esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Growth Chamber Experiments  
Growth chamber experiments were performed to evaluate the control efficacy of the three bacterial strains that showed the highest percentage of fungal growth inhibition: _S. fradiae_ Hvs6, _B. velezensis_ FC37 and _B. paralicheniformis_ Hvs2. Asparagus seeds cv. Grande F1 were surface disinfested by immersion with 10% sodium hypochlorite for 10 min, subsequently rinsed with tap water and air dried on sterile paper in a laminar flow cabinet. Seeds were then immersed in a suspension of 10\({}^{8}\) bacteria/mL (prepared as explained above) and incubated at room temperature for 1 h with gentle agitation. inoculated seeds were germinated on 1% agar-water Petri dishes at 25 \({}^{\circ}\)C in the dark. Non-inoculated seeds were immersed in sterile NB liquid medium. Once the radic had emerged by 1-1.5 cm, seedlings were trespassed to incubation trays with sterile vermiculite and incubated in a growth chamber set at 25/18 \({}^{\circ}\)C (light/dark) with a 14 h photoperiod for 2 weeks. When plants reached an appropriate size, they were first inoculated by adding 1 mL of freshly prepared bacterial suspension (10\({}^{8}\) bacteria/mL) around each plant, and 24 h later by immersing the roots in a fungal suspension of 10\({}^{6}\) conidia/mL for 30 min. For fungal inoculum preparation, fungal isolates were cultured in Czapek-Dox broth on a rotary shaker (150 rpm) at 25 \({}^{\circ}\)C with a 12 h photoperiod for 10 days. The spore suspension was filtered through four layers of cheese cloth and adjusted with sterile distilled water to a concentration of 10\({}^{6}\) conidia/mL. Non-inoculated control plants were root-dipped in sterile Czapek-Dox broth. Plants were then transplanted into pots with 1.6 L of sterile vermiculite and maintained in a growth chamber set at 25/18 \({}^{\circ}\)C (light/dark) with a 14 h photoperiod (14,000 lux) for 60 days. Plants only inoculated with bacteria, only inoculated with fungi and non-inoculated plants were used as controls. Plants were watered as required and fertilized once a week with Hoagland medium [23]. Twelve plants were tested for each fungal-bacterial combination, with three plants per pot arranged in a completely randomized design.
###### Abstract  
Bioremediation of hydrocarbons in soil is a highly complex process, involving a multiplicity of physical, chemical and biological phenomena. Therefore, it is extremely difficult to control and boost the bioremediation of these systems after an oil spill. A mathematical model was developed to assist in the prediction and decision-making regarding the in situ bioremediation of hydrocarbon-contaminated soils. The model considered the most relevant processes involved in the mass transfer and biodegradation of alkanes over time and along the depth of a flooded soil column. Aliphatic hydrocarbons were chosen since they are less water soluble than aromatics and account for 50-90% of the hydrocarbon fraction in several petroleum products. The effect of adding oxygen, nitrate, iron (III) or sulfate as electron acceptors was then simulated (bioremediation scenarios). Additionally, and to feed the model, batch assays were performed to obtain experimental data on hydrocarbon adsorption to soil particles (more than 60% of hydrocarbons tends to be adsorbed to soil particles), as well as hydrocarbon biodegradation rates in the presence of nitrate (0.114 d\({}^{-1}\)) and oxygen (0.587 d\({}^{-1}\)). The model indicates that saturated hydrocarbon removal occurs mainly with adsorption/desorption and transport processes in the upper layers of soil due to methanogenic biodegradation in deeper layers, since the other microbial processes are soon limited by the lack of electron acceptors. Simulation results show that higher initial electron acceptor concentrations led to higher hydrocarbon removal, confirming that the model is performing in accordance with the expected. Close to the surface (at 0.1 m depth), all scenarios predicted more than 83% hydrocarbon removal after two years of simulation. Soil re-aeration results in faster hydrocarbon removal (more than 20% after one year) and surfactants addition (around 15% after one year) may also accelerate soil bioremediation. With this model, the simultaneous contributions of the various physicochemical and biological processes are integrated, facilitating the simulation and comparison of different bioremediation scenarios. Therefore, it represents a useful support tool for the management of contaminated sites.  
mathematical modelling; bioremediation; biostimulation; electron acceptors; hydrocarbons
## 2 Related Works
### Packet Routing
The shadow edges localization procedure is performed during the cluster localization step for clusters having more than three non-collinear, connected localized nodes. For clusters having less than three non-collinear, connected localized nodes, the super-cluster localization step is used, which considers nodes in adjacent clusters. The shadow edges localization procedure is applied to the gateway nodes to improve the overall position information. [PERSON] et al. [26] proposed a system that, when a mobile node needs to calculate its position, it attempts to use reference nodes that are one hop away. If it does not have enough reference nodes that are one hop away to calculate its position, then it looks for multi-hops-away reference nodes that will serve as distance reference nodes. To do so, the sensor node uses its neighbors that can relay using the OLSR [22] by routing the information of other ad hoc nodes that are multi-hops away from the sensor node. When a distance reference node is found, the estimated distance to the sensor node is calculated using the DV-hop and DV-distance methods. The ad hoc routing is necessary to search the multi-hop node that knows its position, and the OLSR routing is used to offer the position calculation of the nodes. Finally, the recursion-based localization process will be completedby offering to estimate distances of nodes that are multi-hops away from the first node that already calculated its position. [PERSON] et al. [27] presented a routing algorithm that can be used in the field of centralized range-based localization schemes. Given two non-neighboring sensors, an unknown sensor and an anchor node, and only using the network connectivity, all of the evaluated shortest paths with the minimum number of hops are averaged to obtain a final distance estimate. This process can be repeated with different anchor nodes in order to estimate the unknown node position using both distance estimates and the absolute positions of anchors. To estimate distances between two non-neighboring sensors, the proposed algorithms follow two steps: to find all paths with minimum hops between the non-neighboring sensors and to obtain the length of the paths based on the known one-hop distance. The non-neighboring distance estimate is calculated as the mean of all evaluated path distances.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 3 Methods
### Interference Elimination in Remote Sensing Images  
A few clouds were present in the used images, and need to be considered, as shown in Figure 2a. In this figure, vegetation is shown in red, while clouds, snow, and ice are presented in white. Textural features are described in terms of spatial variability associated with the digital numbers (DNs) of local pixels. Hence, vegetation and clouds in the image would conceal or impair the textural information. To eliminate interference from vegetation, the normalized difference vegetation index (_NDVI_) was calculated using Equation (4). Based on the statistical characterization of vegetation pixels from the _NDVI_ image, the threshold value for _NDVI_ was set as 0.4 to mark off the vegetation pixels, which are colored green in Figure 3a. Clouds have a relatively high reflectance in the _NIR_ wavelength (_band_ 5). Based on the statistical characterization of cloud pixels from the _NIR_ reflectance image, the threshold of 0.35-1.0 was set to mark off the cloud pixels, as shown in white in Figure 3b. The pixels falsely labeled as cloud were mainly distributed on the bright valley walls (e.g., in the northeast corner of Figure 3b). The bright valley in this study area is mostly covered by vegetation. Since a mask of vegetation pixels was also used before image classification, the pixels falsely labeled as cloud will not influence the results.  
\[NDVI=\ \frac{NIR-R}{NIR+R}=\frac{band\ 5-band\ 4}{band\ 5+band\ 4} \tag{4}\]
# Vibrational Model of Heat Conduction in a Fluid of Hard Spheres
## 2 Hard Sphere Fluid  
The HS interaction potential is defined as:  
\[\phi(r)=\begin{cases}\infty,&r<\sigma\\ 0,&r\geq\sigma,\end{cases} \tag{1}\]  
where \(\sigma\) is the sphere diameter and \(r\) is the distance between the centres of two spheres. The potential ensures that spheres cannot overlap. The HS system is a very important simple model for the properties of condensed matter in its various states [40; 41; 42; 43; 44; 45; 46; 47].  
The thermodynamic and transport properties of HS systems depend on a single reduced density parameter, \(\rho^{*}=\rho\sigma^{3}\), or the packing fraction, \(\eta=\pi\rho\sigma^{3}/6\), where \(\rho\) is the density. Transport properties of the HS fluid have been extensively studied (see e.g., Ref. [42] for a review). Here we use recent molecular dynamics (MD) simulation results for the thermal conductivity coefficient reported by [PERSON] et al. [48]. The use of large simulation systems and long simulation times allowed accurate prediction of the thermal conductivity coefficient in the thermodynamic limit.  
Throughout the paper we use macroscopically reduced units for the thermal conductivity coefficient:  
\[\lambda_{\text{R}}=\lambda\frac{\rho^{-2/3}}{v_{\text{T}}}, \tag{2}\]  
where \(\lambda\) is the dimensional thermal conductivity coefficients, \(v_{\text{T}}=\sqrt{T/m}\) is the thermal velocity, \(T\) is the temperature in energy units (\(\equiv k_{\text{B}}T\)), and \(m\) is the atomic mass. This normalization is essential in [PERSON]'s excess entropy scaling approach [18; 19], therefore the subscript R is often used. This normalization does not contain the Boltzmann constant \(k_{\text{B}}\), because the temperature is measured in energy units. Another normalization that is commonly employed is:  
\[\lambda^{*}=\lambda\frac{\sigma^{2}}{v_{\text{T}}}. \tag{3}\]  
These two normalizations are related via \(\lambda^{*}=\lambda_{\text{R}}(\rho^{*})^{2}\).
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 4 Simulation Results
### Error Performance
The SNR will be affected by the change of the average power of the transmitted signals. With the increase of the average power, SNR increases gradually. Therefore, the error performance will be affected by the dimming factor. From the above reasoning and the BER curves provided in Figure 3, we can conclude that with the increase of the dimming factor, the error performance gets better gradually.
## 3 Results
### Structure of Charged Defects
#### 3.2.2 U Vacancy in UO\({}_{2}\)  
The U vacancy in the fluorite structure resides at the center of the cube formed by the first eight neighboring oxygen atoms. The difference between neutral and charged supercell calculations lies in the presence or absence of local charge compensation mechanisms around the defect site [27; 28]. We examine these differences by explicitly analyzing the difference in charge density and Bader charge in neutral and charged supercell calculations. The neutral uranium vacancy is charge compensated by four U sites (\(h\). or U\({}_{\text{U}}\)\({}^{\delta+}\)) with Bader charges of 2.79-2.81 (much larger than Bader charge of 2.54 on U site in bulk UO\({}_{2}\)) located at 2 nd (3.86 A) and 4 th (5.44 A) neighboring distances from the defect site. However, for the charged (4\(-\)) uranium vacancy there is no local charge compensation from neighboring U sites.  
The changes in nearest neighbor distances (Figure 8) for the both the neutral and charged U vacancy clearly reveal the differences in the short- and long-range atomic relaxation around the defect site. The lattice distortion around the neutral vacancy extends to a relatively larger distance (\(\sim\)6-7 A) compared to the charged vacancy (\(\sim\)4-5 A), because of the presence of neighboring U\({}_{\text{U}}\)\({}^{\delta+}\), which are absent for the charged vacancy. For both the neutral and charged supercell, the relative change in neighboring distances saturates away from the defect site. The fractional volume change for neutral and charged U vacancy are \(-0.46\%\) and \(2.1\%\), respectively, in a 144-atom supercell. Analysis in a larger 324-atom supercell yields similar trends in the local and long-range defect structure of neutral and charged U vacancies.  
In corresponding investigations of charged vacancies in GaAs and Si we find that vacancies show short-ranged local distortion around the localized defect sites. However, changes in nearest neighbor distances away from the localized defect site are manifestations of the overall volume change, which depend on the charge state of the defect.
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 4 The New Clustering Routing Approach
### Operation Step
#### 4.2.2 Identification of Optimal Network Structure  
In our approach, the LS-II scheme selects the optimal cluster network structure \(C\) by determining the optimal group of cluster heads. Therefore, in each execution, cluster head nodes are identified to integrate a new network topology. Then, the network performance is evaluated with regard to its fitness value. In this paper, an objective function to assess the effectiveness of the network structure is implemented. This cost function integrates four elements: the total distance from cluster heads to the base station, the total intra-cluster distance, the residual energy of cluster heads, and the energy consumption. The objective function is modeled by the following Equation (17).  
\[f(C)=f_{1}(C)+f_{2}(C)+f_{3}(C)+f_{4}(C) \tag{17}\]  
The first element denotes the total intra-cluster distance, which is computed as follows:  
\[f_{1}(C)=\frac{d_{max}\Big{(}\sum_{j=1}^{k}d\big{(}\mathbf{n}_{j},CH_{j}\big{)} \Big{)}-\sum_{j=1}^{k}d\big{(}\mathbf{n}_{j},CH_{j}\big{)}}{d_{max}\Big{(}\sum_{j= 1}^{k}d\big{(}\mathbf{n}_{j},CH_{j}\big{)}\Big{)}-d_{min}\Big{  
The fourth element represents the relationship between sensor node members of the sum of the residual energy and its cluster head:  
\[f_{4}(C)=\frac{E_{rmax}\left(\sum_{j=1}^{k}\frac{\sum_{i=1}^{N_{j}}E_{r}\left(n_{ ij}\right)}{E_{r}\left(CH_{j}\right)}\right)-E_{r}\left(\sum_{j=1}^{k}\frac{\sum_{i=1}^{N_{ j}}E_{r}\left(n_{ij}\right)}{E_{r}\left(CH_{j}\right)}\right)}{E_{rmax}\left(\sum_{j=1}^{k} \frac{\sum_{i=1}^{N_{j}}E_{r}\left(n_{ij}\right)}{E_{r}\left(CH_{j}\right)} \right)-E_{rmin}\left(\sum_{j=1}^{k}\frac{\sum_{i=1}^{N_{j}}E_{r}\left(n_{ij} \right)}{E_{r}\left(CH_{j}\right)}\right)} \tag{21}\]  
where \(E_{r}\left(CH_{j}\right)\) represents the residual energy of the cluster head. \(E_{r}\left(n_{ij}\right)\) corresponds to the residual energy of the sensor node. \(E_{max}\) and \(E_{min}\) denote the maximum and minimum total residual energy, respectively.  
A lower value of \(f(C)\) expresses that the identified set of clusters represents a better cluster network structure. Therefore, the LS-II method selects a set of cluster heads to produce distinct cluster structures in each execution with the objective to determine the optimal network structure. Our proposed protocol is exhibited in Algorithm 1.  
```
Input: Number of alive nodes Output: The number of \(k\) clusters while\(t\leq t_{max}\) | Identify cluster heads considering the search strategy of LS-II | if the distance from a node to the closest CH is smaller than to the BS | | Allocate the node to the closest CH | else | | The node transmits the information right to the BS | end if else | Compute the cost function of the produced cluster network structure \(f\left(C^{t}\right)\) | if\(f\left(C^{t}\right)<f\left(C^{opt}\right)\) | | Update the identified cluster network structure as: | | \(f\left(C^{opt}\right)\gets f\left(C^{t}\right)\) | end if end while
```  
**Algorithm 1** LS-II routing protocol
# Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework
## 3 Methodology
### Forensic Analysis Using Machine Learning
#### 3.4.2 Data Preprocessing  
To ensure data consistency, completeness, and soundness, we remove unnecessary fields, remove any characteristics that do not contribute to classification, encode categorical features, and numerical features are also scaled within the range of 0 and 1. Fields such as IP address, port address, and attack type that were previously used for labeling must be removed to avoid data bias and model performance degradation. To remove missing numbers and outliers, various strategies are used.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## 4 Path Planning in Complex Terrains
### Complex Terrain Environment Model  
Common terrains in the wild include flat land, grassland, mountain land, marsh land, rivers, lakes, etc. If we set the marsh land, rivers, lakes, and other impassable areas as obstacle areas and distinguish the flat land, grassland, and mountain land with different colors, then a complex terrain environment model can be established, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Conventional A*** & **Improved RRT*** & **Improved A*** \\ \hline Path length (m) & 66.15 & 65.72 & 66.49 \\ Turning times (times) & 14 & 11 & 9 \\ Sum of turning angles (degree) & 630.00 & 124.36 & 168.89 \\ Length of risky path (m) & 41.53 & 19.25 & 12.89 \\ Length of dangerous path (m) & 30.83 & 8.45 & 5.16 \\ Computation time (s) & 0.06 & 17.84 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons of the conventional A*, improved RRT*, and improved A*.  
Figure 9 represents the square with a side length of 100 m, divided into 100 \(\times\) 100 grids. Figure 9 indicates the terrain model. The obstacles and three different kinds of terrains are distributed irregularly in the regions, wherein the black regions represent obstacles, the yellow regions represent mountain land regions, the green areas represent grassland regions, and the other areas represent flat land regions. Figure 9 indicates the obstacle model. \(S\)'s position coordinates are (2, 4) and \(G\)'s (96, 97).
# Adaptive Antenna Array Control Algorithm in Radiocommunication Systems
## 3 Adaptive Algorithm  
The adaptive antenna system is based on controlling the radiation characteristics of individual active elements so that the value of the characteristic of the entire antenna is at maximum in the direction of arrival of the useful signal, while in the directions of arrival of interfering signals, the value of the radiation pattern is minimal (theoretically it should be zero, but this is unattainable in practice); however, the nomenclature uses the term zero characteristic in the direction of interference. To achieve this, there are the so-called algorithms for controlling the characteristics of the adaptive array. Most control algorithm solutions can be grouped into four categories (Figure 5).  
The first one requires a receiver on each of the active elements of the array. In this approach, a correlation matrix of signals received on individual active elements is formed to obtain optimal weights that ensure the maximum useful signal to interference and noise ratio (SINR). There are many solutions of this type, but they all rely on knowing the signals received on individual elements. Using such a number of receivers is expensive and requires constant calibration [20; 21].  
The second category is a system that relies on knowing the source of the disturbance and on the characteristics of the attenuators and phase shifters being precisely predictable.  
Figure 5: Adaptive algorithm for the radiation pattern of a sector antenna.  
Having this information, the system mathematically calculates the weights to create a zero characteristic in the direction of the disturbance. However, this method is impractical because the source of the interference is usually unknown.  
The third category involves randomly determining possible settings for the weights of array elements. This approach is very time-consuming and therefore impractical in real-time systems.  
Finally, the fourth approach is based on the principle of implementing a mathematical weight optimization algorithm in order to obtain the minimum total power at the output of the adaptive antenna. In this method, a gradient vector is most often formed by selecting the weight values to obtain the minimum total power at the output [22].
## 3 Results
### Sample-Encoding Kohonen Network  
The Kohonen map is trained using a wavelet representation of kinematic data extracted in each plane separately (sagittal, frontal, and transverse planes). The wavelet family and the relevant planes have been evaluated by leave-one-out cross validation. This led to a data representation using the abduction/adduction and internal/external planes, and a level 6 Daubechies DB1 decomposition to four coefficients, i.e., the kinematic data is now represented by (feature) vectors of dimension 4.  
The network parameters in the experiments are \(\epsilon_{i}=0.1\), \(\epsilon_{f}=0.01\), \(\sigma_{i}=3\), \(\sigma_{f}=1\). Figure 5 shows, for two pathologies classification (FR and FT), how the recognition rate varies with the number of network nodes, and with the number of the network training algorithm iterations. The best classification rate is 90.47%, obtained with an \(8\times 8\) network map (64 nodes) after 50 iterations. The corresponding confusion matrix, illustrated in Table 2, shows a balanced classification rate per class (20/21 in FR class and 18/21 in FT).  
Recall that applicability of the Hotelling statistic requires that the dimension of the data vector space be less than the number of data vectors in the sample for which this statistic is written. In our case, a patient data sample contains between 0 to 15 vectors. Therefore, we must retain no more than nine coefficients of representation when we reduce dimensionality. The best performing set of coefficients in our dimensionality reduction experiments was of size 4. We could have safely retained up to nine coefficients. However, using more coefficients than we did does not necessarily translate to better classification. For instance, a nine-coefficient representation of the data gives a lower 88% classification accuracy.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**RealPredicted** & \(C_{1}\):FR & \(C_{2}\):FT & \(\tau\)**(\%)** \\ \hline \(C_{1}\):FR & 20 & 1 & 90.47 \\ \(C_{2}\):FT & 3 & 18 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix corresponding to the proposed Kohonen two class classification method.  
Figure 5: Variation of the recognition rate vs. the number of nodes and the number of iterations in the sample-encoding Kohonen network. The size of a circle is proportional to the classification rate it represents.  
In Table 3, we present the results for the confusion matrix of the three-class classification problem. As illustrated in the table and as expected, the three-class classification problem is much more difficult than the main treated problematic. For this secondary experiment, the best achieved classification rate is 71.43 %.