esa-sceva/satcom-chunk-collection huggingface dataset:
# Petri Net Toolbox for Multi-Robot Planning under Uncertainty
## 1 Introduction
It allows coordination of single and multi-robot systems through task plans designed as Petri nets. It is integrated with ROSPlan [19] and executes the task plans using actionlib. However, it is less scalable as it uses one Petri net model per represented robot. It also does not provide methods to capture probabilistic behavior, or supports policy synthesis and, consequently, requires the input of a task plan. Our work addresses these gaps and proposes a unified toolbox to implement multi-robot coordination problems. This toolbox allows us to capture multi-robot systems as a generalized stochastic Petri net with rewards (GSPNR) [20], to synthesize optimal control policies that optimize team-level objectives, and provides methods to execute the obtained task plans in real robots. GSPNRs allow for unified modeling of action selection, uncertainty on the duration of action execution, and for goal specification through the use of transition rewards and rewards per time unit. When compared against traditional multi-robot formalisms, such as multi-agent Markov decision processes (MMDPs) [21], GSPNRs provide: better scalability and fully asynchronous execution, ensuring a smaller state-space and a more fluid policy execution. This work introduces also a novel algorithm to build GSPNR models from a topological map. This is an intuitive and quick method to design complex models especially for users that are not comfortable with manipulating GSPNRs. It also provides a novel execution algorithm that is able to coordinate robots through ROS. GSPNRs for multi-robot systems is an efficient representation since it models robots as indistinguishable tokens. The proposed execution algorithm is able to track each individual robot, and distinguish between them without compromising the state space efficiency. We provide a rigorous characterization of the toolbox, namely the algorithms' complexity and the computational resources required as the modeled system increases in complexity. Finally, we address a solar farm inspection problem where the capabilities of the toolbox are showcased. When solving this problem we introduce an extension to the work in [20] to enable modeling heterogeneous multi-robot systems and system resources such as battery levels and counters.
## 3 Concept
### Propulsion
#### 3.3.4 Propulsion Configuration  
The positioning of the propulsion system also determines the overall concept.  
A sword configuration, where the armature windings are positioned like a fin on the ground and the excitation system can act from both sides, often comes with a good efficiency but relatively high costs on the track side. Additionally, this would mean there is no propulsion during a horizontal lane switch and that one lane loses propulsion during a vertical lane switch.  
According to [13], it would also be feasible to combine the propulsion system with a superconducting levitation system, such as excitation. However, a separated homopolar system leads to less plating in the levitation reaction plate and, therefore, to a cost reduction on the track side. Also, the separation eliminates the dependency between suspending and accelerating forces, posing a limit on the feasible acceleration. Additionally, in a separate system, there are no groves in the reaction plate, leading to force ripples and vibrations in the system. Furthermore, independent testing of the systems is enabled, which was especially valuable during the prototyping phase of the Demonstrator. However, it has to be acknowledged that an ironless bottom flat design necessitates a second strong excitation system onboard, which is a notable drawback in terms of onboard mass and power consumption.  
In the case of a superconducting excited linear synchronous motor and an electromagnetic suspension in double C configuration, the armature can be positioned centered on the floor. Manufacturing is cost-effective and straightforward in this case. Because of the strong magnetic field, there must not be any ferromagnetic material underneath the excitation, to prevent forces counteracting the levitation. In a vertical lane switch, one switching direction loses propulsion during the switch, which can be counteracted by placing one propulsion system on each side. In this case, it must be distinguished between a propulsion system with laminated iron, which is expensive on the track side, or an air cored system, which is less expensive on the track side but requires extensive excitation.
## 2 Related Works
The accuracy obtained on the datasets was 88.2%, 81%, 96.16%, 64.1%, and 56.73% on five benchmarking datasets (CCAT10, CCAT50, IMDB62, Blogs10, and Blogs50, respectively). The authors of [29] combined widely known features of texts (verbs tenses frequency, verbs frequency in a sentence, verbs usage frequency, commas frequency in a sentence, sentence length frequency, words usage frequency, words length frequency, characters \(n\)-gram frequency) and genetic algorithm to find the optimal weight distribution. The genetic algorithm is configured with a mutation probability of 0.2 using a Gaussian convolution on the values with a standard deviation of 0.3 and evolved over 1000 generations. The method was tested on the Gutenberg Dataset, consisting of 3036 texts written by 142 authors. The method is implemented using Stanford CoreNLP, stemming, PoS tagging, and genetic algorithm. The obtained accuracy was 86.8%. There is no generally accepted opinion regarding the set of text features that provides the best result. In most works, text features such as bigrams and trigrams of symbols and words, functional words, the most frequent words in the language, the distribution of words in parts of speech, punctuation marks, and the distribution of word length and sentence length have proven to be effective. It is incorrect to judge the accuracy of the methods applied to the Russian language based on the results of research in the English language or any other languages because of the specific structure of each language. The choice of approach depends on the text language, the authorship identification method, and the accuracy of the available analysis methods. Particularly, the peculiarity of the Russian language in comparison with English, for which most of the results are presented, is its flexibility and, consequently, more complex word formation and a high degree of morphological and syntactic homonymy, which makes it difficult to use some features useful for the English language. The problems of genre, sample representativeness, and dataset size also limit the implementation of some approaches. Investigations aimed at finding a method with high separating ability with a large number of possible authors are not always useful when solving real-life tasks. It is necessary to continue further research aimed at finding new methods or improving/combining existing methods of identifying the author, as well as conducting experiments aimed at finding features that allow accurately dividing the styles of authors of Russian-language texts. By using these features, it will be possible to work with small samples.
# Image Segmentation by Searching for Image Feature Density Peaks
## 2 Materials and Methods
### CIELab Color Space for Image Feature Description  
In the conventional color representation space, such as \(RGB\) color space and \(CMYK\) color space, their channels contain not only color information but also luminosity information, which can not be extracted separately. In CIELab space, because of its unique channel settings, luminosity features are stored in \(L\) channels alone, and color features are stored in \(a\) and \(b\) channels. They are independent of each other. Therefore, any operation to the image in the \(Lab\) color space will not affect the hue. If luminosity and color features need to be extracted and adjusted, \(Lab\) space will facilitate the operation. In addition, \(Lab\) color space has a large range than \(RGB\) space, which means that color information described in \(RGB\) space can also be mapped in \(Lab\) space, and it can make up for color distribution inhomogeneity in the other color space.  
In this method, we choose the CIELab color space, which consists of three channels for describing colors visible to human beings [28]. A method based on rough set for color channel selection proposed by [PERSON] et al. [14] provides ideas for the choice of color space in our work. Thus, in image segmentation, even only choose one color channel to analyze can we accomplish our task  
Figure 1: Overview of our image segmentation framework.  
satisfactorily either. It also contributes to reducing the computation amount of the algorithm. After the superpixel segmentation is finished, we will use the planar space composed of \(L\) channel and \(a\) channel as the image feature space. With the distribution of sample points in the feature space, cluster analysis can be performed. More specifically, we take a fraction of Figure 2a and take it as an example, the above process is intuitively illustrated in Figure 3. Corresponding to the example of Figure 2, all the superpixels of this picture can be viewed as clustering sample points, and their distribution in the feature space is shown in Figure 4b.  
Figure 3: The flowchart of Sections 2.1 and 2.2. The small image is an interesting region selected from the original image, and this interesting region is divided into 62 superpixels. We consider each superpixel as a sample point and represent them in feature space.  
Figure 2: The process of superpixel pre-segmentation. (**a**) The original image. (**b**) We partition the original image into multiple homogeneous. (**c**) The superpixel level map. Calculating the average of the color features for each superpixel region and use that value to replace the pixel values for all points in the region. In this process, not only can maximize the retention of the effective information but can drop some unnecessary noise, the complexity of the calculation will be greatly reduced.
###### Abstract
This confluence, along with factors like circulation and water mass mixing, dictates the region's biological and chemical processes [16; 17]. In particular, the mixture and the upwelling of nutrients determine the characteristics of the ecosystem [14; 16; 17; 18]. Furthermore, interannual variations such as El Nino-Southern Oscillation (ENSO) strongly influence this intricate transitional ecosystem. Our study assesses the efficacy of two global algorithms (OCx, CI) and a CCS-specific algorithm [9] using in situ Chlorophyll-\(a\) measurements off the Baja California Peninsula, as well as Copernicus merged satellite data. The significance of this evaluation stems from the fact that the performance of Chl-\(a\) algorithms for Copernicus data in this region remains untested. Copernicus, with its merged sensor data, offers enhanced spatial and temporal coverage, enabling a deeper dive into regional processes; for example, the longer-term trends modulating atmosphere-ocean interactions in Eastern Boundary Upwelling Systems triggered by decadal-scale fluctuations linked to climate forcing [19]. Post evaluation, we propose and test a regionalization tailored for this area and satellite dataset. To refine our understanding, we segmented the dataset based on hydrographic properties, bio-optical properties, and climatic events like ENSO. For each segment, specific algorithms were introduced and evaluated. Our findings facilitate the selection of the most representative model for this region using the Copernicus dataset, shedding light on the region's distinct bio-optical properties. A meticulously analyzed Chl-\(a\) retrieval from a consistent dataset across instruments will bolster our understanding of biogeochemical processes and the broader implications of climate change.
# A Review of Oil Spill Remote Sensing
## 4 Slick Thickness Measurements
### Passive Microwave
Passive microwave has long been used as an indicator of oil slick thickness. The microwave brightness of the slick varies with the thickness, but in a cyclical fashion. This means that a particular microwave brightness implies one of several thicknesses [57]. One method to overcome this is to use multiple wave lengths. This will reduce ambiguity to a useable degree. One should note, however, that the microwave brightness is also influenced by a number of other factors such as weather and sea conditions and type of oil. Another important factor one should note, is that in practice, the microwave response requires calibration to determine the response factors. The best way to do this is to use simulants such as plastic sheets and integrate volume back to the amount released. Once one instrument is \"calibrated\" in this manner, it is useable for field work.  
In the 1970s, [PERSON] and [PERSON] studied a series of small test slicks off the coast of New Jersey [58]. Using a two-frequency microwave receiver, they plotted the thickness of the slicks. They found that 90% of this fresh slick volume was contained in 10% of the area and vice versa, this number was subsequently found to vary from slick to slick and could not be used as a \"rule of thumb\" [57]. Microwave thickness measurement technology continued to evolve for the next 15 years. [PERSON] et al. reviewed microwave sensing extensively and carried out experiments to measure oil thicknesses using a three-frequency radiometer (5, 17, and 34 GHz) [59]. It was shown that an accurate thickness profile could be drawn, given major adjustments.  
This highlighted the problem with early devices; the early selection of frequencies and calibration methodology did not allow for the measurement of slicks thicker than about 1 mm.  
Figure 11: A slick thickness profile from a passive microwave sensor (photo from [PERSON], Optimare). The colors represent thicknesses as shown on left hand scale (thickness ranges from 0.2 to 1.2 mm in this case). This image was taken directly from an operating aircraft display. The information available on the display includes location, area of slick (here 0.008 km\({}^{2}\)), and oil volume (here 1897 L). The microwave instrument was a 3-band passive microwave with receivers at 18, 36, and 89 GHz.  
In the next decade, microwave sensor technology developed to the point of commercialization. The only instruments currently available for measuring slick thickness are the Optimare three- to five-channel microwave instruments [60]. These instruments have the capability of measuring from 0.05 to 3 mm. A sample slick output from this sensor is shown in Figure 11. There is little public data available, as most of the existing measurements are used for legal prosecution purposes, but these instruments are extensively used. Questions remain, however, on the applicability of this technology to oils containing water, such as emulsions.
## 3 Design of Hardware and Software for the Data Monitoring System
### System Testing  
The developed software has been monitoring for a period of 11 days, with a monitoring frequency of one datum per minute, obtaining a total of 15,840 datapoints per sensor (60 data/h \(\times\) 24 h/day \(\times\) 11 days). Furthermore, commercial data-loggers recorded data every 10 min (for the control of the developed system). The trajectories of the three sensors are shown in Figure 11.  
The mean daily trajectory of data recorded inside the display cabinet and outside of it, as well as data recorded by the commercial data-loggers have been plotted to assess the results obtained by our monitoring system (Figure 12).  
Figure 11: Trajectories of sensor inside the display cabinet (red), sensor at the outside of the display cabinet (blue) and control data-logger at the outside of the display cabinet (green). (**a**) Temperature; (**b**) RH.  
Figure 10: Global communication example.  
Each RH measured by our system out of the display cabinet differs 1.09% on average from the control data-logger. For the temperature the difference is 0.46%. In absolute terms, this mean difference is 3.72% RH and 0.54 \({}^{\circ}\)C for temperature. These values are within the error range of the sensors (\(\pm\)5% RH and \(\pm\)0.5 \({}^{\circ}\)C).
## 3 Data Analysis
### Discharge Rate  
To study the effect of the discharge rate, we differentiated between two types of flight, training, and competition, the first (65 A average current, approximately) less demanding than the second (80 A approximately). This parameter is very important for the study of  
Figure 16: Average storage time vs. internal resistance growth.  
Figure 15: Average storage time vs. cycle life.  
the behavior of the batteries, since high discharging rates can cause a decrease and loss of capacity, and therefore a shorter use life [20; 21].  
An attempt was made to find a relationship between a greater dispersion of voltage between cells coinciding with the competition flights, since it seems logical to think that with a higher discharge rate the battery will be more stressed, and malfunctions may occur that cause bad balancing between cells. If we compare the average voltage dispersion between the two types of flight, we observe how in the competition flights the value is slightly higher (0.038 V > 0.035 V). However, this trend is not sufficiently appreciable to affirm this phenomenon, although it is not out of the question either.  
Regarding the relationship with internal resistance, there is no evidence that high discharge rates cause instantaneous peaks in resistance. Observing the evolution of both parameters, this phenomenon does not occur. To study how this parameter affects aging, a comparative analysis is performed between all batteries. In this case, we will use the total percentage of competition flights performed as a measure of the average discharge rate.  
Figures 17 and 18 show the ratio of the average discharge rate of each battery compared to the total number of cycles and the growth of internal resistance. It is observed how a higher rate of discharge tends to cause a shortening in cyclical life, as well as a greater increase in internal resistance. This is consistent with the theory explained above [21]. In addition, it was observed how high discharge rates in the first uses of the battery led to a more marked deterioration of performance for the rest of the battery's life. This may be because these initial surges prevent the battery from stabilizing properly in the first few cycles.
# Ecological Suitability Evaluation for Conservation and Development in Bac Kan Province, Vietnam
## 2 Materials and Methods
### Ecological dynamics resistance assessment
#### 2.2.3 Development Zonation and Direction for Sustainable Use of Natural Resources  
The ecological suitability classification for construction and development is created based on basis of landscape ecology; it is necessary to integrate the derived ecological suitability result with the related regulations of the government in order to meet the specific condition of the province for ensuring the sustainable use of natural resources in the future. According to the construction law of Vietnam [89] and the law on the cultural heritage of Vietnam [90], the new construction and development in the protected areas including the national park, nature reserve, and species and habitat conservation areas are prohibited.  
In this study, the spatial analysis was applied to assign the development zone at the provincial level while the ratio between development and ecological protection areas with the trade-off technique was used to propose a future direction for sustainable use of natural resources at the district level in Bac Kan province.  
For development zonation at provincial level, the important ecological function area in Bac Kan province (Ba Be National Park, Kim Hy Nature Reserve, and Nam Xuan Lac Species and Habitat Conservation Areas) was superimposed on the ecological suitability classification map to quantify their relationships using a spatial analysis tool of ESRI ArcMap software, and the derived result was applied to define development zone into 5 categories: forbidden development zone, restricted development zone, low priority development zone, moderate priority development zone, and high priority development zone.  
To propose a future direction for the sustainable use of natural resources at the district level, it is necessary to integrate development zonation with current administrative units. In this study, the moderate and high priority development zone were merged as a development area, whereas the forbidden and restricted development zone were grouped as ecological protection area. Herein, the low priority development zone was not merged into the development area or ecological protection area since this zone cannot be clearly assigned as a development area or ecological protection area. By using the trade-off technique, the percentage of development and ecological protection areas in each district was extracted, and they were used to calculate a ratio for assigning the future direction for sustainable use of natural resources of 8 districts into three categories: prior areas for conservation, comprehensive development areas, and prior areas for development.
## 3 Results and Discussions
### ATR-FTIR Analysis  
The ATR-FTIR spectra exhibited a consistent pattern across all the studied soil samples, with slight variations in intensity, as shown in Figure 3.  
The results revealed specific absorption bands that can be attributed to characteristic functional groups associated with clay and non-clay minerals [33]. In the spectral fingerprint domain, peaks in the 1600-400 cm\({}^{-1}\) range are observed, which are associated with clay minerals such as montmorillonite ((Na, Ca)\({}_{0.33}\)(Al,Mg)\({}_{2}\)(Si\({}_{4}\)O\({}_{10}\))(OH)\({}_{2}\)NH\({}_{2}\)O) and kaolinite (Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)) as well as non-clay minerals such as quartz (SiO\({}_{2}\)), feldspars (KAlSi\({}_{3}\)O\({}_{8}\)), albite (NaAlSi\({}_{3}\)O\({}_{8}\)), dolomite (CaMg(CO\({}_{3}\))\({}_{2}\)), gypsum (CaSO\({}_{4}\))2H\({}_{2}\)O) and calcite (CaCO\({}_{3}\)). These results are similar to those obtained by [33] on soils collected from the  
Figure 3: ATR-FTIR spectra of playground (PS) and edge of park (ES) soil samples.  
agricultural area around the Galati steel enterprise, highlighting the mineralogical footprint of the region.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 3 Results and Discussion
### Experiments of Automatic Irrigation System
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indexes for Single-Seedling** & **Lower Left** & **Lower Right** & **Central** & **Upper Right** & **Upper Left** & **Total** \\ \hline \# of watering to stem-root junction & 6 & 7 & 5 & 5 & 6 & 29 \\ \# of watering to stem-leaf junction & 2 & 3 & 3 & 2 & 2 & 12 \\ \# of improper watering position & 2 & 0 & 2 & 3 & 2 & 9 \\ Successful watering (\%) & 80 & 100 & 80 & 70 & 80 & 82 \\ Perfect watering (\%) & 75 & 70 & 62.5 & 71.4 & 75 & 70.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of automatic irrigation experiments with the same plant but different position of seedling. Figure 34: The configuration of the multiple-seedling experiments. Figure 33: The pot holder for the multiple-seedling experiments. From Table 3, it can be found that there exists a ratio of 30% for no occurrence of watering, which could be the following two reasons. Firstly, the measurement errors of the depth map may be caused by insufficient environmental lighting onwards the feature points. Moreover, the watering process might be cancelled due to wrongly computed moving distance to the watering point for the nozzle, which thus moved too far to stay within the working boundary.