esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, the influence of the dye concentration in a guest-host polymer system on the effective refractive index and field confinement of SOI slot-waveguides at various geometric parameters are investigated. It is shown that the dye concentration has a much weaker influence on both, the effective refractive index and the field confinement factor compared to the influence of the geometrical waveguide structure. As a consequence, the refractive index change due to different dye concentrations is negligible compared to the influence of geometrical parameters such as tilt angle, slot width and slab height. Our results can also be used as guideline for designing optimized silicon-organic hybrid slot-waveguides for electro-optical switches, modulators and tunable filter.  
Figure 11: Influence of the slab height \(h\) on the modal properties. Higher modes propagating into the slab can be found for \(h>60\,\text{nm}\).  
Figure 10: Difference in the effective refractive index \(\Delta n_{\text{eff}}\) (**a**) and the field confinement factor \(\Delta\Gamma_{\text{slot}}\) (**b**) of both concentrations as a function of \(s\), with \(h\) as parameter.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the simulation study; [PERSON] performed the material characterization; all authors analyzed the data.  
**Funding:** This work is funded by European Regional Development Fund (10.13039/501100008530).  
**Acknowledgments:** We acknowledge support by the German Research Foundation and the Open Access Publication Funds of the TH Wildau.  
**Conflicts of Interest:** The authors declare no conflict of interest.
Figure A5: Effective refractive index \(n_{\text{eff}}\) for both concentrations as a function of \(s\) and with \(h\) as parameter.
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 3 Methodology
### Input Variable Calculation
#### 3.1.2 Calculation of Agrometeorological Index  
As a crop with high water requirements, maize is sensitive to water deficiency, and the effects of drought during different growth stages on maize production differ [56]. Eight-five percent of the corn planting areas in the study area are rain fed [43; 57; 58; 59], and drought occurs frequently during the maize growth period [60; 61; 62]. The standardized precipitation index (SPI) [63] is a commonly used drought monitoring index and has the advantage of multitemporal characteristics. The growth period of maize in the study area lasts from April to September. To reflect the effects of drought on maize growth at different times, SPI indexes with a two-month scale in May (SPI2-5), July (SPI2-7) and September (SPI2-9) were selected to represent drought conditions during the early, middle, and late growth stages of maize, respectively. For each study year, we first calculated the SPI2-5, SPI2-7, and SPI2-9 values iteratively for each grid; then, for each SPI index, we calculated its average value within the cultivated land area of each municipal administrative region and used it as a candidate independent variable to participate in the construction of the subsequent production estimation model.  
Temperature is also an important climatic factor affecting maize production. The suitable growth temperature of maize ranges from 10 to 30 \({}^{\circ}\)C [64]. During the growth period, with increasing temperature, the yield of maize will increase; however, when the temperature exceeds a certain threshold, temperature will have an obvious negative effect on maize production [65; 66; 67; 68]. Therefore, we calculated the extreme degree-day (EDD) and accumulated temperature above 10 \({}^{\circ}\)C (AAT) to reflect the impact of temperature on maize production. EDD was calculated as follows:  
\[EDD=\sum_{h=1}^{N}DD_{h}/24 \tag{2}\]  
\[DD_{h}=\left\{\begin{array}{ccc}0&if&T_{h}<T_{opt}\\ T_{h}-T_{opt}&if&T_{h}\geq T_{opt}\end{array}\right. \tag{3}\]  
In Equations (2) and (3), \(N\) is the number of hours within a certain period of time, \(T_{h}\) is the actual temperature at h-th hour, and \(T_{opt}\) is the high temperature threshold. We chose 30 \({}^{\circ}\)C as the threshold.  
For each study year, we first calculated the EDD and AAT for each grid throughout the whole growing season (from April to September) and each for month in the whole growing season. The EDD from April to September was recorded as EDD4, EDD5, EDD6, EDD7, EDD8, and EDD9, and the EDD of the whole growing season was recorded as EDD4-9.  
Similarly, the AAT from April to September was recorded as AAT4, AAT5, AAT6, AAT7, AAT8, and AAT9, and the AAT of the whole growing season was recorded as AAT4-9. Then, for each EDD (AAT) index, we calculated its average value in the cultivated land area of each municipal administrative region and used it as a candidate independent variable to participate in the construction of the subsequent yield estimation model.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 6 Issues and Challenges
### Quality of Service  
In the expected VEC environment, vehicles would continuously upload their local sensor data to the nearest edge. To this end, power generation and consumption at the edge should be taken into careful consideration to avoid quality of service (QoS) loss and service disruptions. In [44], the viability of solar-powered road side units (SRSU) is shown. Each SRSU is powered by solar panels with battery. It consists of small base stations in addition to a MEC server. An SRSU should allocate sufficient computational resources to the vehicles within its coverage. When the power demand of SRSU is  
Figure 6: Simulation Results (**a**) Data Delivery Ratio; (**b**) Dissemination Overhead; (**c**) Dissemination Delay.  
higher than the solar generation or the energy of the battery, communication and computation resource allocation are rearranged to reduce power consumption.  
In smart cities, different scenarios would require significant QoS improvement to handle temporal excessive traffic loads during public events, unexpected weather conditions, or extreme traffic congestions. In these scenarios, the employment of additional temporal RSUs that are carried by unmanned aerial vehicles (UAVs) will provide the additional required infrastructure for supporting traffic applications. UAVs can be dynamically deployed to act as mobile edges in accordance with traffic events and congestion conditions. Another idea for enhancing QoS under different traffic scenarios is proposed in [45], where the authors suggested to utilize the vehicles themselves as the infrastructures for communication and computation. A further research effort is required for enhancing and managing QoS in vehicular networks, with the consideration of a heterogeneous edge-based environment.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### System Model
#### 3.1.3 Satellite Constellation Model
The constellation parameters are summarized in Table 1.  
Figure 6: Illustration of the Walker-Star (**left**) constellation for \(\{h=550\;\text{km},i=87.5^{\circ},N=240,P=16,F=8\}\)and Walker-Delta (**right**) constellation for \(\{h=550\;\text{km},i=70^{\circ},N=240,P=16,F=8\}\).
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## 2 Motivation
### The Relation between the MWI and Inverse Source Problems  
Consider Figure 1a in which the dashed green region represents the OI surrounded by multiple antennas. As can be seen, the antenna denoted by \(Tx1\) irradiates the OI. The other antennas surrounding the OI act as receivers, and collect the resulting _total_ field data \(E\). The incident field \(E^{\text{inc}}\), i.e., the field in the absence of the OI, is also collected in a separate experiment by the same antennas. Having both the total and incident fields, the scattered field can then be obtained as \(E^{\text{scat}}\triangleq E-E^{\text{inc}}\). Equivalently, this scattered field can be thought as the field radiated by a new set of currents, often referred to as _contrast sources_[38] which radiate in the background medium. These contrast sources are confined within the OI's geometrical domain; i.e., they are zero outside the OI's geometrical support. This is shown in Figure 1b where the actual illuminating antenna \(Tx1\) is replaced by contrast sources within the OI's geometrical domain. As shown in this figure, these contrast sources depend on the multiplication of the OI's dielectric contrast profile and the induced total field within the OI due to \(Tx1\). These contrast sources are then written as \(\mathbf{w}(\mathbf{r})=\chi(\mathbf{r})\mathbf{E}(\mathbf{r})\)[38] where \(\chi\) is the dielectric contrast profile of the OI, \(\mathbf{E}\) is the total field induced in the OI due to a given transmitter (in this case, \(Tx1\)), and \(\mathbf{r}\) is the location vector. Note that the dielectric contrast profile is defined as  
\[\chi(\mathbf{r})\triangleq\frac{\epsilon(\mathbf{r})-\epsilon_{b}}{\epsilon_{b}}, \tag{1}\]  
where \(\epsilon(\mathbf{r})\) is the relative permittivity of the OI, and \(\epsilon_{b}\) is the relative homogeneous permittivity of the background medium. (In this paper, the background medium is air.) As can be seen from the above discussion, the MWI problem in Figure 1a has now been cast as an electromagnetic inverse source problem shown in Figure 1b, with its unknown quantity being these contrast sources. (This well-known relation can also be found in other references, e.g., see [39].) To minimize the null space of the associated inverse problem, the OI may be illuminated from multiple angles. This has been demonstrated in Figure 1c in which a different transmitter (\(Tx2\)) now illuminates the OI. Similarly, this is equivalent to the inverse source problem depicted in Figure 1d; however, the new unknown contrast sources in this case are different than the ones in Figure 1b as the total field induced in the OI is now due to \(Tx2\), instead of \(Tx1\). Based on this discussion, the MWI problem can be thought as the summation of multiple inverse source problems whose unknown contrast sources share one common component: the dielectric profile of the OI. For a more detailed discussion on this topic, see, for example [40]. (As also noted by other authors, this analogy has been used to develop the contrast source inversion algorithm [38].)
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### Principles of the Enhanced Feature Matching Algorithm
#### 3.2.3 Improved RANSAC Algorithm with Automatic Threshold Adjustment  
Numerous feature-matching mismatches can arise from the emergence of similar feature points during the matching process due to the complexity of the image information and the limitations of computational methods. The RANSAC (Random Sample Consensus) method is used to remove these discrepancies. Therefore, this paper employs a self-threshold adjustment method to optimize the algorithm. The specific flowchart illustrating the improved RANSAC matching result optimization algorithm is depicted in Figure 5.  
The basic idea behind automatic threshold adjustment is to use statistical properties of the data to dynamically determine thresholds instead of manual settings. This method greatly improves the algorithm's ability to adapt to different datasets, thereby lowering the problems of missed and false matches. The drawbacks of fixed thresholds can be successfully addressed in the RANSAC algorithm by introducing an automatic threshold  
Figure 4: Example image of feature point segmentation: (**a**) establishment of KD tree; (**b**) plane segmentation corresponding to KD tree.  
adjustment mechanism before determining the number of inliers. In particular, each time the initial model estimation is completed, the residuals of every data point must be calculated using the current model, and the threshold must be dynamically adjusted in accordance with the results.
## 4 Trajectory Prediction Performance Evaluation
Assuming the distribution of the maximum absolute prediction errors at the 31 intersections is normal, 95% confidence intervals of the population mean of the maximum absolute prediction error under each predicted curve were derived as shown in Table 1.  
Figure 8: The comparison of the accuracy of trajectory prediction methods. More than 70% of the predictions by the Triclothoidal curve had less than 0.3 m of absolute error. Comparing to the other prediction methods, the Triclothoidal curve had a strong advantage for trajectory prediction at the intersection for right-turns. We also evaluated the significance of the population mean of the maximum absolute prediction errors between the prediction methods with \(t\)-tests. We assumed a null hypothesis, that the population means of the maximum absolute errors were the same and tested the significance at the 5% level. The test indicated that the t-value between the Biarc curve and the Trichtoidal curve (B-T) was 6.724, and the t-value between the clothoidal curve and the Trichtoidal curve (C-T) was 4.887. The t-value when the alpha value and degrees of freedom were 0.05 and 30, respectively, was 2.042, so we could reject the null hypotheses for both B-T and C-T. Thus, in the population of the urban intersections, the trajectory prediction method with the Triclothoidal curve could predict a significantly accurate right-turn trajectory compared with the conventional methods. From the evaluations, the trajectory prediction with the Triclothoidal curve had a strong advantage over the trajectory prediction at the intersection right-turn.
## 4 Dataset Description
### Scenarios and Environments  
For our research purposes, we focused on pedestrian scenarios that are highly relevant in a variety of location-based applications such as studying mobility and migration patterns of workers or accessing various fundamental resources such as potable water (see LEDSOL project [6; 19]), helping city planners to offer optimized services based on user location, or promoting more sustainable ways of transportation through optimized access to bikes, green lanes, or parks [39].  
The university campus area and Hervanta center (a district in Tampere, Finland) offer an interesting and diverse environment for challenging positioning (i.e., open-sky, light forest, lake, suburban). While there are not many high buildings that could represent very harsh urban canyoning conditions, it is possible to recreate a partial blockage within the campus due to campus buildings (having between 3 and 10 floors). We have divided the surveys into three scenarios, all pedestrians with the conditions summarized in Table 2.  
Defining the way the device is held or carried is also part of the scenario definition and crucial for future post-processing. Several studies [40; 41] have shown how these carry modes determine the positioning models to use [40]. In [41], several carrying modes were assessed (e.g., texting, swinging, jacket pocket) and their effect on the inertial measurements, and consequently on the positioning solution, were reviewed.  
Figure 3: Description of the acquisition setups. (**a**) Static acquisition setup. (**b**) Backpack setup with reference receiver.  
In our study, two carry modes were reviewed in the dataset: (1) texting, where the device was held face up in front of the user; (2) backpack pocket, where the device was put in a stood-up orientation. These modes were chosen for their practical aspects, but also to understand how positioning might be affected by partial body blockage, as highlighted in [42]. Note that the smartwatch was placed on the left wrist (except S1). A representation of the scenario's trajectories is displayed in Figure 4.
# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 5 Conclusions  
This research has demonstrated the exceptional potential of embedding methods and ensemble classifiers in overcoming the challenges posed by imbalanced datasets. Embedding methods such as InceptionV3, SqueezeNet, and DeepLoc, in conjunction with stacked ensemble methods, led to considerable improvements in the CA, AUC, and F1-scores. The study also revealed that classes with distinct and separable features, such as grassy shoot and sett rot, achieved near-perfect classification performance. This validates the robustness and precision of the proposed methodologies in identifying diseases with clear distinguishing characteristics. However, the analysis also highlighted limitations when dealing with more challenging classes, such as smut and pokkah boeng, which often exhibited overlapping or ambiguous features. These findings point to the need for further enhancements in data preprocessing and domain-specific feature engineering. Stacking stands out as the optimal choice among the tested methods, achieving superior results while remaining computationally efficient. While DL models are powerful, their training complexity and high computational costs limit their practicality in certain scenarios. In summary, this study provides a comprehensive framework for improved classification performance in imbalanced datasets through embedding-based feature extraction and ensemble-based classification techniques. The proposed approach offers a robust solution for real-world agricultural and industrial applications, where datasets are often characterized by significant imbalance and complexity. The results underline the importance of integrating state-of-the-art AI methodologies to develop scalable, accurate, and efficient disease detection systems. Future research should explore the incorporation of domain knowledge into the feature extraction process, enabling models to learn more context-specific representations of the data.  
Conceptualization, S.Q. and A.A. ([PERSON]); Methodology, S.Q. and H.A.; Software, S.Q. and A.A. ([PERSON]); Validation, S.Q., A.A. ([PERSON]) and H.A.; Formal analysis, A.A. ([PERSON]), A.A. ([PERSON]) and H.A.; Investigation, H.A.; Data curation, A.A. ([PERSON]) and H.A.; Writing--original draft, S.Q. and A.A. ([PERSON]); Writing--review & editing, A.A. ([PERSON]) and H.A.; Visualization, A.A. ([PERSON]); Project administration, A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in: [[https://data.mendeley.com/datasets/9](https://data.mendeley.com/datasets/9) twjtv92 vk/1]([https://data.mendeley.com/datasets/9](https://data.mendeley.com/datasets/9) twjtv92 vk/1) (accessed on 1 July 2023).  
The authors declare no conflicts of interest.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 4 Conclusions  
Experimental results show that the improved ResNet-50 model effectively handles complex disaster scenarios by accurately classifying diverse disaster images, achieving 87% accuracy across tested categories, including distinguishing similar disasters such as landslides and debris flows. The residual structure also enabled rapid convergence and improved training stability by mitigating gradient vanishing issues common in deep networks. However, despite having fewer parameters than traditional deep models, the computational demands remain high, requiring considerable computational resources and limiting real-time applications, especially on resource-constrained platforms. Moreover, the relatively limited training dataset size suggests that the model's real-world generalization performance requires further validation. Future work should include expanding dataset diversity, exploring deeper or more advanced architectures, and adopting ensemble  
Figure 10: Workflow of the disaster image classification system based on the improved ResNet-50 model.  
learning to improve model adaptability, robustness, and accuracy. Overall, this study demonstrates the effectiveness of residual networks for multi-type disaster classification, offering valuable insights and laying a solid foundation for further advancements in disaster detection technologies.  
Conceptualization, L.W. and B.L.; methodology, L.W.; validation, L.W.; formal analysis, L.W.; investigation, L.W. and Z.X.; resources, L.W., X.X. and B.L.; data curation, L.W. and Z.X.; writing--original draft preparation, L.W.; writing--review and editing, L.W. and B.L.; visualization, L.W.; supervision, B.L.; project administration, L.W. and B.L.; funding acquisition, L.W., X.X. and B.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Student Innovation and Entrepreneurship Training Program of Xiangtan University S202410530097X.  
Not applicable.  
The image data used in this study are publicly available from disaster-related websites and open-source platforms such as GitHub. No new data were created during the study.  
The authors declare no conflicts of interest.
## 3 Feature extraction and dimension reduction
### Feature subset selection
#### 3.3.2 Differential evolution (DE)  
DE is a novel parallel optimal searching method proposed in the mid-1990s [43]. The main concept of DE is a scheme to generate population vectors. Basically, DE employs three population vectors to generate a new population vector. The weighted difference between two selected population vectors is added to the third vector, which is then crossed-over with the target vector **g** to generate a new vector **v**. If **v** has a better quality than **g**, it will replace **g** for the next generation, otherwise, **v** will be discarded and **g** will survive [51]. This \"family\" generation process is different from that of GA, where two randomly selected members are used to generate a member for the next generation.  
Using a probabilistic neural network (PNN) model as the cost function, DE was applied for the wavelength selection to develop a surface acoustic wave sensor for food quality monitoring [51]. In this study, a comparison between the results of DE and GA shows that although both methods  
Figure 3: Amplitude distributions between pairs of 10 detected biomarkers that distinguish T4 bacteriophage (blank box) from Bovine serum albumin protein (shaded box).  
significantly reduced data dimensions by 50%, DE achieved a better average performance than GA, reaching the minimal misclassification rate of 0.0175.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 5 Results and Discussion
### WSNs Coverage Optimization Analysis  
To assess the performance of the proposed algorithm in terms of target area coverage, simulation experiments were conducted on the algorithms presented in this paper. It was assumed that WSNs was deployed within a 50 m \(\times\) 50 m target area, which included a diamond-shaped obstacle and a triangular-shaped obstacle. Sensor nodes were randomly scattered within this target area, and the simulations were verified using Matlab 2021a software. Based on references [12; 33], it is known that the simulation parameters provided in Table 4 yield the best algorithm performance. The simulation parameters are as shown in Table 4.