esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Species that could potentially interfere with the sensing phase were evaluated by injecting controlled amounts of methanol, ethanol, and isopropyl alcohol gaseous solutions into the three-necked flask, analogous to the acetone sensing procedure. To do that, 100 \(\mu\)L acetone was injected in the 500 mL exponential dilution system to prepare an air sample containing a concentration equal to 157 ppm. Additionally, 500 \(\mu\)L of the assayed species and possible interferents were injected consecutively to ensure concentrations in the air phase of 781 ppm isopropyl alcohol, 757 ppm methanol, and 789 ppm ethanol. Such air samples were analyzed using the sensing system and the phosphorescence signal from the sensing material was monitored in each case. The results showed that the ethanol and isopropyl alcohol caused little or no interference (no significant changes in the phosphorescence emission from the sensing material were observed) at the assayed concentration levels. However, the presence of methanol in the air sample affected the determination of acetone. In fact, an analysis of an air sample containing a 757 ppm concentration of methanol resulted in a decrease of the luminescent analytical signal equivalent to 17% of the one produced by the presence of a 157 ppm acetone concentration. For similar concentration levels of acetone and methanol in the gaseous air sample, the effect of methanol was less than 5% of that produced by acetone. The relative effect of the tested interferents on the luminescence signal of the sensing material is presented in Figure 10.  
Figure 10: Effect of the assayed interferents on the luminescence of the sensing phase. The assayed concentrations were 157 ppm of acetone, 781 ppm of isopropyl alcohol, 757 ppm of methanol, and 789 ppm of ethanol.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Analytical Comparison of the Models
#### 5.2.4 User Experience (UX/UI)  
Providing a good user experience is essential for the effective use and long-term success of a web application. Table 6 contrasts the UX/UI implications in both architectures.  
HSALA promotes a superior user experience by dedicating layers to UX/UI and accessibility, ensuring that the interface is both user-friendly and secure. The three-layered model's approach may result in a compromised UX/UI due to conflicting priorities and the insufficient integration of accessibility and security considerations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **HSALA** & **Three-Layered Model** \\ \hline \multirow{4}{*}{UX/UI Focus} & The Content Presentation Layer focuses on delivering an intuitive and responsive user interface with an emphasis on UX/UI design. & The Presentation Layer handles both presentation and interaction, potentially overloading its scope. \\ \cline{2-3}  & Improves user experience for all users, including those with disabilities, through the Accessibility Enhancement Layer. & Retrofitting accessibility can compromise design consistency and user experience. \\ \hline \multirow{4}{*}{Security Enhancements} & The Content Presentation Security Layer ensures that security measures do not disrupt the user experience. & Security prompts or measures may interfere with usability if not carefully integrated due to the lack of dedicated layers. \\ \cline{2-3}  & Separation of concerns leads to a cohesive and maintainable UX/UI design, facilitating updates and consistency. & Less flexibility in adopting new UK/UI trends due to tightly coupled layers and potential conflicting priorities. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the two architectural models considering UX/UI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **HSALA** & **Three-Layered Model** \\ \hline \multirow{4}{*}{Modularity} & The multi-layered architecture allows for the independent scaling of each layer based on demand. & Tightly coupled layers can hinder scalability and flexibility. \\ \cline{2-3}  & Easier to update or replace components without affecting the entire system due to the separation of concerns. & Changes in one layer may require significant adjustments in others, complicating maintenance. \\ \hline \multirow{4}{*}{Performance Optimization} & Security and accessibility features are optimized within their layers to minimize performance impact. & Difficulty in isolating and optimizing specific components can lead to inefficiencies and performance bottlenecks. \\ \cline{2-3}  & Adaptable to new technologies and standards due to its flexible structure and modularity. & Incorporating new technologies or standards may be challenging due to the rigid structure. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the two architectural models considering scalability.
## 3 Proposed Method
### Noise-Reduction Processing
#### 3.1.2 Suppression of Noise and Contrast Enhancement
Figure 11: Comparison of the line profiles for contrast enhancement (The green line indicates the position of the line profile): (**a**) blur image; (**b**) blur image’s line profile; (**c**) MSR image; and (**d**) MSR image’s line profile. In nighttime images, where illumination is lower and noise is more pronounced, this paper aims to maintain the maximum edge gap while suppressing noise. Figure 13a,c,d illustrate the experimental results obtained by setting different weights for each single-scale sigma value (10, 90, and 200). Figure 13a represents the result obtained using the MSR algorithm with uniform sigma values for the parameters of the bilateral filter. Figure 13c shows the result when a higher weight is assigned to the lower sigma value of 10, while Figure 13e displays the result when a higher weight is assigned to the larger sigma value of 200. By analyzing the line profiles shown in Figure 13d, it can be observed that the profile with a higher weight on the lower sigma value exhibits the best noise suppression while maintaining the edge gap. This indicates that by assigning higher weights to lower sigma values, the MSR algorithm effectively suppresses noise in nighttime images, contributing to improved lane detection. Through these experimental results, the optimal weights for different sigma values in the MSR algorithm were determined, enabling effective noise suppression while preserving the maximum edge gap in nighttime images. Figure 12: A comparison for determining the appropriate parameters of an MSR during daytime (The red lines indicate the position of the lane profile, and the red dots represent the boundary of the lane edge): (**a**) low bilateral filter parameters and MSR with uniform weight on sigma; (**b**) image (**a**)’s lane-line profile; (**c**) low bilateral filter parameters and MSR with higher weight on low sigma; (**d**) image (**c**)’s lane-line profile; (**e**) low bilateral filter parameters and MSR with higher weight on high sigma; and (**f**) image (**e**)’s lane-line profile.
## 3 Proposed Method
### Cascaded Adaptive Feature Extraction Unit  
A single convolutional operation hampers the effective utilization of valuable features in deep networks. In line with the concept of dense connectivity [23], we redefined the bottleneck layer and introduced the cascaded adaptive feature extraction unit (CAE) to facilitate feature reuse and enhance the propagation of useful features throughout the network. Figure 4 illustrates the structure of the CAE unit, which consisted of two convolutional units (Conv_Unit1 and Conv_Unit2), multi-head attention, and atrous spatial pyramid pooling (ASPP). These submodules were interconnected through dense short connections, enabling each module to extract semantic information from the preceding layer or layers, thereby promoting feature reuse and transfer. Additionally, this connectivity aided in the network's convergence.  
Among these submodules, Conv_Unit1 played an essential role in initially enhancing the network's feature representation. Multi-head attention enabled the model to adaptively extract crucial semantic features, focusing on valuable features relevant to liver tumors while reducing the impact of redundant features or background noise. This allowed the model to make more precise determinations regarding organ and lesion locations. ASPP facilitated the acquisition of multi-scale features with diverse receptive fields, enabling the network to capture a richer array of semantic information. Finally, Conv_Unit2 was utilized to further fine-tune the multi-scale features generated by ASPP.
###### Abstract  
Endophytic entomopathogenic fungi are being explored for the management of phytophagous insect pests. The effects of _Beauveria bassiana_ (Hypocreales) inoculation of grape plants on the infestation level of _P. ficus_, tissue nutrient contents, and growth and volatile constituents of potted grape plants were assessed. Grapevine plants were individually inoculated with a suspension of 1 \(\times\) 10\({}^{8}\) conidia mL\({}^{-1}\) of _B. bassiana_ by drenching before experimentally infesting each of them with thirty adult females of _P. ficus_. At four weeks post-treatment, the fungus was re-isolated from leaves of 50% of the fungus-exposed plants. However, no significant difference (\(p>0.05\)) was observed in all the plant growth parameters measured in the fungus-treated and control plants. Plant tissue analysis revealed markedly higher contents of calcium (Ca) and magnesium (Mg) in the leaf tissue of plants exposed to the _B. bassiana_ relative to the control. Gas chromatography mass spectrometry (GC-MS) analyses showed that a significantly (\(X^{2}=5.1\); \(p<0.02\)) higher number of known anti-insect volatile compounds (nine) were present among fungus treated plants compared to the control plants (five). Naphthalene, which is toxic to insects and humans, was detected only in the volatiles of the fungus-exposed plants. _B. bassiana_ did not have any significant effect on total polyphenol, alkaloid, and flavonoids. Overall, treatment with fungus did not inhibit the infestation by _P. ficus_. In conclusion, these findings shed light on some of the mechanisms involved in endophytic fungus-plant-insect interactions.  
g ruevine; volatiles; _Beauveria bassiana; Planococcus ficus_; endophytes
## 2 Theoretical Background and Literature Review
### The Core of 5G Networks: Advanced Ceramic Antenna Technologies
#### 2.1.2 Advantages of 5G Ceramic Antennas and Comparison with PCB Antennas
This configuration not only demonstrates the reduced size of individual antennas due to the higher permittivity of ceramic materials but also showcases the system's capacity to address a wide spectrum of 5G frequencies within a single, streamlined module.
## 3 Results and Discussion
### Comparison with Experiments and Other Data
The increased suction and positive pressure can be explained by the increased and reduced normalized wind speed, \(u/U\), observed above and below the airfoil surfaces, respectively, which are consistent with the observations of the pressure fields (Figures 8e,f and 12e,f) and velocity fields (Figures 8c,d and 12e,f) simulated by the VPM. Moreover, the decrease in the lengths of the separation bubbles, which can be identified by the region of the suction plateau (Figure 17c,e), can be observed as the Reynolds number increases. As a result, the reattachment point, which can be identified as the starting point of the pressure recovery, moves upstream. Downstream of the reattachment points, the amount of pressure recovery (Figure 17a,c) increases with the Reynolds numbers, which explains the drag reduction (Figure 17b). Similar effects of reduced separation bubbles are observed in VPM-simulated surface pressures, as well (Figures 9 and 14). Although the mismatches are observed in VPM simulated forces at \(Re_{C}=60,000\) (Figures 15 and 16), the corresponding VPM pressures in Figures 9e-h and 14e-h resemble those of higher Reynolds numbers in Figure 17c-f; namely, the VPM simulations at \(Re_{C}=60,000\) seem to 'overestimate' the effects of the Reynolds number. Figure 17: XFOIL simulations of surface pressures and forces at \(\alpha=10^{\circ}\) and \(R\epsilon_{C}\) = 4000, 60,000, 120,000, 240,000, 480,000, and 750,000: (**a**) Lift force coefficients. (**b**) Drag force coefficients. (**c**) NACA64 upper surface Cp. (**d**) NACA64 lower surface Cp. (**e**) DU40 upper surface Cp.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 7 Conclusions  
A BICI multi-sensor fusion positioning algorithm is proposed based on the batch form of ICI. Compared with SICI, BCI and SCI algorithm, the positioning accuracy of the BICI algorithm increases by 26.7%, at least in the simulation. We presented the BICI-IMM algorithm with unknown motion model, uncertain time-varying noise variance and unknown covariance based on an IMM algorithm structure. The positioning accuracy increased by 14.3% compared with BICI. The BICI-IMM multi-sensor fusion algorithm has better positioning accuracy than SICI in the open and semi-open scenes. The BICI-IMM can provide a robust positioning for the distributed control of robots and assist in the rapid construction of dynamical network topologies [36]. It helps to decrease the average time taken to organize the group in a coordinated behavior and increase the moving speed of robots in real applications.  
Conceptualization, Y.L. and Z.D.; Investigation, Y.L.; Methodology, Y.L. and Z.D.; Validation, Y.L., Z.D. and E.H.; Visualization, Y.L.; Writing--original draft, Y.L.; Writing--review and editing, Z.D. and E.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2016 YFB0502003).  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
GNSSGlobal Navigation Satellite Systems  
CICovariance Intersection  
CVConstant Velocity  
CTConstant Turn  
BCIBatch Covariance Intersection  
SCISequential Covariance Intersection  
PCIParallel Covariance Intersection  
ICIInverse Covariance Intersection  
SICISequential Inverse Covariance Intersection  
BICIBatch Inverse Covariance Intersection  
IMMInteracting Multiple ModelRMS Root Mean Square CDF Cumulative Distribution Function RTK Real-Time Kinematic
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were performed in SPSS Statistics version 29 (IBM Corp., Armonk, NY, USA) and R (R Foundation for Statistical Computing, Vienna, Austria). The mean and standard deviation (SD) were given as a measure of the centrality and dispersion of the data. The Shapiro-Wilk test was used to check the assumption of normality. The effect of overload on \(\
u_{\rm{max}}\) was assessed with a one-way analysis of variance (ANOVA), while the 3 (overload: MiL vs. MoL vs. HiL) \(\times\) 2 (method: FP vs. HRD) repeated measures ANOVA was used for the remaining variables. Greenhouse-Geisser adjustments of the \(p\)-values were used if the assumptions on sphericity were violated. The effect size was assessed using partial eta squared (\(\eta^{2}\)), with a value from 0.01 to 0.05 representing a small effect, one from 0.06 to 0.14 representing a medium effect, and a value greater than 0.15 representing a large effect. To detect differences between the overloads and the different calculation methods, a post hoc test was performed using the Holm-Bonferroni probability adjustment, with the significance level set at \(p<\) 0.05. To further assess the feasibility of the HRD, a correlation between the HRD and the FP was calculated for the outcome variables using Pearson's product moment correlation coefficient (r). The degree of correlation was interpreted as trivial (0.00-0.09), small (0.10-0.29), moderate (0.30-0.49), large (0.50-0.69), very large (0.70-0.89), nearly perfect (0.90-0.99), or perfect (1.00) [32].
## 3 Results and Discussion
### MEG/EEG-Based Hyperscanning Research
#### Education  
These studies were conducted in a classroom setting with portable EEG equipment to measure the brain-to-brain neural synchrony and joint attention between a teacher and her students. The education application makes up 3% (2/60) of the research done with the MEG/EEG neuro-imaging methodology. The goal of these studies was to gain more knowledge in the learning-process--specifically in a classroom setting. These studies looked into the most effective teaching methods by measuring their neural-responsivity/attention and testing their material retention with quizzes. In the first study done in a classroom application by [PERSON] et al. [35]. EEG was used to record 12 students' brains simultaneously under different teaching conditions (video vs. lecture) and post-class quizzes were given to measure engagement and material retention. This study showed an increase in students' brain-to-brain synchrony when they are more engaged in class and a positive correlation between brain-to-brain synchrony and teacher likeability. Interestingly, this study also showed that brain-to-brain synchronization was highest in the preferred teaching style (videos), not lectures [PERSON] et al. [31] extended [PERSON] et al. [35] research of brain-to-brain synchrony and learning outcomes in a real-world classroom. Specifically, this study evaluated the effect of student-teacher relationships on student-teacher brain-to-brain synchrony in a high school biology class (12 th graders). This study found that brain synchrony and quiz scores on class material were higher for educational videos compared to lectures, which is possibly due to videos being more entertaining and therefore more engaging. The study on the biology class found no correlation between student-teacher synchronization and quiz scores, but a positive correlation between student-teacher closeness and quiz scores. However, this study failed to look at retention of information as a function of time and failed to record when, during the class, student-teacher synchrony was highest, and if that timing agrees with the topics covered by missed quiz questions. This information would help to accurately reflect the retention of information.  
_Setting_. No study was done in the lab, instead both studies took place in a natural classroom setting [31; 35]. During both studies students were able to look at each other and the teacher, thus we classified this as face-to-face orientation.  
_Orientation_. Both studies also evaluated the neural synchrony between the students (group synchrony) and between the student(s) and teacher, thus 100% (2/2) of the studies looked at the neural responses in a classroom of students (more than two participants). This is an effective way to research a classroom setting, because it takes into account student-to-student interaction as a factor.  
_Group Size._ One study was done over 11 class sessions [35], while the other study took place over six class sessions [31]. Both studies had a sample (class) size of 12 [31; 35].
## 1 Introduction
Some examples of such approaches where Copernicus was involved include both in-person and virtual workshops held in tropical Andean countries, covering various topics such as radar-based remote sensing [19], monitoring glaciers [20], indigenous land management [21], and monitoring forests [22]. Nevertheless, the literature shows that it is less common for workshops to go beyond the use of Sentinel data and its specific applications to utilizing the full range of possibilities offered by Copernicus's services, which include applications to the atmosphere, marine environment, land, climate change, security, and emergency management domains. It is more challenging to hold a workshop on these, as a multidisciplinary approach and tailored training programs are needed to effectively address the diverse aspects of Copernicus's services. To contribute to this area that has seldom been studied in the literature, this research documents the experiences of design and implementation of training workshops on Copernicus's services held in Ecuador and panel discussions in Bolivia, both in the tropical Andes. Ecuador and Bolivia were chosen due to their similar environmental challenges, institutional needs, and interest in geospatial tools provided by Copernicus, which aligned with the research goals and the openness of government institutions. Although we worked with similar target groups (i.e., mostly government personnel), challenges such as cultural differences, organizational climate, language modisms, technology use, and economic reliance on mining in Bolivia and oil in Ecuador provided a testbed for capacity-building methods in Latin America. The Ecuadorian training workshops, lasting 10 days, focusedon strengthening stakeholders' capabilities in specialized topics on Copernicus's services, while the Bolivian panel discussions, lasting 4 days, aimed to promote the use of Copernicus's services and identify any need for advanced training. Both events emphasized applications of Sentinel data through Copernicus's services, along with a variety of different tools. To organize the documentation of these experiences, this article begins with a background on the Copernicus program and foundational information about the events. It then details the procedure for identifying the needs, organizing the curriculum, the contents of the presentations, the training materials, and key indicators of the events' success. The results highlight and discuss the successes and challenges during the workshop and panel discussions, offering recommendations for multidisciplinary Copernicus-focused training programs.
## 2 Application of Networked Mobile Sensors
### Simultaneous Localization, Mapping and Exploration
Percolation enhanced entropy based SLAM harnesses both exploration and exploitation by switching between the two under a switching controller, depending upon whether position or map accuracy is needed during FastSLAM [42].
## 3 Characteristics of Spoke Type IPM Model and Flared Type IPM Model
### Experimental Results  
The load test was performed using the prototype. Figure 18 shows the torque waveform at rated load, and Table 10 shows a comparison of the basic spoke type model and the prototype of the proposed flared IPM model. As shown in Figure 18, the experimental results of the proposed flared IPM model were similar compared to the analytical values. Additionally, torque ripple decreased by 66.2%, and efficiency improved by 0.9%. This confirms the performance improvement of the proposed model.