esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Field-of-View Visible Light Communication: Concept and Prototype Development
### Discussion on the Experimental Results, and Future Perspectives
In order to be on the safe side, the lowest FoV was considered in this case, to ensure that the BER was as low as possible. Nevertheless, the accuracy could be further improved based on a received DC noise signal analysis and SNR estimation, with a better hardware platform and software algorithms. Another improvement is based on a solution integration that dynamically handles the tradeoff between the BER and a wide FoV. For example, this solution should also consider the distance between the VLC transmitter and the VLC receiver. Thus, as the distance increased, the received optical power decreased, whereas the geometric distribution led to a lower margin of error due to misalignment. Consequently, in such surroundings, reducing the FoV to mitigate the effects of optical noise and to compensate for the decreasing optical power of the data signal was perfectly justified, thus improving the SNR and avoiding the loss of link. On the other hand, when the transmitter-receiver distance decreased, the wide FoV had a lower impact on the SNR, but it facilitated the connectivity due to the LoS with the VLC emitter, increasing the mobility of the system at the same time. A dynamic adaptation of the FoV based on distance measurements could be intensively studied and variable solutions could be modeled and investigated in order for the system to move one step closer to real-life deployment. Such an approach is facilitated by the VLC systems' ability to provide distance measurements and relative positioning as well [49; 50; 51]. Adaptations based on the priority of the data could also be included in a multilevel architecture for a context-adaptive system. In light of the above, one can see that the integration of an FoV adaptation function enables the VLC system to significantly improve its resilience, mobility, and efficiency. Additionally, one can say that the integration of context-awareness and context-adaptive functions seems to be the most efficient solution to take vehicular VLC systems to the point where they are ready for deployment. Nevertheless, establishing the optimal adaptation parameters is still challenging. Last, but not least, it should be mentioned that the compact size of the prototype enables its integration on real cars. Moreover, the experimental testing of the system in mobile conditions with the VLC system implemented on a car are currently being performed and have provided encouraging preliminary results.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
###### Abstract  
We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth's ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze a large dataset of whistler observations, which has been developed to advance our understanding of whistler generation and propagation. Unlike conventional image-correlation-based techniques, WhISPER identifies whistlers based on their energy content, enhancing computational efficiency. This work presents the results of applying WhISPER to four years (2019-2022) of top-side ionospheric magnetic field data. A statistical analysis of over 800,000 detected whistlers reveals a strong correlation with lightning activity and (as expected) higher occurrence rates during local summer months. The presented results demonstrate the excellent performance of the WhISPER technique in identifying whistler events.  
whistlers; top-side ionosphere; automatic detection; spectral analysis; lightning activity
## 1 Introduction
In the present work, we compare our method with two state-of-the-art statistical parsing techniques, namely dependency and constituent parsing from SpaCy and Stanford, respectively. These two approaches lead to parse trees, which both follow the Robinson Axioms, but are complementary to each other. Dependency parsing focuses on the relationships between words (e.g., subject-verb-object), capturing syntactic dependencies, while constituent parsing identifies hierarchical phrase structures (e.g., noun phrases and verb phrases), revealing the sentence's overall syntactic organization. Our parsing approach can be seen as a third possibility to capture the linguistic structure of a sentence but that has an inherent mechanism to handle ambiguities, which the other two do not have. Dependency parsing and constituent parsing are less informative than our approach; therefore, we include large language models (LLMs), namely, GPT-4 and GPT-3.5, in our analysis, although they are not specifically designed for the grammatical parsing of sentences. These LLMs, as our parser, are capable of determining main- and sub-clauses, and the cases of nouns and linguistic information, which is not present in the parse trees of the dependency and constituent parsers. We begin in Section 2 with a brief description of other parsing techniques, and describe how LLMs process natural language grammars. We then present our rule-based parser in detail in Section 3. The description presented in this paper extends the description of the parser in [PERSON] et al., 2023 [PERSON] et al. (2023). In Section 4, we show the parsing results of the dependency parser SpaCy, the constituent parser from Stanford, together with the answers from the LLMs, GPT-4 and GPT-3.5, and compare these results with our parser. The results are discussed in Section 5 and concluded in Section 6.
# A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism
## 2 GNYL (Global Normalization Attention Mechanism You Only Look Once)  
This paper designs a GNYL object detection network based on YOLOv8l, with the network structure shown in Figure 1. The GNYL network structure consists of a backbone network and a detection head. The backbone network utilizes the CSPDarknet feature extraction network, while the head comprises a feature enhancement network and detection heads. This paper aims to improve the problem of dense small object detection in aerial datasets according to the following two aspects:  
1. Adding the Global Normalization Attention Mechanism (GNAM): The GNAM first processes the input features using a channel attention unit, which utilizes scale factors from batch normalization (BN) to highlight features based on the variance measurement of the training model weights and then inputs them into a spatial attention unit, which incorporates spatial information using two convolutional layers, further emphasizing the spatial information. The interaction ability between the channel and spatial information is improved, fully exploits effective information for classification and localization tasks within the input features, and enhances the richness of detail in the GNAM's output features. In order to increase the detail richness of the GNAM's output features, the GNAM is added to the penultimate layer with rich semantic information in the backbone network;
2. The network architecture design, including the feature enhancement network and detection head design. Adding larger-scale detection heads increases the number and density of the anchor boxes, thereby improving the localization accuracy of small objects. A high-resolution feature enhancement network is employed to preserve more detailed information. Finally, utilizing large-scale detection heads with large and dense anchor boxes enhances the fitting between the predicted boxes and target boxes, thereby improving the localization accuracy.
## 3 Results
### Nanemulsion Stability During Storage
#### 3.2.1 Physical Stability  
Droplet Size During Storage  
Nanoemulsions are inherently unstable colloidal systems, susceptible to various destabilization mechanisms, such as sedimentation and creaming, due to the presence of poly-disperse droplet sizes and intermolecular attractive forces. Despite their enhanced stability during storage, fluctuations in temperature can significantly influence their physicochemical properties, ultimately leading to product destabilization. Therefore, the physical stability of the nanoemulsion was assessed by monitoring the mean droplet diameter (MDD) over a 2-month storage period at 4 \({}^{\circ}\)C.  
After 2 months, all samples stored at 4 \({}^{\circ}\)C were found to be quite stable in the nano-sized range, with a maximum MDD of 307.5 nm for the nanoemulsions with olive pomace oil and ascorbic acid (Table 2). The DLS analysis demonstrated that nanoemulsions with corn oil and ascorbic acid were subjected to significant destabilization, as the droplet size increased by 15%, compared to the other samples, which maintained their initial MDD or showed a slight increase of 5% or 6%. The satisfactory stability of the o/w nanoemulsions observed is attributed to their initial small droplet size and fairly narrow droplet distribu  
Figure 2: Typical droplet-distribution graph based on intensity for olive pomace oil (OPO), carola oil (CLA), corn oil (CO), fish oil (FO), and sunflower oil (SO) nanoemulsions enriched with lycopene and ascorbic acid (AA) after the homogenization process.  
tion, which allows them to remain stable against Ostwald ripening for a period of time. Nanoemulsions are in the kinetically stable regime, where droplet growth is thermodynamically favored and the kinetic energy barrier slows this growth. However, if the particle size distribution is wide, Ostwald ripening will occur at a significantly higher rate. Other studies have also demonstrated that emulsions used in food and beverages, with around 10-20 wt.% of the oil phase, can be stabilized against Ostwald ripening by incorporating water-soluble components [45; 48]. Moreover, the high stability of the nanoemulsions can be attributed to high oil concentrations and consequently to higher viscosity. When viscosity increased, the rate of Brownian motion decreased, thereby the aggregation effect caused by thermal motion was reduced [18]. This viscosity-related stability is essential for maintaining a consistent droplet size over time, as reduced movement of the droplets minimizes coalescence and phase separation, preserving the properties of the nanoemulsion.
## 3 Results and Discussion
### Limitations of Study  
This study has shown that autonomous delamination detection in bridge decks using DCNN models that are trained, validated, and tested with publicly available IRT datasets is saddled with some challenges and limitations. The reliability and performance of the models to correctly predict delamination depends largely on several factors. The final condition map shows that an appropriate model, image processing and quality, hypertuning parameters, and others would largely determine the performance of the developed model and segmentation map.  
Figure 18: Adaptive image-based model for delamination detection for (**a**) FRNB and (**b**) FRSB bridges [14].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Bridge** & \multicolumn{3}{c}{**FRNB**} & \multicolumn{3}{c}{**FRSB**} & \multicolumn{3}{c}{**PRND**} & \multicolumn{3}{c}{**FRNB**} & \multicolumn{3}{c}{**PRSB**} \\ \cline{2-13}
**Model** & **Image-** & **aNet** & **Incease** & **Image-** & **aNet** & **Incease** & **Image-** & **aNet** & **Incease** & **Image-** & **aNet** & **Incease** & **Image-** & **aNet** & **Incease** & **Image-** & **aNet** & **Incease** \\ \hline GA & 0.69 & 0.728 & 5.4 & 0.716 & 0.736 & 2.8 & 0.655 & 0.719 & 9.8 & 0.696 & 0.644 & \(-\)7.5 & 0.731 & 0.773 & 5.8 \\ \hline F1-cc. & 0.26 & 0.295 & 13.5 & 0.099 & 0.268 & 170.7 & 0.131 & 0.290 & 121.4 & 0.287 & 0.268 & -6.6 & 0.148 & 0.261 & 76.4 \\ \hline TPR & 0.248 & 0.326 & 31.5 & 0.077 & 0.292 & 279.2 & 0.123 & 0.307 & 149.6 & 0.254 & 0.257 & 1.2 & 0.136 & 0.286 & 110.3 \\ \hline TNR & 0.816 & 0.813 & 0.0 & 0.879 & 0.824 & \(-\)6.3 & 0.799 & 0.814 & 1.9 & 0.836 & 0.776 & \(-\)7.2 & 0.853 & 0.852 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of image-based [14] against UNET model.  
DCNNs are required to be developed with large datasets for training and validation. Previous studies have only centered on developing and training models with laboratory model samples. This study adopted only datasets from in-service bridges, without any laboratory datasets. This invariably presented limited datasets for study. The TPR of the model is still seen to be lower than 35% for the FRNB bridge deck. This implies that less than this proportion is detected as defective by the model, resulting in higher false positives (FP). This may have been due to the quality of the image. [PERSON] et al. [14] highlighted the impact of image quality on the performance of the model. In addition, the feature extraction layer may not have extracted very prominent features for classification. This may be due to fewer features in thermal images when compared to visual images, which contain prominent and notable textural features for classification. Future studies will also include hyper-tuning parameters and modifying the models' architecture by adopting the backbone of other architecture.
## 5 Types of UAV Deployments
### Standalone UAV Deployments  
Standalone UAV deployment involves the deployment of single or multiple UAV-BS to provide network service in an area without fixed cellular network coverage. Two major deployment scenarios exist in this approach. The first scenario involves the deployment of a single UAV-BS to provide wireless network service in an area, including harvesting data from IoT networks deployed in a field, acting as a relay to provide wireless service to users that have been separated by large obstacles such as mountains and hills, etc. [108]. In the second scenario, multiple UAVs are deployed in the form of a swarm network for the following purposes: (i) to provide a complete wireless network service to a particular area, which could be a dedicated network for an organization; (ii) to provide wireless service in rural areas without prior cellular network infrastructure; (iii) to restore network coverage for a large area that has been affected by a natural disaster such as an earthquake or a volcano [109]. The major challenge with single UAV deployment is that when a fault occurs in the UAV-BS, it could result in complete network failure. On the other hand, in a multi-UAV-BS system, when a single UAV fails, we can reconfigure the system and still have a sub-optimal solution. However, this does not mean that the use of multiple UAV-BSs does not have disadvantages, as the problem of proper coordination among the multiple UAVs deployed exists [110]. In summary, choosing between a single UAV or multiple UAV deployment depends on the nature of the communication system that is being developed and the problem that the network would address.
## 6 Performance Evaluation  
In this section, we provide routing performance results that compare the ASAART, SHR and SSR protocols. Because our routing technique (ASAART) integrates the autonomous self-awareness and adaptive feature of routing path discovery and route repair into SHR, we use the Sensor Network Simulator and Emulator (SENSE) [36] for our performance evaluation. We consider both local and global network state information to compute the routing decision using the modified self-aware formula and backup strategies and efficient and reliable route repair technique as given in Section 5.  
We performed five sets of simulations under a large and scalable sensor network with different simulated scenarios. In the first to third scenarios, we evaluate the network density test under a large and scalable sensor network. We consider a sensor network with 100 to 1000 nodes in step of 100 nodes. We vary the number of source nodes from 10 to 20 and 40 nodes in increasing network traffics. We evaluate protocol performance under increasing network density to observe the extent to which the proposed protocol is fault tolerant and resilient against errors with different network configurations. In the fourth and fifth scenarios, we evaluate the performance of the three protocols under transient and permanent node failure rates. In our simulation, we consider a 2000 \(\times\) 2000 m\({}^{2}\) terrain densely populated with 100 to 1000 nodes in step of 100 nodes, all which have a nominal transmission range of 250 m. We employed the free space propagation model in order to simulate the wireless medium [37]. We used the Constant-Bit-Rate (CBR) model to simulate the bi-directional traffic between the sources and destinations. The simulation at each scenario was performed at least five times with a different number of seeds to ensure accurate and reliable measurements and avoid errors. The 95% confidence intervals are shown in the figures.
## 5 Discussion  
### The Analysis of Experimental Results  
In order to verify the estimation performance of the proposed estimation framework, we compare and calculate the computational load and mean square error (MSE) of the proposed approach and three common approaches for three cases.  
The comparative estimation results of Case 1 are shown in Table 1 below.  
According to Table 1, we can obtain that, in the absence of MMs and cyber-attacks, the sum of MSE of the approaches in [11; 27; 55] is higher than that of our approach by 85.22%, 79.78% and 96.34%, respectively, while the difference between their calculation load and our approach is within 3 seconds, except for [11]. Although the approach in [11] is fast, the accuracy is too low. Therefore, the comprehensive comparison shows that our approach is optimal when MNNs are not influenced by MMs and cyber-attacks.  
The comparative estimation results of Case 2 are shown in Table 2 below.  
According to Table 2, we can conclude that, in the presence of MNNs with MMs and cyber-attacks, the sum of MSE with the approaches in [11; 27; 55] are 87.93%, 79.78% and 96.91% higher than our approach, respectively. Furthermore, during MMs, their MSE values are 78.33%, 38.92% and 82.55% higher than our approach, respectively. The results show that, even when MNNs are influenced by MMs and cyber-attacks, our approach still has high estimation performance and can significantly reduce the influence of MMs on the estimation performance accuracy.  
The comparative estimation results of Case 3 are shown in Table 3 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimation Approach** & **Calculated Load (s)** & **Sum of the MSE** \\ \hline approach in [27] & 24.213 & 2.010 \\ approach in [55] & 19.733 & 1.469 \\ approach in [11] & 7.462 & 8.134 \\ our approach & 21.452 & 0.297 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation performance of Case 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Estimation Approach** & **Calculated Load (s)** & **Sum of the MSE** \\ \hline approach in [27] & 31.462 & 2.081 & 61.156 \\ approach in [55] & 24.421 & 1.319 & 21.698 \\ approach in [11] & 8.421 & 8.127 & 75.927 \\ our approach & 26.597 & 0.251 & 13.251 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimation performance of Case 2.  
Figure 13: True state and its estimated values based on our approach and the approach from [55].  
From the practical system case in Table 3, we can see that, compared with the approach in [55], the computational load of our approach is slightly lower, but the sum of MSE values during MM occurrence is 92.24% and 82.76% lower than the approach in [55]. This case also shows that the comprehensive estimation performance of our proposed estimation framework is better and practical.
## 5 System Implementation
### Redundancy Removal of Component Geometry Information  
For the convenience of design, production, and construction, many components in steel structures have the same geometry. The removal of redundancy components with the same geometry can greatly improve the efficiency of storage, network transmission, and rendering. Given that there are many instances in the steel BIM models, preliminary screening was performed to reduce the scope of comparison according to the type of elements, the number of vertices, the number of patches, and other information. After PCA alignment for all instances, for elements with the same basic attributes, the developed system sampled the geometric vertices and patch centroids and then calculated the Hausdorff distance. If the error of the point set meets the threshold, the point set would be treated as geometric information shared by instances, and then only the GUID and the global coordinate transformation matrix would be marked. In the output gITF files, different geometry nodes shared the same mesh, and the matrix attribute was added to the Node in gITF to record the coordinate positions of different nodes. The gITF files generated according to the above rules are universal and can be used by other software systems.  
When using the three.js framework to render the model in the browser, it is necessary to reduce the number of drawbacks as much as possible. Based on the above procedure, many different instances with the same geometry trigger a drawcall only once, significantly reducing the times of input and output. The three.js framework provides the GLTFLoader plugin to load the gITF file. However, in this framework, in order to maintain the names of geometry nodes and other attributes, the shared meshes need to be duplicated multiple times and transformed during the loading process, i.e., performing a drawcall for each shared instance. To solve this problem, the authors improved the plugin by performing coordinate transformation in the vertex shader, as well as creating a mapping relationship between the transformation matrix and the GUID attribute for each instance such that the transformation matrix can be achieved when clicking interaction. Thus, the GUID attribute of the clicked component can be obtained, and the component attribute in the back-end database can be searched and displayed in the front end. Figure 9 shows the display interface before and after the geometric redundancy elimination of a steel workshop model. The testing results are listed in Section 6.