esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Open Problem and Research Challenges
### Open Challenges
#### 5.1.5 Energy Consumption  
The harsh conditions of UWSN pose another great challenge, which balances the energy and longevity of the sensors. Moreover, the size of the sensors makes it hard to provide them with high-power batteries, as it is hard to replace the sensors or their battery in UWSN architecture [1]. Finally, propagation underseas is rough due to latency, Doppler effect, delay, low bandwidth, and dynamic variation. In addition, the one-way transfer handicaps the acoustic channel; this causes an imbalance during routing. Keeping this in mind when designing, it is hard to make simple routing protocols for UWSN. Future research needs work on solving this problem.
## Literature Review
### Weather Forecast  
A weather forecast (also called prognosis) is the application of science and technology to make a detailed description of expected future occurrences in the atmosphere at a specific location. A weather forecast includes the use of models based on certain atmospheric parameters [25]. In addition, it involves the search based on the momentary meteorological conditions that seek to anticipate future weather conditions. This study involves knowledge and data from a variety of areas, such as sky views, cloud formation analysis, observed temperature, atmospheric pressure, Doppler radar images, meteorological stations, atmospheric balloons, and other data [26].  
From the knowledge and data collected from several areas [27], monitoring of air masses occurs 24 h a day. Information about temperature, air pressure, wind speed, and humidity are also collected [28]. These data are used to make numerical models that run on supercomputers, which compare the initial state and analyze the various possibilities of the evolution of time, generating predictions based on probabilities [29]. Even with all this reading done by modern computers, much of the forecast comes from the readings made by meteorologists, who analyze the predictions and conclude on them after making the corrections that are necessary [30].  
The climatic behavior of Minas Gerais is a relevant factor for the culture, economy, and agricultural activities in this territory. The state concentrates a considerable number of river springs vital to Brazil, a large part of the territory destined to the breeding of animals, coffee plantations, and other food types. Therefore, it is necessary to evaluate the climate behaviors in the state, allowing corrective or preventive measures to be taken to avoid associated impacts on daily actions in Minas Gerais. These meteorological indices are monitored by agencies controlled by the Brazilian and Minas Gerais government. There are physical stations to control state-critical regions, receiving data, and climate specifications from each of these regions. Figure 4 presents installations of these climate control structures in southeastern Brazil, mainly focusing on the territory of the state of Minas Gerais.  
For example, rainfall and weather influences can change actions and strategies in food planting or in storing water for people or crops [31]. Figure 5 presents an image collected from a satellite, identifying in July 2019 (traditionally with low rainfall) locations and densities of water precipitation in the Brazilian territory.  
Figure 3: Wind currents behavior (200 h Pa) (satellite Image). Adapted from Imnet.  
Another relevant factor for climate analysis can be observed in assessing a nation's temperatures. Figure 6 represents an assessment of average temperatures in Brazil in August 2019.  
Figure 4: Meteorological stations installed in southeastern Brazil. Adapted from Inmet.  
Figure 5: Rainfall indices (mm/h) in the month of July 2019. Adapted from Inmet.
# Dark-Center Based Insulator Detection Method in Foggy Environment
###### Abstract  
In foggy environments, outdoor insulator detection is always with low visibility and unclear targets. Meanwhile, the scale of haze simulation insulator datasets is insufficient. Aiming to solve these problems, this paper proposes a novel Dark-Center algorithm, which is a joint learning framework based on image defogging and target detection. Firstly, the dark channel prior algorithm is used to calculate the foggy sky image transmittance and then transpose it to the original image to generate a foggy-simulated insulator dataset; secondly, the defogging and restoration modules and an optimized defogging module are combined to improve the robustness of the defogging algorithm; then, for small insulator detection, the CenterNet network structure is improved to enhance the feature extraction capability for small targets; finally, the target detection accuracy in foggy environments is improved by jointly learning the structure details and color features recovered in image defogging via the defogging model and the target detection model, which effectively learn the structure details and color features recovered in image defogging. The experimental results on the CPILD dataset show that the proposed Dark-Center algorithm based on image defogging and target detection can effectively improve the performance of the target detector in foggy scenes, with a detection accuracy of 96.76%.
## 5 Evaluation Measures
### Miscellaneous
#### 5.4.4. Explanation Preferences  
Explanation preferences have also been a focus in several evaluation studies, aiming to understand which explanation styles or types users prefer [63; 72; 73; 74; 89]. In most studies, various explanation styles or types were compared to determine user preferences. A common approach involved presenting users with one or more outputs, where different explanation concepts were available. Then, participants evaluated these explanations and were asked to express their preferences. With regard to the experimental design, two approaches are common: In a between-subject design, different groups of participants are exposed to different explanation styles. In contrast, in the case of a within-subject design, all participants are exposed to multiple explanation styles simultaneously or in a (random) order.  
Regarding preference assessment, two methods are commonly used: ranking-based and rating-based. Ranking-based methods require participants to rank the different explanation concepts, forcing them to prioritize their preferences by directly comparingexplanations against one another. This method has higher ecological validity, because in real-life situations, users must often choose between options. However, ranking-based methods require a within-subject experiment, where ordering effects must also be considered. On the other hand, rating-based methods involve asking participants to rate each explanation independently on a Likert scale. This allows users to express their preference level, offering more nuanced insights. It also enables the use of between-subject experiments. In our sample, [PERSON] et al. [89] employed a rating-based approach and additionally asked open-ended questions about which system participants preferred and why.  
In contrast, [PERSON] et al. [72] used a ranking-based approach, where participants were asked to select which of the two available methods offered a better explanation for the provided model prediction. Similarly, [PERSON] et al. [73] asked participants whether they would choose an AI model or a human. Still, they used a 5-point Likert scale ranging from \"_definitely prefer the model_\" to \"_definitely prefer the human_\".  
Various studies also used evaluation measures specific to the respective context, such as debugging support, situational awareness, and learning or education. These measures aim to gain a better understanding of how well explanations address particular needs and goals in a given context.
## 8 Use Cases and Applications  
Drones and autonomous ground vehicles have proven to be excellent tools with a wide range of applications that impact a variety of industries. Their flexibility, precision, and availability are some of the most important factors in their procurement. This chapter describes the various uses of drones and autonomous vehicles, thereby ranging from agriculture to surveys, monitoring, and beyond.
# Automatic Point-Cloud Registration for Quality Control in Building Works
## 3 Results and Discussion
### Data Acquisition  
Data acquisition was performed with the UGV described in Section 2.1. For the first study case, seven stations were needed, in the Stop&Go strategy, for the complete acquisition (Figure 9b). For the second study case, a total of 11 stations were needed to cover all the spaces (Figure 9d). In the second study case, more stations were necessary, since this floor was in a more advanced state of construction and there were more occlusions between elements. The position of the stations was optimized by the path planning programmed in the UGV [43] in order to minimize the trajectories (Figure 9a,c), acquiring all the information required. Since both floors were in the construction phase (Figure 9b), the trajectory had the requirement of including all the pillars in the data acquisition. Resolution and quality of the data acquired were adjusted in a compromise to obtain all the information needed and to minimize acquisition time and quantity of data generated. Thus, 60% overlap between stops was established, together with a scanning resolution of 4 mm at 20 m. The overlap percentage was selected towards the optimization of the automatic registration process in terms of precision and reliability. In addition, providing the minimum acquisition range of the TLS (0.6 m), all points at a distance below 0.8 m from the TLS were discarded from the acquisition (Table 2)  
Figure 8: Building selected as case study: (**a**) 2D planes of the buildings. (**b**) Building information modelling (BIM) model of the first floor in the north block selected as the first study case. (**c**) BIM model of the ground floor in the south block selected for the second study case.  
The time required per scan position was approximately 3 min. This value should be added to the time required in the trajectory between stops, for a total acquisition time of 25 and 45 min for the first and second study cases, respectively.  
For the first and second study cases, the result of the acquisition was six and eleven point clouds with 24 million points each, respectively. To have a homogeneous and clean point cloud in the next steps, the raw point clouds were filtered as described in Section 2.2. In this application, the voxel size was established at 0.5 cm, resulting in point clouds about 7 million points each and with homogeneous data distribution. The UGV can operate at night and can go over piles of rubble as well as muddy and wet surfaces thanks to its caterpillar wheels.
## 3 Results and Discussion
### Characterization of Cold and Hot Brew Coffee
#### 3.3.3 Volatile Compounds in Cold and Hot Brew Coffee  
The volatile compounds in cold brew and hot brew coffee samples were analyzed using GCMS, and the results are shown in Table 8. A total of 23 volatile compounds were identified in CBC samples, while in hot brew coffee samples, 16 volatile compounds were detected. The main compounds detected in both cold and hot brew samples were furans and pyrazines. Other minor volatiles detected were phenol, pyridine, ketone, aldehyde, oxime, and pyrrole. This agrees with a study by [PERSON] et al. (2021), which found that furans and pyrazines are the major volatile compounds in coffee [32].  
Pyrazine is associated with nutty, earthy, roasty, and green aromas, while furans are linked to sweet, caramel, fruity, roasted, and earthy aromas and flavors [32]. Our findings showed that the percentage area of volatile compounds was higher in CBC samples than in hot brew coffee. Apart from that, some volatile compounds, including 1-methyl-1H-pyrrole, Furan, 2-(methoxymethyl)-, Pyrazine, 2,5-dimethyl-, Pyrazine, 2,6-dimethyl-, Pyrazine, 2-ethyl-5-methyl-, Pyrazine, 3-ethyl-2,5-dimethyl-, Furfuryl formate, and 2-Methoxy-4-vinylphenol were identified in cold brew samples but not detected in the hot brew. Only 3(2H)-Furanone and dihydro-2-methyl- were detected in the hot brew but were not detected in CBC. This suggests that different extraction conditions play a role in volatile compound extraction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cold Brew Coffee** & **Hot Brew Coffee** & \(p\)**-Value** \\ \hline Total phenolic content (GAE/mL) & 2.0281 \(\pm\) 0.1538 & 2.0737 \(\pm\) 0.1349 & 0.7193 \\ \hline Antioxidant assay & & & \\ FRAP assay (\(\mu\)mol TE/mL) & 8.5374 \(\pm\) 0.0916 & 8.6428 \(\pm\) 0.4567 & 0.7305 \\ DPPH radical scavenging assay & 0.0066 \(\pm\) 0.0002 & 0.0065 \(\pm\) 0.0002 & 0.6005 \\ (\(\mu\)mol TE/mL) & 47.8460 \(\pm\) 1.3109 & 34.8303 \(\pm\) 3.7049 & 0.0171 \({}^{\star}\) \\ \hline \hline \end{tabular} \({}^{\star}\)Statistically significant, \(p\) < 0.05; results are the mean \(\pm\) standard deviation from triplicate extractions.  
\end{table}
Table 7: Total phenolic content and antioxidant activities of cold and hot brew coffee.
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
## 3 Results and Discussion  
### Effect of Operational Parameters on Kernel OSI Measurements  
Results of oxidative stability measured in the walnut kernels at three different temperatures and three different airflow rates are shown in Table 1. All temperatures affect the oxidation stability of the kernel significantly (\(p\) < 0.0001). The induction time was about halved with each 10 \({}^{\circ}\)C increase in temperature. Measurements at 110 \({}^{\circ}\)C were significantly higher than measurements at 120 \({}^{\circ}\)C and subsequently at 130 \({}^{\circ}\)C, with an average of 13.9, 5.3 and 2.6 h, respectively. Differences between airflow rates were significant only at 110 \({}^{\circ}\)C with 20 L h\({}^{-1}\), giving the highest oxidative stability values. Contrary to our results, studies in soybean oil, demonstrated that airflow above 15 L h\({}^{-1}\) significantly affect the OSI [20]. In a fluid matrix, it is more difficult to reach the equilibrium at higher airflows. Our results showed that with small sample size, the equilibrium was reached, giving no significant differences between airflow rates.  
As the accelerated method was conducted on the same samples with different operational parameters, direct comparisons of the absolute value of induction times could not provide insightful information. Instead, the coefficients of variance (CV) were calculated to distinguish operational  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Flow Rate (L h\({}^{-1}\))**} & \multicolumn{6}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**110**} & \multicolumn{2}{c}{**120**} & \multicolumn{2}{c}{**130**} \\ \cline{2-7}  & **OSI** & **CV** & **OSI** & **CV** & **OSI** & **CV** \\ \hline
15 & 13.2 \({}^{\text{b}}\) & 10.5 & 5.2 \({}^{\text{c}}\) & 8.9 & 2.4 \({}^{\text{d}}\) & 8.1 \\
20 & 14.9 \({}^{\text{a}}\) & 6.9 & 5.2 \({}^{\text{c}}\) & 5.7 & 2.5 \({}^{\text{d}}\) & 8.4 \\
25 & 13.5 \({}^{\text{b}}\) & 4.4 & 5.4 \({}^{\text{parameters effects on OSI and determine the best parameter combination to evaluate walnut kernels (Table 1). At lower temperature (110 \({}^{\circ}\)C), airflow showed to be decisive for the reproducibility of the method, with 15 L h\({}^{-1}\) giving the highest and 25 L h\({}^{-1}\) the lowest CV. The highest temperature (130 \({}^{\circ}\)C) consistently yielded higher CV for all flow rates studied (7.6-8.1). By excluding the lowest airflow (15 L h\({}^{-1}\)) and the highest temperature (130 \({}^{\circ}\)C), we narrowed down the operational parameters for further investigations.
# Characterization of the Migration of Soil Particles in Lateritic Soils under the Effect of Rainfall
## 2 Research Background and Experimental Design  
### Characteristics of Soil Erosion in the Study Area  
#### 2.1.1 Erosion Profile  
The 110 catastrophic debris flow gullies in the Xiaojiang River Basin, represented by Jiangjiagou, form serious soil erosion, and a large amount of sediment enters the Jinsha River, and the amount of sediment that enters the Xiaojiang River in the territory of Dongchuan City, Yunnan Province, alone is as high as 30 to 40 million tons per year [18]. Therefore, in order to gain a deeper understanding of the mechanism and influencing factors of soil erosion, simulation experiments of soil internal erosion were carried out. In order to simulate the properties and behaviors of real soils, it is crucial to select suitable experimental materials. As a widely distributed soil type in the Xiaojiang River Basin, laterite has a representative and important engineering application background. The study of laterite soil in slope engineering, soil conservation, and soil and water conservation is of great value. Secondly, lateritic soils have high erodibility in the Xiaojiang watershed and are susceptible to rainfall scouring and internal erosion, as shown in Figure 1. And the particle composition and pore structure characteristics have an important influence on the internal erosion process. Therefore, choosing laterite as the experimental material can better simulate the mechanism and influencing factors of soil internal erosion in order to explore the migration of fine particles under rainfall conditions. This will help to reveal the mechanism and influencing factors of internal erosion in laterite soils, as well as the relationship with slope stability, and provide assistance in erosion control and debris flow initiation assessment in the area.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 2 State of the Art
### Convex and Mixed Integer Optimization in Motion Planning  
The use of mixed-integer formulations in path planning is not new; it has been done in ariel, underwater, and walking robotics, to name a few examples [42]. Convex solvers are much faster than general non-linear optimization algorithms; however, casting a path-planning problem as convex severely restricts the possible scenarios the planner can tackle. The use of mixed-integer variables here lifts some of these restrictions, mostnotably restrictions on the convexity of the obstacle-free area, but also restrictions on the types of movements the robot can make. Thus, in [43], the authors developed a mixed-integer footstep planning method, eliminating the problem of non-convex rotation-related constraints by replacing them with mixed-integer piece-wise linear ones.  
Additionally, a sequence of convex optimizations can be used to generate free space segmentation into large overlapping obstacle-free convex regions. In [44], the free space segmentation algorithm IRIS (Iterative Regional Inflation by Semi-definite Programming) was proposed; it represented a new approach for calculating large polytopic and ellipsoidal regions of obstacle-free space efficiently via a sequence of convex optimizations.  
Here, mixed-integer optimization is adopted for simultaneous path planning and deformation planning of a tensegrity drone, which has not been previously reported in the literature.
## 5 Conclusions  
In this paper, an optical bar code (OBC) detection and recognition method based on visible light communication using machine learning is proposed as a complement to QR code technology. To provide a high robustness, widely used LED-ID identification scheme and a huge number of unique LED-ID, we translated the problem into an OBC recognition problem in the machine learning field using RGB-LEDs as the transmitters. A variable frequency, duty-ratio, and phase difference PWM method was employed to create different features for different LEDs. Therefore, in the feature space, the features of different LED-IDs are linearly separable. Once the proposed image processing method realized the feature extraction and selection of LED-IDs, an ANN or SVM could be used to identify LED-IDs.  
As the experiment result shows, the proposed scheme could offer roughly 1.05 \(\times\) 10\({}^{7}\) unique LED-IDs with a greater than 95% LED-ID recognition rate. Moreover, the number can be more by using LEDs of different shapes and combining shape recognition. The effective working distance of the system could reach up to 6 m, which is significantly farther than QR code. Furthermore, with the upgrade of the camera technology, the proposed method would offer more unique LED-ID and the maximum distance between the transmitter and receiver would also increase. Therefore, the OBC can be applied as a complement to QR code technology.  
**Author Contributions:** Conceptualization, G.W.; Data curation, L.J.; Investigation, L.J.; Software, L.J.; Writing--original draft, L.J.  
**Funding:** This work supported by National Undergraduate Innovative and Entrepreneurial Training Program (No. 201810561195, No. 201810561218, No. 201810561219, No. 201810561217), Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (\"Climbing Program\" Special Funds) (pdjh2017b0040, pdjha0028), Guangdong science and technology project (2017B010114001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Time-Synchronization Algorithm of Heterogeneous Networks
### Time-Synchronization Method of the CAN Network  
The PTP procedure can be directly applicable to an homogeneous CAN network. Unlike Ethernet, which uses a 1:1 connection, a CAN network employs bus topology where broadcast transmission is readily available [20]. This feature can be used to synchronize all the CAN nodes without repeating PTP procedure for each node. We can design the procedure so that a master node can transmit synchronization and follow-up messages only once. However, slave nodes will try to transmit a delay-request message almost simultaneously, which will cause message collisions. To prevent these collisions, we may assign different IDs for each delay-request message, which will increase the duration  
Figure 4: Synchronization algorithm of the master and slave nodes in the CAN–Ethernet network: (**a**) master node (Ethernet), (**b**) slave node (CAN), and (**c**) gateway.  
of the synchronization process and the network traffic. Instead, we selected a single slave node to perform the delay calculation using Equation (1) and let the node broadcast the results to other nodes. Figure 5 shows the method for sharing the delay between slave nodes. First, the master transmits synchronization and follow-up messages to all slaves, and slave 1 stores t\({}_{1}\) and t\({}_{2}\). Slaves 2 and 3 share t\({}_{1}\), but because the reception time differs, they store t\({}_{2}\)* and t\({}_{2}\)**, respectively. Next, the time synchronization procedure is performed for only slave 1 to calculate the delay. The calculated delay is transmitted to the remaining slaves, and the previously-stored timestamps (t\({}_{1}\) and t\({}_{2}\)*, or t\({}_{1}\) and t\({}_{2}\)**) are used to calculate the offset using Equation (2). This modified procedure can simplify the synchronization over the broadcast medium although synchronization error may increase as the length of network increases.