esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Motif Discovery Assisted by Complex Networks  
The identification of motifs accounting for the antibiofilm activity can be assisted by the exploration of ABFP similarity networks looking for sequence patterns within network communities. Although the HSPN representing the ABFP chemical space was built using AF distance metrics (Euclidean) and the network communities are estimated considering parameters from the nodes and edges properties [40], such clusters should contain peptides sharing similar features. Thus, the communities from the HSPN model of the 174 ABFPs resulted in the source for the motif discovery. The sequence diversity in each community was evaluated by global alignments. Figure S2 displays the heatmaps that mirror the pairwise sequence identities for communities containing more than two peptides that correspond to the clusters 4, 7, 9, 11, 14, 15, 17, and 22, including clustered singletons (Supplementary File S3). Figure S2 evidenced a high sequence diversity within all communities. Consequently, iterative alignment algorithms such as MAFFT and MUSCLE were applied to deal with the high sequence diversity. The multiple sequence alignments (MSAs) were visualized with the Jalview, which allowed the estimation of their corresponding consensus sequences and Seq2 Logos. The consensus sequences from the MSAs were also estimated by the EMBOSS Cons. The full exploration of the MSAs considering their corresponding consensus sequence and Seq2 Logos allowed the identification of conserved regions considered ABF motifs. The strategy carried out for the identification of the motifs in the MSAs and performed on cluster 4 of the HSPN is illustrated in Figure 11, while the strategy for all communities/clusters is displayed in Figure S3. Table 5 listed ABFP motifs identified by each MSA method at each network community or cluster. The consensus estimated by the EMBOSS Cons was the preferred  
Figure 11: Motif detection by the multiple sequence alignment (MSA) algorithms MAFFT and MUSCLE on the network cluster 4. The MSAs are visualized with the Jalview program, which also estimates a Seq2 Logo and the consensus from the alignment positions. Another consensus sequence that served as a guide for motif location was estimated by the EMBOSS Con. template for motif identification, because it gives a more legible output. High-scoring amino acids/positions are represented by capital letters, and lower-scoring but positive residues by lower-case letters; non-consensus positions are denoted by x (Table 5). As part of the motif discovery process, the AB search was complemented by evaluating an AF approach. Based on its high performance and versatility in identifying motifs in OMICs data, the STREME algorithm was applied to find unaligned patterns ranging from 3-5 aa length in each network community [46].
# Estimating the Thermal Conductivity of Unsaturated Sand
## 3 Model Development
### Determination of Parameters \(A\) and \(B\)  
The physical characteristics of samples, such as grain size distribution, particle and bulk density, and porosity should be defined before laboratory thermal conductivity experiments. The determination of parameters \(A\) and \(B\) can be accomplished using the following steps. First, at a constant temperature, which is controlled within the range of 20-30 \({}^{\circ}\)C, the dry thermal conductivity \(\lambda_{dry}\) of the samples is measured by the transient method, and \(\lambda_{es}^{dry}\) is calculated based on Equation (3). \(\lambda_{es}^{dry}\) is a function of sand porosity as discussed previously. Then, the sample thermal conductivity \(\lambda\) is measured for different \(S_{r}\) values under identical settings. The spans of \(S_{r}\) elected in the test should be larger than 15%, and the magnitudes of selected \(S_{r}\) should cover above 80%. A series of \(\lambda_{es}\) values for different \(S_{r}\) are calculated via Equation (4). Finally, the relationship of the proposed function \(f(S_{r})\) between \(\lambda_{es}\) (\(S_{r}\
eq 0\)) and \(S_{r}\) can be described by a hyperbolic expression, and the parameters \(A\) and \(B\) in the hyperbolic expression can be determined through nonlinear regression analysis based on the calculated \(\lambda_{es}\).  
Figure 5 shows that the parameters \(A\) and \(B\) of the four samples are determined by nonlinear regression analysis, which is performed by a hyperbola. For each sample, the hyperbolic expression fit of \(\lambda_{es}\) to \(S_{r}\) can better describe the increase in \(\lambda_{es}\) with \(S_{r}\). The growth trends of \(\lambda_{es}\) in the form of a hyperbolic curve are dramatic, especially when \(S_{r}\) varies from 0 to 30%, and then enters a stable region as \(S_{r}\) is over 40%. After the series of steps above, substituting the coefficients including \(\lambda_{es}^{dry}\) and parameters \(A\) and \(B\) of \(f(S_{r})\) into Equation (7), the full MPTC model can be obtained.
## 3 Results  
### Frequency Drift  
Measurements of the realignment time (\(T_{SY}\)) between each pair of nodes are reported in Table 1, where \(2\longrightarrow 1\) indicates the realignment time between the PWM signals of converter 2 and converter 1, and similarly for the other pairs of units. Data was captured using the statistical functions of a digital storage oscilloscope (Figure 5).  
Combining the results of Tables 1 and 2 confirms the intuitive idea that increasing realignment time results in a smaller oscillator mismatch, i.e., frequencies are better matched. The values in Table 2 confirm that, even if all the microcontrollers are clocked by quartz oscillators of the same nominal value, the actual clock frequencies are randomly dispersed. This frequency mismatch, although small, if left uncontrolled, can quickly lead to arbitrarily large phase differences, which justifies the need for a closed loop control (not described in this paper) to strictly maintain the required phase match after the initial alignment.  
Considering Equations (4) and (5), it is possible to estimate the relative oscillator tolerance between units. These tolerances are reported in Table 2.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Application Ontology Extensions for Modeling the Scenario  
Application ontology extensions, shown in Figure 5, are defined to enrich the SWARMs ontology model in order to represent the oil spill detection scenario and encode marine experts' understanding.  
The determination of the severity of an oil spill is subject to several factors, including SpreadSpeed, EstimatedSize, Thickness, Currents, and Weather. The concept of the SeverityLevel is linked to its reference factors via the relationship _dependsOn_, whose attribute is set as transitive. All these criteria are described in the following.  
* SpreadSpeed. This concept includes two types of velocity, namely, Fast and Slow, to describe the possible spreading speed of the detected oil spill. It is asserted that the spreading speed depends on two features: Weather and Currents.
* Weather. It conceptualizes two kinds of weather conditions: Clement and Inclement. Spills often occur in inclement weather and spread more widely in such weather.
* Currents. It describes whether currents in the spilled region fluctuate strongly or weakly.
* EstimatedSize. It is a characteristic of spills and represents the estimated coverage range of spills. Specifically, it is divided into two kinds, Large and Small.
* Thickness. It defines a measuring unit to estimate the quantity of spilled oil, such as Thick and Thin.  
All raw context information, gathered by vehicles, can be diagnosed by the data processor component, transformed by the semantic mapper component, and populated into the ontology described above. Therefore, based on the ontology model, a set of high-level information with more meaningful semantics, which is more useful for operators and vehicles, can be obtained based on the different reasoning capabilities provided by the context reasoner.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 4 Proposed Method
### Feature Extractor and Prototype Classifier
#### 4.1.4 Prototype Classifier  
KIS classification and UIS rejection are realized by using a prototype classifier after feature extraction in the HCPL.  
Figure 4: HAFRNet.  
The multiclassification process in the prototype classifier is formulated as:  
\[x\in class\ \underset{i=1}{\text{argmax}}\ S(\mathbf{m}_{i};f(x;\theta)) \tag{14}\]  
where \(f(x;\theta)\) is the feature of sample \(x\), and \(S(\mathbf{m}_{i},f(x;\theta))\) is the similarity measure function defined as the inner product between the sample feature and prototypes as follows:  
\[S(\mathbf{m}_{i};f(x;\theta))=f^{T}(x;\theta)\mathbf{m}_{i} \tag{15}\]  
To realize the OSR of interference signals, we propose a process for KIS classification and UIS rejection according to the inner product between the sample feature and each prototype, as shown in Figure 5.  
For each test sample \(x\), the probability score of class \(i\) based on the inner product is defined as:  
\[score^{i}=\frac{e^{\alpha f^{T}(x\theta)\cdot\mathbf{m}_{i}}}{\sum\limits_{k=1}^{ N}e^{\alpha f^{T}(x\theta)\cdot\mathbf{m}_{k}}} \tag{16}\]  
where \(\alpha\) is a hyper-parameter that controls the hardness of the similarity-probability conversion.  
When a threshold \(\beta\) is given, the sample belongs to unknown classes if the highest probability score is lower than \(\beta\); otherwise, it belongs to known classes:  
\[prediction=\left\{\begin{array}{ll}class\underset{i}{\text{argmax}}score^{i} &if\ \underset{i}{\text{max}}score^{i}>\beta\\ unknown&if\ \underset{i}{\text{max}}score^{i}<\beta\end{array}\right. \tag{17}\]
# Methane Gas Density Monitoring and Predicting Based on RFID Sensor Tag and CNN Algorithm
## 5 Conclusions  
Considering the advantages of integrating RFID and WSN, a novel monitoring and predicting method for underground methane gas density is introduced in this paper. The proposed wireless sensor is based on passive RFID technology and consists of a communication section, RF front-end  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reported Method** & **Cost** & **Memory** & **Function** & **Range (m)** & **Accuracy** \\ \hline
[8] & high & methane gas density & monitoring & / & high \\
[9] & high & methane gas density & monitoring & / & medium \\
[15] & high & methane gas density & monitoring & / & high \\ This work & low & methane gas density and ID message & monitoring and predicting & 17 & high \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances comparison of different wireless sensors.  
Figure 11: Comparison of measured results and predicted results.  
Figure 12: Prediction error of least squares support vector regression (LS-SVR).  
section, and digital section. The data transmitted by the wireless sensor contains miscellaneous noises, which makes it difficult to realize accurate monitoring and prediction. Hence, a deep learning approach is employ to extract robust features from the raw data. The experimental results demonstrate that the introduced wireless sensor achieves a maximum communication distance of 10.3 m and can accurately measure and predict the underground methane gas density.  
[PERSON] and [PERSON] are responsible for the algorithm design. [PERSON] and [PERSON] are responsible for the design of the RFID sensor tag. All authors provided help in the revision of this manuscript.  
This work was supported by Natural Science Foundation of China (51767006, 51464015), Natural Science Foundation of Jiangxi Province (20171 BAB206045), and Science and Technology Project of Education Department of Jiangxi Province (GJ)160491, GJJ170378).  
The authors declare no conflict of interest.
# Removal of Nitrate Ions Using Thermally and Chemically Modified Bioadsorbents
In the aqueous phase, nitrate is present in anionic form (NO\({}_{3}\)\({}^{-}\)) due to the complete solubilization of HNO\({}_{3}\) in water. Thus, electrostatic repulsion between the nitrate and the negatively charged biochar surface is expected, which establishes one of the most critical barriers for nitrate adsorption on biochars and biomasses, showing low effectiveness when evaluated without protonating their surface [20]. In Colombia, there is a national policy oriented to the intensive cultivation of yam on the northern coast of Colombia and areas affected by the armed conflict, intending to export and satisfy national consumption; therefore, many hectares of cultivation are exploited for export and local consumption. However, large volumes of post-harvest yam remain that do not meet the quality standards for human consumption. Thus, research was motivated around industrialization, such as conversion to flour for animal feed, starch and biopolymers for food packaging, among others; therefore, this study proposes an alternative use for the large volumes of peel that will be generated in yarn biorefineries as an agroindustrial rejection product, seeking to use and optimize the resources and residues from this raw material [26]. A total of 90% of yarn production takes place in the departments of Bolivar, Cordoba and Sucre, on Colombia's northern coast, with about 27,938 yarn production units [26]. For this reason, the uses of yams as a source of starch production have diversified [27], including flour [28], biopolymers [29] and adsorbent materials [30; 31]. Due to the high availability of residual material from yarn exploitation, this work aimed to evaluate the efficacy of natural yarn skins and biochar modified with NH4 Cl as adsorbent for the removal of aqueous nitrate. Morphology and surface composition of biodsorbents were studied by scanning electronic microscopy (SEM) and X-ray dispersion spectrometry (EDS). The response surface method (RSM) was used to optimize the NO3 adsorption capacity. Adsorption kinetic and equilibrium behavior was also studied, using mathematical models to determine adsorption mechanisms. Bioadsorption has been widely studied for the removal of cations in solution as a method of effluent treatment before discharge into the environment; however, the novelty of this study consists in modifying these biodsorbents to test their effectiveness in the removal of anions as an alternative treatment method with low environmental impact, which seeks to reduce the environmental load of effluents discharged into the environment while solving the problem of contamination due to inadequate final disposal of agroindustrial solid waste.
## 2 Background
### Path Discovery  
The interesting part about HWMP is how the protocol discovers paths for mesh STAs or, in other words, the wireless routing. Figure 7 shows an example of how a bi-directional path can be formed using the PREQ and PREP frames. In this example, mesh STA A is the originator, while mesh STA D is the target. Tables 1 and 2 are generated based on the scenario depicted in Figure 7. Table 1 is an example of a routing table for mesh STA C after it received the PREQ frame that originated from mesh STA A. Table 2 is an example of a routing table for mesh STA B when it receives the PREP frame replied by mesh STA D. The path discovery process is completed once the reverse and forward paths are formed. At that point, mesh STA A and D can communicate with each other using the path [A,B,C,D].  
When a PREQ frame is received, the forwarding information for the originator mesh STA and the transmitter mesh STA can be updated. When a PREP frame is received, the forwarding information for the target mesh STA and the transmitter mesh STA can be updated. The forwarding information is created and updated according to the message and type of node, as described in Table 3. The table is sourced from the 802.11s drafted standards [3].
## 2 Related  
**Featured Application: Developed mathematical models and correlations describing the effects of the high-osmolality environment of sugar beet molasses on the viability of selected microorganisms, allowing a better understanding and management of the safety of different products during the application of molasses in food processing.**  
In this research series, several sugar beet molasses of different osmolalities were inoculated with a mix of the following microorganisms, _Escherichia coli_, _Salmonella_ spp. and _Listeria monocytogenes_, to develop mathematical models and correlations of the effect of different levels of osmolality and different exposure time to the viability of the selected microorganisms. The respective enumerations of _Escherichia coli_, _Salmonella_ spp., _Listeria monocytogenes_, Entobacteriaceae, and total plate count were conducted on inoculated molasses samples of different osmolalities (from 5500 to 7000 mmol/kg) and at different exposure times (from 0 to 5 h). The results showed that by increasing molasses osmolalities, all the selected microorganisms' exposure time viability measures statistically decreased significantly. _Salmonella_ spp. showed the highest viability of all the tested microorganisms in a high osmotic environment. In contrast, _Listeria monocytogenes_ showed the least resilience to osmotic stress, with a reduction in the numbers below the detection limit. The developed mathematical models of microorganisms' viability exposed to molasses's high-osmolality environment were statistically significant, allowing for the good prediction of a number of microorganisms based on exposure time and osmolality levels. The obtained results describe molasses's excellent microbial load-reducing capability and provide the potential for applications in the production of safe foods.  
sugar beet molasses; osmolality; _Escherichia coli_; _Listeria monocytogenes_; _Salmonella_ spp.
## 2 Recent Application of Hyperspectral in Food Authentication
### Honey and Honey Products  
Recent applications of HSI have focussed on the analysis of honey and honey products in order to trace their origin. A recent paper reported the ability of HSI imaging (NIR and Vis) to detect and trace the origin of honey samples [21]. In this study, the authors analysed several honey samples (\(n\) = 52) using a HSI system and the data analysed by applying three different machine learning algorithms [21]. The machine learning algorithms that were tested by the authors were radial basis function (RBF) network, support vector machine (SVM), and random forest (RF) [21]. Before analysis, the dimensionality of the data set was simplified be means of principal component analysis (PCA) [21]. The best results that are reported by these authors were obtained when RBF was used to classify the honey samples (94% correct classification), according to origin [21]. The authors also reported that three types of honey (buckwheat, rapeseed, and heather floral origin) were correctly classified (100% accuracy) [21]. It was concluded that the proposed approach based on HSI has a great potential for the classification of honey samples according to their floral origin [21]. The authors also highlighted that other honey properties aside from its floral origin can be predicted with this method [21].
# Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method
## 2 Materials and Methods
### Methods
#### 2.2.2 Principle of GA-BP Neural Network  
The BP neural network method optimized by genetic algorithm was used to predict the peak shear strength. BP neural network is a multi-layer feed-forward neural network, which is characterized by signal-forward propagation, error-back propagation, and has excellent nonlinear fitting ability and generalization ability [34]. Genetic algorithms can make it infinitely close to the global optimal solution by iteratively optimizing the calculation results [35]. Therefore, the genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network model to improve the calculation efficiency and prediction accuracy of the model. The synergistic mechanism of the genetic algorithm and the BP neural network model was then constructed [36].  
The BP neural network model is composed of input layer, hidden layer and output layer. Each layer contains one or more neuron nodes. Based on the multi-layer feed-forward neural network trained by the error-back propagation algorithm, the mapping relationship between the input value and the output value is established through continuous training and learning of the data [37]. The number of hidden layers is determined by Formula (20).  
\[l=\sqrt{m+n}+a \tag{20}\]  
where \(m\) is the number of neurons in the output layer; \(n\) is the number of neurons in the input layer; \(a\) is a constant between 1 and 12; \(l\) is the number of neurons in the hidden layer, \(l\in\left[4,15\right]\).  
The constructed GA-BP neural network model is evaluated by determining coefficient (\(R^{2}\)), mean absolute error (\(MAE\)), mean bias error (\(MBE\)) and root mean square error (\(RMSE\)).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(x_{sim}-x_{i})}^{2}}{\sum\limits_{i=1}^{n }{(\overline{x}-x_{i})}^{2}} \tag{21}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{|a_{i}-b_{i}|} \tag{22}\]  
\[MBE=\frac{1}{n}\sum\limits_{i=1}^{n}{(a_{i}-b_{i})} \tag{23}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(a_{i}-b_{i})}^{2}} \tag{24}\]  
where \(x_{i}\) is the experimental value; \(x\) is the average value of the test; \(x_{sim}\) is the predicted value; \(a_{i}\) and \(b_{i}\) represent the predicted value and the actual value; \(n\) represents the number of specimens.  
By calculating \(R^{2}\), the correlation between the predicted value and the real value can be observed. The higher the correlation is, the better the prediction accuracy is. The smaller the average absolute error is, the smaller the gap between the predicted value and the real value is. The closer the average deviation is to 0, the better the model fitting effect is. The trained model is used to predict the test data, and the error between the predicted value and the actual test result is observed. The accuracy of the training model is tested by the size of the error.