esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Transcriptional Regulation in Integrons
### Regulation of Integrase Transcription in MI and CI by Stress Response Mechanisms
#### 2.5.1 The Role of SOS Response
disrupts the LexA binding site, preventing LexA from binding to P\({}_{\text{int}}\), which abolishes integrase regulation (Figure 8). Therefore, this phenomenon suggested that LexA repression may be abolished under strong selective pressures to promote variability and increased basal transcription levels of antibiotic resistance genes. In this case, the maintenance of integrase activity providing cassette shuffling and phenotypic innovation must be preferable to the loss of integron functionality under changing selective environments, such as the clinics [19]. Despite these specific cases, there is a clear selective pressure regarding the conservation of the LexA-dependent integrase regulation. For example, in the CIs, this pressure is represented by the need in stabilizing these large platforms in the genome [72]. In MIs, this selective pressure might favor inactivated integrins that are able to run into recombination activity when adaptive innovation is needed [19]. It means that the SOS-dependent regulation of integrase and antibiotic resistance gene expression leads to gene silencing at no biological cost until activation by selective antibiotic pressure, while other adaptive traits continue to be expressed [17; 105]. Another example of the link between the SOS response and the evolution of antibiotic resistance is the association of this system with the expression of \(qnrB\) genes. The \(qnrB\) alleles are frequently found associated with ISCR1 in complex class 1 integrins and confer resistance to quinolones, such as ciprofloxacin, by protecting the antibiotic targets. An internal promoter region is conserved in all \(qnrB\) alleles as well as a sequence homologous to the gammaprotebacteria consensus LexA-protein-binding site found in the vicinity of these \(qnrB\) promoters. In this case, as found for \(intl\) genes, the \(qnrB\) expression is negatively regulated by the activity repression of LexA [105]. Interestingly, the SOS system is known to be induced by ciprofloxacin [102], which upregulates the expression of its own resistance determinant (\(qnrB\)).
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 2 Materials and Methods  
### Refik Cesur Intersection Structure  
Polatth is located in the western part of Ankara province and is the closest district to Ankara on the Eskisehir-Ankara State Highway. The methods in this study are applied at Refik Cesur Intersection in Polatth district. Refik Cesur Intersection is a signalized intersection on the Eskisehir-Ankara State Highway. An average of 55,000 vehicles travel through Refik Cesur Intersection daily. The satellite photo of the intersection is seen in Figure 1.  
Refik Cesur Intersection has four directions. Eskisehir and Ankara directions consist of four and five lanes each. One of these lanes is a left-turn lane. The exit directions from Eskisehir and Ankara intersections are three lanes each. In addition, Refik Cesur Street and Selimiye Street directions consist of two lanes each. Refik Cesur Street and Selimiye Street exit directions from the intersection are two lanes each. The intersection directions and traffic flows are shown in Figure 2.  
Figure 1: Refik Cesur Intersection satellite image.  
Figure 2: Refik Cesur Intersection directions and traffic flows.  
There are total of six traffic flows at Refik Cesur Intersection. These traffic flows are named Q5, Q6, Q7, Q8, Q9, and Q10. The methods in this study are applied on a traffic flow basis. Q5 and Q7 traffic flows are from Ankara direction. Similarly, Q6 and Q8 traffic flows are from Eskisehir direction, Q9 traffic flow is from Refik Cesur Street direction, and Q10 traffic flow is from Selimiye Street direction.
## 3 Methodology: Proposed SybilSocNet Algorithm
After the seventy one data blocks are formed, a matrix is created from each block, similar to the one shown in Figure 4.  
Next, we use these 71 matrices to train the SVM, KNN, and Random Forest algorithms. The first column, in green, represents the user ID. The remaining columns, in white, indicate the other users who follow the user from the first column. The last column, in yellow, labels users (from the first column) as '1' if they are detected as Sybil or '0' if they are considered legitimate users. This matrix includes all users and their connections. Figure 3: Portion of adopted dataset. The pseudocode for the creation of these 71 matrices is portrayed in Algorithm 2. For example, the first row shows data for user 1, followed by 2, 7, 90, and 111, which are labeled as Sybil users. The second row corresponds to user 5, followed by user 7, which are legitimate users. The matrix continues to include all users in the dataset [47] and their connections. In the second-to-last column (on the right side of the table in Figure 4), an \"X\" indicates either the node with the highest index or zero if the node is not followed. This is denoted by \(X_{i}\), where \(X_{i}\in\{0,M\}\), and \(M\) is the value of the highest index among the sorted nodes before being included in the matrix (See Algorithm 2). ```
Input :BlocksOfData Output :Matrices  Matrix \(\leftarrow\) Empty array to store data for Matrix;  nodeIndex \(\gets\) 0; for\(i\gets 1\)to 71 do whileNot at end of BlocksOfDatado  Matrix\({}_{i}\leftarrow\) read node at nodeIndex;  Matrix\({}_{i}\leftarrow\) append currentNode relation of everyColumn;  Matrix\({}_{i}\leftarrow\) currentNodeIndex for every Row; ifnodeIndex is at the end of BlocksOfData\({}_{i}\)then break;  end if  end for Save Matrix\({}_{i}\) to a file;  MatrixIndex \(\gets i+1\);  end for returnMatrices;
```  
**Algorithm 2**Pseudocode for the Creation of Matrices
## 2 Methods
### Definition and Calculation of the Fitness
#### 2.3.3 Distance Fitness  
The \"distance fitness\" means the level of effectiveness that changes depending on how far the person is from the task at hand. It is calculated using the following formula:  
\[DF_{i}\big{(}a_{i,j},t_{m}\big{)}=\min(DH(i,m),DR(j,m)) \tag{9}\]  
The adaptabilities of distance from the \(i\)th astronaut and \(j\)th lunar robot to the \(m\)th mission are represented by \(DH(i,m)\) and \(DR(j,m)\). Agents that have higher fitness values are deemed more suitable for the task. When the astronauts in the capsule are selected, the distance fitness is calculated using the robot's moving distance, and the degree of human-robot collaboration is designed to describe how closely astronauts and robots collaborate. When selecting an astronaut outside the capsule, the distance fitness is calculated using the larger moving distance between the astronaut and the robot. The detailed calculation of the mobility distance fitness is as follows:  
\[\left\{\begin{array}{c}DH_{in}(i_{in},m)=\frac{HRcd(i_{in},m)}{\max HRcd(i_{ in},m)}\qquad i_{in}e[1,I_{in}]\\ DH_{out}(i_{out},m)=1-\frac{DH_{out}(i_{out},m)}{\max(DH_{out}(1,m),\cdots,DH_{ out}(i_{out},m))}\\ DR(j,m)=1-\frac{DR(j,m)}{\max(DR(1,m),\cdots,DR(J,m))}\end{array}\right. \tag{10}\]  
The range of HRcd for human-robot collaboration is taken from 0.0 to 1.0. The lower the HRcd, the higher the autonomy of the robot. When HRcd is 0.0, the robot is considered capable of performing the task entirely on its own, without the operation of the astronaut who is supposed to do nothing but confirm the results of the operation. When HRcd is 1.0, the robot is not considered capable of performing the task on its own at all, and the astronaut should teleoperate with the robot for the entire mission.  
To calculate the distance traveled by an astronaut or a robot outside of the capsule during a mission, we use the straight line distance between two locations on the unstructured lunar surface, as detailed maps are not available. The straight line distance between the agent and the mission location is used to calculate the distance traveled by an astronaut or robot outside the capsule to the mission.  
In order to account for the increase in distance around a barrier that is not accounted for by the calculation in a straight line, the dynamic HRL algorithm is used in this study. This algorithm is able to continuously compute the task distance in real-time, accounting for changes in time and the movement of the robot's position, allowing for simultaneous running, computation, and planning. Additional details on the algorithm can be found in the following subsection.  
Figure 5: Environment fitness of different robots in various environments.
## 3 Principle and Processing Flow of Atmospheric Correction Algorithm
### Adjacency Effect Correction  
Equation (5) assumes a homogeneous surface, but real-world surfaces are often complex and heterogeneous. This necessitates the consideration of the influence of adjacent pixels, which can be modeled as the convolution of the target surface's radiative field with the atmospheric point spread function (PSF). Based on the spatial resolution of MSC, the weighting function of the atmospheric point spread function is calculated to determine the size of the adjacency effect window.  
\[\rho_{M}=\sum_{x,y=1}^{N}\rho_{1}(x,y)P(x,y) \tag{6}\]  
Here, \(\rho_{M}\) represents the background reflectance around the target pixel, \(N\) is the size of the adjacency effect window, and \(P(x,y)\) is the weighting function describing the contribution of a point at a distance from the target pixel [8]. For the HJ-2A/B satellite's MSC, considering its spatial resolution and computational efficiency, \(N=10\) is selected.  
Based on the calculated surface composite reflectance (\(\rho_{1}\)) and background reflectance (\(\rho_{M}\)), the true surface reflectance (\(\rho_{t}\)) can be computed as:  
\[\rho_{t}=\rho_{1}+(\rho_{1}-\rho_{M}) \tag{7}\]  
where \(q\) is the adjacency effect correction coefficient, derived from the ratio of diffuse to direct atmospheric radiation. This correction ensures that the influence of scattered light from adjacent areas is accounted for, improving the accuracy of surface reflectance retrieval in heterogeneous regions.
###### Abstract  
Phytoremediation supported by bioaugmentation is a promising method considered for cleaning up areas polluted with petroleum hydrocarbons. In this study, phytoremediation was carried out using _Echinacea purpurea_ as a phytoremediant on two types of soil: Soil DW--aged soil taken from an excavation pit, Soil OS--soil taken from an oil spill area. The tests for each soil were carried out in six test systems (non-inoculation, inoculation with the B1 microbial consortium, inoculation with the B2 microbial consortium, inoculation with the B1 microbial consortium with the addition of \(\gamma\)-PGA (\(\gamma\)-poly glutamic acid), inoculation with the B2 microbial consortium with the addition of \(\gamma\)-PGA and inoculation with the \(\gamma\)-PGA solution) for 6 months. The effectiveness of the remediation treatments used was assessed based on chromatographic analyses of soil and plant material (roots, shoots) and toxicological analyses using four types of toxicological tests (PhytotoxkitTM (MicroBioTests Inc., Gent, Belgium), OstracodrokitTM (MicroBioTests Inc., Gent, Belgium), Mircotoxo(r) Solid Phase Test (Modern Water Inc., New Castle, DE, USA), MARA (NCIMB Ltd., Aberdeen, UK). The research conducted showed that the most effective method of bioremediation of soils contaminated with petroleum hydrocarbons was phytoremediation supported by bioaugmentation with the microbial consortium B2 with \(\gamma\)-PGA, which allowed for reducing the concentration of total petroleum hydrocarbons (TPHs) and polycyclic aromatic hydrocarbons (PAHs) in the tested soils by 53.98% and 49.54% (Soil DW-5) and 60.47% and 37.55% (Soil OS-5), respectively. In turn, the lowest bioremediation efficiency was recorded in non-inoculated systems, for which the concentration of TPHs and PAHs at the end of the study decreased by 18.40% and 16.14% (Soil DW-1) and 21.87% and 18.20% (Soil OS-1), respectively. The results of toxicological analyses confirmed the relationship between the concentration of TPHs and PAHs in the soil and its toxicity level.  
bioremediation; _Echinacea purpurea_; toxicological tests; microbial consortium (biopreparation); petroleum hydrocarbons; phytoremediation
# Impacts of Land-Use Change from Primary Forest to Farmland on the Storage of Soil Organic Carbon
## 4 Discussion
### Effect of Soil Structure on SOC Storage  
Soil aggregates are the basic building blocks of soil structure and play a crucial role in protecting SOC, which is a key factor in SOC fixation. Tillage, however, can be detrimental to soil structure, disrupting aggregates and altering soil temperature and moisture levels, leading to accelerated oxidation and mineralization and resulting in a decrease in organic matter content [23]. Several studies [24; 25] have shown that, compared to intensive tillage, conservation tillage or no-tillage practices can provide better physical protection for carbon components, resulting in greater accumulation of soil organic carbon. For instance, [PERSON] (1986) [26] proposed that macro-scale polymers (particle size \(>\) 250 \(\upmu\)m) contain more unstable SOC and are easily lost under high disturbance. [PERSON] et al. (2022) [27] estimated that conversion to conservation tillage could sequester about 43 tons of carbon annually in farmland soil across Europe, suggesting that a transition to no-till practices could offset all fossil fuel carbon emissions in European agriculture. Additionally, residue and much cover can reduce soil erosion and evaporation and improve soil aggregate stability [28]. Consequently, soil aggregates, soil organic matter content, and soil physical properties all influence soil structure, thus affecting SOC.
## 4 Nutritional and Bioactive Potential of Fresh Fruit and Vegetable Waste
### Fresh Fruits
#### 4.1.3 Citrus Fruit  
Citrus fruits, mainly orange and lemon, are very popular worldwide. These fruits are rich in carotenoids, flavonoids, terpenes, limonoids, and many other bioactive compounds of nutritional and nutraceutical value, including polymethoxylated flavones (PMFs). These flavones were shown to elicit several bioactive effects against metabolic disorders, atherosclerosis, inflammation, neuroinflammation, cancer, and oxidation (reviewed in [164]). In addition, citrus essential oils are rich in limonoids and terpenes, which are widely appreciated for their antioxidant, antimicrobial, and flavoring properties with broad industrial applications [164]. Citrus peels are the main residue originating from citrus fruit, being very rich in dietary fibers (cellulose, hemicellulose, and pectin), bioactive compounds (phenolic compounds, flavonoids, and carotenoids), and vitamin C [90,165,166]. A myriad of bioactive compounds has been reported in citrus peels. A study recently conducted by [PERSON], et al. [167], for instance, revealed that kinnow peel contains six flavones, eight favon-3-ols, five flavanones, four favan-3-ols, one anthocyanin, phenolic acids, and limonoids. Other reports point to citrus peels as promising sources of naringin, \(\beta\)-carotene, hesperidin, neohesperidin, and pectin [168,169]. Tangerines grown in Madeira,for instance, have a very intense and unique aroma which is mainly due to the presence of high amounts of volatile bioactive compounds in their peels, including thymol and dimethyl anthranilate [170]. Overall, citrus peels gained considerable attention due to their content in essential oils, mainly constituted by a mixture of volatile compounds such as terpen (limonene) and oxygenated derivates such as aldehydes (citral), alcohols, and esters [89; 169].
## 4 Conclusions and Future Direction  
In this paper, DoA estimation methods were proposed for a spherical antenna array with unknown mutual coupling. These two intensity-based methods (PV and SPV) were compared with baseline methods in the literature. The PV method was demonstrated to be more computationally efficient, and accurate up to \(1^{0}\) across SNR (20-40 dB), and within \(3^{0}\) in the most severe of the considered scenarios (SNR of 10 dB). The second method, SPV, employed frequency smoothing along with a spatial covariance matrix subspace decomposition. It is more robust to noise and interference than the PV. The SPV runs faster than the PWD-SRP and DPD-MUSIC, but is marginally less accurate. The SPV's  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & **PV (s)** & **SPV (s)** & **PWD-SRP (s)** & **DPD-MUSIC (s)** \\ \hline
10 & 0.018 & 0.024 & 0.153 & 0.161 \\ \hline
50 & 0.201 & 0.254 & 0.311 & 0.323 \\ \hline
75 & 0.281 & 0.291 & 0.374 & 0.393 \\ \hline
100 & 0.632 & 0.643 & 0.773 & 0.787 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elapsed run-time for each signal for the DoA estimation of the DoA search grid resolution at various snapshot numbers.  
Figure 6: Gross error performance versus SNR using measured data for different methods.  
good performance was demonstrated in the form of an interchange between measurement number and missed detections in a multiple-sources scenario. Hence, the SPV method is a good candidate for tracking applications. However, PVs and SPVs are adequate in practice for stationary single and multiple sources, and find applications in spacecraft, aerospace and other applications where SAA is found to be useful.  
Despite the outcomes of this work, there are still issues that require further research. For example, the presented methods may further be extended to antenna array calibration and time-delay measurements in different applications, or their response to key characteristics that unavoidably cause alterations in the practical use of SAA could be examined, and a qualitative comparison could be provided with related works in the literature, based on their effectiveness, accuracy, and computational complexity.  
Conceptualization, O.J.F.; methodology, O.J.F.; software, O.J.F.; validation, O.J.F. and T.S.; formal analysis, O.J.F.; investigation, O.J.F.; resources, T.S.; writing--original draft preparation, O.J.F.; writing--review and editing, O.J.F.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Faculty of Engineering and the Built Environment, University of Johannesburg.  
This research is supported in part by University Research Committee (URC) of the University of Johannesburg, South Africa.  
The authors declare no conflict of interest.
## 4 Methodology and Procedure
### Structural Models  
Three standard 3-, 9-, and 20-story office buildings were selected for the study. These buildings were designed as part of the SAC Steel Project [47] and were assumed to be situated on stiff soil of a high seismic zone. Bi-dimensional (2D) representations of these buildings, which consist of the perimeter moment resisting frames (PMRF), are specifically used as the models of the study. The low-, mid- and high-rise models, which are defined as Models 1, 2 and 3, have fundamental periods of 1.03 s, 2.38 s and 4.07 s, respectively. Some details of the models can be seen in Figures 1-3. The structural sections of the vertical and horizontal members can be seen in Tables 1 and 2; except for the exterior columns of Model 3, all sections consisted of wide-flange shapes (W-sections). The designation of the structural sections is following the AISC manual [48]. For example, for a W section, the term \"14 \(\times\) 257\" corresponds to a W-shape that is nominally 14 in deep and weighs 257 lb/ft. All dimensions of the box sections of Model 3 are in inches. As stated earlier, each \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Story**} & \multicolumn{2}{c}{**Columns**} & \multirow{2}{*}{**Girders**} \\ \cline{3-3} \cline{5-5}  & & **Exterior** & & **Interior** \\ \hline \multirow{4}{*}{\(1\)} & 1 & 14 \(\times\) 257 & 14 \(\times\) 311 & 33 \(\times\) 118 \\ \cline{2-5}  & 2 & 14 \(\times\) 257 & 14 \(\times\) 311 & 30 \(\times\) 116 \\ \cline{2-5}  & Roof & 14 \(\times\) 257 & 14 \(\times\) 311 & 24 \(\times\) 68 \\ \hline \multirow{4}{*}{2} & Basement & 14 \(\times\) 370 & 14 \(\times\) 500 & 36 \(\times\) 160 \\ \cline{2-5}  & 1 & 14 \(\times\) 370 & 14 \(\times\) 500 & 36 \(\times\) 160 \\ \cline{2-5}  & 2 & 14 \(\times\) 370 & 14 \(\times\) 500 & 36 \(\times\) 160 \\ \cline{2-5}  & 3 & 14 \(\times\) 370 & 14 \(\times\) 545 & 36 \(\times\) 135 \\ \cline{2-5}  & 4 & 14 \(\times\) 370 & 14 \(\times\) 455 & 36 \(\times\) 135 \\ \cline{2-5}  & 5 & 14 \(\times\) 283 & 14 \(\times\) 370 & 36 \(\times\) 135 \\ \cline{2-5}  & 6 & 14 \(\times\) 283 & 14 \(\times\) 370 & 36 \(\times\) 135 \\ \cline{2-5}  & 7 & 14 \(\times\) 257 & 14 \(\times\) 283 & 30 \(\times\) 99 \\ \cline{2-5}  & 8 & 14 \(\times\) 257 & 14 \(\times\) 283 & 27 \(\times\) 84 \\ \cline{2-5}  & Roof & 14 \(\times\) 233 & 14 \(\times\) 257 & 24 \(\times\) 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wide-flange sections (W-shapes) used in Models 1 and 2.
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 4 Results
### Statistical Feature Analysis
#### 4.6.2 PCA and Normalization  
Another approach is to use PCA to reduce the dimensionality of the features and keep only the ones that contribute effectively. However, the main problem with this approach is that there will be a need to normalize the features, before applying PCA. This need stems from the fact that the individual features lie at different scales, and since PCA is sensitive to the variation of the data, it will not be correctly applied unless they are normalized. To underline the effect of the normalization, we will also produce results with the normalized features without using PCA, which will be reported as \"DCNN norm\".  
We applied PCA with 40 principal components to imitate the 40 statistical features. A guide for the number of dimensions to keep is the explained variance ratio of each component. In our case, the first six axes account for 98.9% (\(42.97+27.49+18.65+4.73+2.84+2.22\)) of the total variance. Consequently, we kept the first six dimensions, and the respective results will be reported as \"DCNN PCA-6\".  
The mean and standard deviation of the accuracy after running 10 trainings with different initializations for each approach can be seen in Table 14. The results were acquired with a window size of 200. It is clear that the normalization of the features significantly drops the performance of the network. The features keep important information about the global statistics of the input window, such as the mean and the standard deviation. These features lie at different scales and are distorted by the normalization. This results in loss of information, which, in turn, explains the drop of performance. Consequently, even though using PCA, the dimensionality is reduced, this only marginally increases the performance, since the normalized features do not offer significant supplemental information. It is telling that the accuracy with normalized features--93.69%--achieves inferior or at best equal performance even compared against the one with no statistical feature input--93.73% (Table 13).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Average Accuracy** & **Standard Deviation** \\ \hline  & & **200** \\ \hline DCNN-0 & 93.73\% & 0.11\% \\ DCNN-3 & 94.06\% & 0.14\% \\ DCNN-6 & 94.08\% & 0.19\% \\ DCNN-9 & 94.24\% & 0.09\% \\ DCNN-10 & 94.28\% & 0.07\% \\ DCNN & 94.36\% & 0.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average accuracy scores and their standard deviation of the original train/test split of the WISDM dataset for varying numbers of statistical features.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Average Accuracy** & **Standard Deviation** \\ \hline  & & **200** \\ \hline DCNN norm & 93.69\% & 0.3\% \\ DCNN PCA-6 & 93.78\% & 0.14\% \\ DCNN & 94.36\% & 0.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 14: Average accuracy scores and their standard deviation of the original train/test split of the WISDM dataset for different statistical feature processing choices.