esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Proof of Theorem 2  
In the case of TAS with silencing scheme, we can rewrite (5) as  
\[F_{\text{SIR}_{\text{ TAS}}}(\theta)=\mathbb{P}\Bigg{(}\frac{\Omega}{\sum\limits_{j=k+1}^{N}h_{j}d^{- \alpha}}<\theta\Bigg{)},\] (A3)  
where,  
\[\Omega=\text{max}(h_{0}d_{0}^{-\alpha},(h_{1}.....h_{k})d^{-\alpha}).\] (A4)  
Note that in (A4), \(k\) is the number of RRHs in cooperation, and we take the maximum between typical and interfering RRHs in cooperation. Now from (A3) and (A4) we have that  
\[F_{\text{SIR}_{\text{ TAS}}}(\theta)=\mathbb{P}\Bigg{(}\frac{\text{max}(h_{0}d_{0}^{- \alpha},(h_{1}.....h_{k})d^{-\alpha}}{Zd^{-\alpha}}<\theta\Bigg{)}.\] (A5)  
Let \(Z=\sum\limits_{j=k+1}^{N}h_{j}\) be a gamma distributed RV such that \(Z\sim\Gamma(k,N)\). Then we can rewrite (A5) conditioned on \(Z\) as  
\[F_{\text{SIR}_{\text{ TAS}}|Z}(\theta)=\mathbb{P}\bigg{(}\max(h_{0}d_{0}^{- \alpha},(h_{1},\cdots,h_{k}))d^{-\alpha}<\theta Zd^{-\alpha}|Z\bigg{)},\] (A6)  
From the definition of CDF and higher order statistics [50] we rewrite (A6) as  
\[F_{\text{SIR}_{\text{ TAS}}|Z}(\theta)=(1-\exp(-\theta Z\delta^{\alpha})(1-\text{e}^{-\theta Z})^{k}.\] (A7)  
Then, de-conditioning (A7), we have  
\[F_{\text{SIR}_{\text{ TAS}}}(\theta)=\int\limits_{0}^{\infty}(1-\exp(-\theta Z \delta^{\alpha}))\underbrace{(1-\text{e}^{-\theta Z})^{k}}_{\text{(a)}}\text{ f}_{\text{Z}}(\text{z})\text{d}Z.\] (A8)By using binomial Theorem, \((1+x)^{n}=\sum\limits_{k=0}^{n}{(\frac{n}{k})x^{k}}\) to expand term \((a)\) in (A8) and substituting the PDF of Z, which is gamma distributed as \(\mathrm{f}_{\mathrm{Z}}(\mathrm{z})=\frac{\mathrm{Z}^{(N-k-1)}\mathrm{e}^{- \mathrm{Z}}}{(N-1)!}\) we have that,  
\[F_{\mathrm{SIR}_{\mathrm{TAS}}}(\theta)=\sum\limits_{t=0}^{k}{k\choose t}\int \limits_{0}^{\infty}{\big{(}1-\exp(-\theta Z^{\alpha})\big{)}(-1)^{t}\times( \mathrm{e}^{-\theta Z})^{t}\frac{\mathrm{Z}^{N-k-1}\mathrm{e}^{-\mathrm{Z}}}{( N-k-1)!}\mathrm{d}Z}.\] (A9)  
Thus, simplifying and substituting \(x=\mathrm{Z}(1+\theta t)\) and \(y=\mathrm{Z}(1+\theta t+\theta\delta^{\alpha})\) and setting limits as \(x,y\to\)0 when \(\mathrm{Z}\to 0\) and \(x,y\to\infty\) when \(\mathrm{Z}\to\infty\) respectively into (A9), we have  
\[\begin{array}{ll}F_{\mathrm{SIR}_{\mathrm{TAS}}}(\theta)&=\sum\limits_{t=0}^{ k}{(\frac{k}{t})\frac{1}{(N-k-1)!}(-1)^{t}\Bigg{(}(1+\theta t)^{k-N}\times\int \limits_{0}^{\infty}{\mathrm{e}^{-x}x^{N-k-1}dx}-\\ &(1+\theta t+\theta\delta^{\alpha})^{k-N}\times\int\limits_{0}^{\infty}{ \mathrm{e}^{-y}y^{N-k-1}dy}\Bigg{)}.\end{array}\] (A10)  
Simplifying the gamma function in (A10), we can achieve our closed form solution for TAS with silencing scheme concluding the proof as  
\[F_{\mathrm{SIR}_{\mathrm{TAS}}}(\theta)=\sum\limits_{t=0}^{k}{k\choose t}(-1)^{ t}\Bigg{(}(1+\theta t)^{k-N}-\Big{(}1+\theta t+\theta\delta^{\alpha}\Big{)}^{k-N} \Bigg{)}.\] (A11)
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 3 Generating Density Maps from Raw AIS Data
### Creating the Grid  
In order to generate density maps, the area of interest needs to be partitioned into square cells of equal size, creating a corresponding grid. Each grid is characterized by the edge length of its cells (in meters), with grids of smaller cells resulting in more accurate visualizations (see Figure 3).  
For the process of creating such grids, it is important to select the appropriate projection system for the areas in question. As an example, for the European seas, the ETRS89 European Terrestrial Reference System [56] may be used. Although this step allows for an easier navigation through the data and area partition, an additional step of removing all cells that include solely land areas further improves the performance of our approach. While this can be applied in all resolutions, its use is more apparent in more detailed maps (see Figure 4).
## 3 Discussion of the Experimental Results
An additional factor affecting the results obtained in the video feedback loop is the presence of fish; however, considering the fact that most of the time the fish do not move significantly, it is possible to detect and filter out the detected sudden movements of the fish. As verified experimentally by some other researchers [25; 27; 28], the presence of fish may decrease the water velocity by 25-30% in comparison to the tank without fish, affecting the flow pattern. However, it has been confirmed that trends of mean velocity in the radial direction remain unchanged [25]. Therefore, as verified for some video sequences acquired in the presence of fish, the method proposed in the paper also leads to similar conclusions and results in the presence of fish biomass, assuming the necessary calibration, taking into account the expected velocity decrease. Nevertheless, it is also worth noting that a typical water flow used in fish farming for such types of tanks does not exceed 3.7 dm\({}^{3}\)/s; hence, the proposed method may be successfully applied for the typical velocity range from 1.5 to about 3.7 dm\({}^{3}\)/s, ensuring the Pearson linear correlation coefficient equal to 0.9868. For further verification of the universality of the proposed solution in fish-farming tanks, additional experiments were performed after recording several hours of video sequences using five water-flow velocities in the presence of fish, as illustrated in Figure 3. However, the proposed method requires the additional calibration in the presence of fish and some changes in the parameters, also using a different camera location. In such cases, very good experimental results were also obtained applying the fourth root of the entropy of the motion vectors map for the Lucas-Kanade method (\(Ent_{LK}^{0.25}\)) instead of the squared entropy of the Farneback motion vectors map (with \(A=30\) and \(B=-55.5\)). Additionally, in this case, there is no necessity to use the Farneback method at all, since all three features originate from the Lucas-Kanade method. Therefore, an additional advantage of this simplified approach is the decrease in the number of computations, preserving relatively high correlation with measurement results (0.9847 in the experiments illustrated in Figure 7). Similarly, considering the four measurements obtained for the water flows from 2.35 to 4.01 dm\({}^{3}\)/s, a high linearity of the obtained relationship may be noticed as well. Figure 6: The relationships between the uncalibrated estimates achieved using the proposed method and the velocities measured using the water-flow meter: (**a**) for the whole range 1.5–5.0 dm\({}^{3}\)/s; (**b**) for the typical range 1.5–3.7 dm\({}^{3}\)/s.
# Ethanol Production from Waste of Cassava Processing
## 1 Introduction  
Cassava bagasse is an inexpensive and broadly available waste by-product from cassava starch production. It contains roughly 50% cassava starch along with mostly fiber and could be a valuable feedstock for various biproducts [1]. Cassava pulp or bagasse is a byproduct of starch production.  
For each 250-300 tons of cassava root processed, about 280 tons of bagasse with 85% moisture are produced [2]. Currently, the majority of the cassava bagasse is discarded as waste; however, this residue can be considered as an excellent source of fermentation for higher--value biochemicals [3], as is the case with second generation ethanol.  
Second generation ethanol production is a strategic alternative to increasing ethanol production with environmental and economic advantages. Because of this, there is currently great interest in developing technologies for obtaining cellulosic ethanol (second generation) from the conversion of cellulose and hemicellulose of the bagasse [4] Great effort has been employed on the study of the enzymatic hydrolysis of sugarcane bagasse and many different pre-treatment methods were studied to enhance itsbiodigestibility by enzymes [5].  
The use of lignocellulosic materials as feedstock for second generation bioethanol production has not yet become an industrial reality due to the lack of efficient and low cost technologies, which results in prohibitive investment and production costs as well as poor returns on investment [6].  
Increased production and use of ethanol with emphasis on advanced biofuels participation (such as cellulosic ethanol, also known as second-generation or 2G) is a commitment recognized by manycountries in the world, including the Brazilian Government in its Intended Nationally Determined Contribution (INDC) to United Nations Conference on Climate Change--COP 21 [7].  
Therefore, potential environmental benefits of different alternatives for 2G ethanol production should be properly quantified for supporting public policy formulation that will affect the future of climate and other environmental aspects of our planet [8].  
The results obtained from the test were applied to verify the production potential in Brazil. According to [9] each ton of manioc processed about 930 kg of manioc bagasse was produced, so it was possible to obtain a survey of the Brazilian Ministry of Agriculture and Supply related to cassava production between 2017 and 2018 to estimate the second-generation ethanol production potential from cassava bagasse. Subsequently, this value was compared to the data expressed by EPE--Energy Research Company (2018), where they present the volume of ethanol produced in 2017.  
In this context, the objective of this study was to analyze ethanol production from cassava processing residues and to evaluate its impact on the Brazilian energy matrix, replacing first generation biofuels.
# An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem
## 4 Computational Experiments
### Compare the Results with Other Algorithms

Table 5: Comparison of results in least-waste-first heuristic algorithm (LWF), hybrid simulated annealing algorithm (HSA), iterative bidirectional heuristic placement (IBHP), and hybrid adaptive genetic algorithm (HAGA). However, the average filling rate of HAGA on the N1-N12, gcut1-gcut13, and beng1-beng10 instances is worse than LWF and IBHP. For example, the average filling rate of HAGA is only 0.23%, 0.0252%, and 0.0897% lower on these instances for IBHP, respectively. Overall, the HAGA is relatively superior to these three algorithms within a reasonable duration of computational time, especially on the C11-C73, cgcut1-cgcut3, and ngcut1-ngcut12 instances. In order to observe the performance of different changes of the proposed algorithm, we just run the proposed algorithm on the N1-N12 instances. We first set \(c=0.1\) and perform the experiments with \(\lambda=\{0,0.3,0.6,0.9,1\}\). For each \(\lambda\), the calculation result is the average of twenty operations for each instance. Figure 8 with the \(y\)-axis representing the average filling rate reports the results of different \(\lambda\). From Figure 8, we can observe that \(\lambda=0.6\) is better. Now, we fix \(\lambda=0.6\) and perform the experiments with \(c=\{0.05,0.1,0.2,0.4,0.8,1.6\}\). Similarly, Figure 9 shows the computational results of different \(c\). From Figure 9, we can observe that \(c=0.2\) is better. Furthermore, from Figures 8 and 9, it can be seen that the change of parameters has a greater impact on the performance of HAGA as the scale of the instance increases. However, the parameter changes for different problems may achieve better results. In addition, if \(\lambda=0\), the algorithm just changes to AGA [26] from the local perspective of the individual fitness; if \(\lambda=1\), the algorithm happens to be an adaptive genetic algorithm that changes from the overall perspective of the population evolution. We can see that the proposed algorithm, HAGA, has better performance. According to the results of parameter changes, we can establish a consistent parameter setting for HAGA to obtain good benefits. Figure 8: The effect of HAGA parameter \(\lambda\). Figure 9: The effect of HAGA parameter \(c\).
## 1 Introduction
This type of sensor does not measure directly from the medium, but a sample has to be taken for measurement. [PERSON] et al. [12] developed a device to determine sucrose and phosphate content, in cola drinks, using a sequential injection system with a low-cost paired emitter-detector diode by mean of the schlieren effect and turbidity. This method is readily applicable for automation and is found to be an alternative method to conventional procedures for online quality control processes in the cola-drink industry, but the method is not able to measure concentrations. A sensor-based on hetero-core structured fiber optics would be able to measure the degree Brix of beverages correlating the peak wavelength and sucrose concentration [13]. The optical fiber is illuminated with white light and the peak of loss of every wavelength is correlated with the concentration. This sensor is only able to measure sucrose concentrations from 0-23.467%. With the current processes, we are unable to automate the entire system, the extraction of the sample is essential, then the analysis, and in view of the results, add, extract or do nothing. If the determination of the concentration of sucrose were continuous, i.e., the determination of the concentration of sucrose anywhere in the tank and in real-time, the system would be able to give the order to add sugar or not to the dissolution and completely automate the process in the production line of the soft drink. In view of all the limitations of the sensors described above, we propose an SPR sensor to measure the concentration of sucrose for a wide range of concentrations. In this research, we have designed two surface plasmon resonance (SPR) sensors based on intensity interrogation for measuring refractive indexes. We have used the Kretschmann configuration [14].
## 1 Introduction
Their evaluation results have demonstrated that the automatically extracted parallel data obtain significant translation quality improvements. [PERSON] et al. have designed UM-Corpus as a multi-domain and balanced parallel corpus [23]. It is a two million English-Chinese aligned corpus from eight different text domains, including Education, Laws, Microblog, News, Science, Spoken, Subtitles, and Thesis. Although using a domain-specific corpus for model training has yielded promising results, we believe that there is a lack of a bilingual corpus from domain experts. Therefore, determining how to leverage domain expertise and build a new corpus is largely required. In this work, we present an easy-to-use deep learning machine translation system, built from the scratch, based on a domain corpus. The deep learning algorithm takes in English text as input and uses an encoder-decoder model with an attention mechanism based on Google's Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on paired English and Chinese text sentences from the news area of the UM-Corpus. The parallel training process of our model can be performed on common laptops, desktops, and servers with one or more GPUs. During training time, we not only track loss over training epochs but also measure the quality of our model's translations using the BLEU score (see Section 2.1.5). The experimental results on the UM-corpus show that our trained model can achieve a maximum BLEU score of 29.2. We can further improve this score by tuning other hyperparameters. We provide a web interface for users to build a domain-specific corpus and configure training parameters. We also observe that training the model on high-end GPUs is much faster than on a multi-core CPU, and thus the GPU is a very promising training platform for NMT. We conclude that our translation system is platform-portable, which is suitable for teaching purposes and use scenarios in small/medium-sized enterprises. As a case study, we compare the performance of our model to that of Baidu's and show that our model can compete with the production-level translation system. To summarize, our contributions are as follows:  
* We present a transformer-based machine translation system, which is built from scratch, based on a domain corpus. * Our translation system is easy to use with a web interface, so that domain experts can extend the existing corpus and train the model in a fine-tuned manner. * Our machine learning system is both portable and configurable and can be deployed on laptops, desktops, or servers with multiple GPUs. * Our translation system trained based on a domain corpus can achieve competing performance with a production-level translation system.
## 5 Conclusions and Future Developments  
The experimental activity allowed assessing the effect of spray pressure on drop pulverization in three Albuz nozzle types (ATR 80 orange, AVI 11003, TVI 8002). The research, aimed at describing the experimental data and not the theoretical atomization process, as is common practice in the studies of nozzles for pesticide applications, produced results, especially in terms of volume median diameter, consistent with the expectations, with the nozzle manufacturer information, and with similar data found in the literature. Drop pulverization was higher for the turbulence nozzle than for the two air induction nozzles at every pressure, and an increase in the spraying pressure caused an increase in the drop pulverization, regardless of the type of nozzle. Thus, even if further experiments are necessary, the results validated the functionality of the proposed measurement system.  
As regards the drop size distribution, the number probability distribution function that best described the experimental data was the gamma, whose scale and shape parameterswere adjusted in the function of nozzle type and spray pressure. The shape parameter was almost constant for each nozzle, while the scale parameter showed a clear decreasing trend with the increase in pressure, an index of higher drop pulverization. This aspect will be further investigated in future research considering other nozzle types, including the reference nozzles, and other pressures, in such a way as to extend the present results to a model able to reasonably estimate the drop size distribution and the most significant diameters in function of the operating conditions.  
Conceptualization, E.C. and G.M.; methodology, E.C., G.M., D.L. and R.P.; software, E.C. and D.L.; formal analysis, E.C., G.M., D.L. and R.P; investigation, E.C., G.M., D.L. and R.P.; resources, E.C., G.M. and D.L.; data curation, E.C., G.M., D.L. and R.P.; writing--original draft preparation, E.C.; writing--review and editing, E.C., G.M., D.L. and R.P.; visualization, E.C., G.M., D.L. and R.P.; supervision, E.C. and G.M.; project administration, G.M.; funding acquisition, E.C. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Catania with the research project \"Contributo della meccanica agaria e delle costruzioni rurali per il miglioramento della sostenibilita delle produzioni agricole, zoo-tecniche e agro-industriali. WP1: Impiego sostenibile di macchine irroatrici in serra e in pieno campo\", Linea di intervento 2-dotazione ordinaria per attivita istituzionali dei dipartimenti-2016-18-II annualita.  
Not applicable.  
Data available on request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Literature Review and Analysis on HRC Modeling Methods
#### 2.1.4 Behavior Trees  
Behavior trees have been widely used in the field of robotics to define the behavior of autonomous agents. The graphical modeling language involves a hierarchical structure of tasks and dependencies, making them an ideal approach to model complex decisions and coordination in HRC scenarios.  
The literature related to behavior trees is largely concerned with the limitations of automation to capture human dexterity and judgement, especially in unstructured environments where unforeseen events commonly occur. A framework was thus presented, which captured various behaviors through which robot autonomy is enhanced. This was achieved using behavior trees to model the robot's intelligence, such as social behaviors, human intention, and various tasks, including collaborative ones [45].  
While behavior trees can be a valuable tool for modeling the autonomous behavior of a robotic system, they are less suited for modeling the complexities of human-robot collaboration that involve, for example, communication, shared decision-making, and social interaction. Behavior trees tend to focus on predefined actions and decision sequences, making them less flexible in terms of accommodating the varied and sometimes unpredictable nature of human intention. Behavior trees prioritize system-centric decision-making and are not adept at handling human-centric decision processes. The design of behavior trees is not human-centered, nor user-friendly; this is another drawback of behavior tree modeling.
## 4 Performance Analysis
### Average Delivery Ratio  
The overall created messages are divided by the number of messages, which can be analyzed by TMS. To calculate the average delivery ratio, we must divide the total number of messages sent by a sender to a destination host in the system by the total number of messages that were received. The goal is to send as much information to its destination as possible.
suppress phytopathogens, improve the soil structure, and detoxify the soil by degrading toxic compounds such as pesticides, xenobiotics, and plant hormones [9; 10]. Notably, rhizobacteria, which colonize plant roots and the surrounding soil, represent a well-known type of biofertilizer due to their capacity to transform, solubilize, and mobilize nutrients from bulk soil [11; 12].  
Rhizobacteria are crucial in maintaining soil fertility by recycling nutrients [13]. Owing to their diverse roles in improving crop production, biological approaches have garnered attention from plant scientists for managing the plant nutrient system. Substantial research has explored the various roles of rhizobacteria in enhancing plant growth [10; 14], stress tolerance [12; 15], pesticide detoxification [9], the biological control of pathogens [16; 17; 18; 19], phytohormone production [20], ammonia production, and nitrogenase activity [21; 22]. Due to these beneficial effects, symbiotic and non-symbiotic rhizobacteria are used globally as bio-inoculants to enhance growth under severe environmental stress [23; 24; 25; 26; 27].