the best fixed-weight configuration, while reducing identity switches by 8.6%.  
The superior performance of BDDM can be attributed to its dynamic weighting mechanism, which adaptively adjusts the contribution of appearance and motion information based on trajectory duration and detection context. Specifically, for newly initialized trajectories with limited motion history, BDDM assigns higher weights to appearance features (approximately 70%), while for established trajectories with consistent motion patterns, it gradually increases the weight of motion information (up to 70%). This adaptive strategy enables the system to leverage the most reliable information source in different tracking scenarios.  
Further analysis of sequence-specific results reveals that the advantages of the BDDM are most pronounced in challenging scenarios, such as crowded scenes with frequent occlusions and rapid motion changes. In such cases, the dynamic fusion strategy significantly reduces identity switches by up to 15.2% compared to fixed-weight methods, demonstrating its robustness to complex tracking conditions.