esa-sceva/satcom-chunk-collection huggingface dataset:
# Trepenoid Metabolic Engineering in Photosynthetic Microorganisms
## 3 Photosynthetic Microorganisms as Terpenoid Production Hosts
### Cyanobacteria
Therefore, the redox availability increases flexibility and makes redox enzyme expression more straightforward. One example is the expression of cytochrome P450s in cyanobacteria by using ferredoxin as a direct link to a photosynthetic redox power. This strategy not only results in active enzymes but also may increase overall photosynthetic capacity [105; 106; 107]. Cyanobacteria seems to have efficient regulation mechanisms that buffer the effects of metabolic perturbations to maintain homeostasis in primary metabolism. This can be both advantageous (as important native branching pathways are not affected by the heterologous ones) but may also hinder metabolic engineering [82]. Another noteworthy observation is that different cyanobacterial strains behave very differently. It is important to keep in mind that, despite their similar morphology, cyanobacterial species are genetically divergent and this may result in a varied behavior in terpenoid production. The cyanobacterial species diversified around 2.5-2.1 billion years ago [108], which means that, in practice, _Synechococcus elongatus_ and _Nostoc_ sp. PCC 7129 are more distant phylogenetically to each other than humans are to plants. This may explain significantly different production profiles in the presence of engineering between different species. These studies highlight that, while native MEP pathway flux in cyanobacteria may be relatively high, engineering heterologous terpenoid production still faces challenges in these organisms. Competition with native terpenoid production as well as a relatively poor understanding of regulatory mechanisms are two key challenges that require further attention. Despite this, the ability of these organisms to tolerate and compensate for heterologous terpenoid production is extremely promising.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Clustering  
In order to improve the accuracy and evaluation of dynamic routes for EVs, the clustering approach is used for charging stations [28; 29]. Clustering helps to group nearby charging stations, allowing for a more streamlined decision-making process when selecting optimal stops along a route. By organizing stations into clusters, the algorithm can quickly identify which areas have a higher concentration of charging options, thereby reducing the search space and improving computational efficiency [30]. Additionally, clustering can account for regional demand variations, ensuring that EV drivers have access to charging infrastructure that aligns with their travel needs and patterns [31]. Charging stations are grouped into clusters using the K-means algorithm [32], and each route point is assigned to the nearest cluster.  
One potential challenge in clustering charging stations is the variability in demand across different times of the day or week, which can lead to imbalances in cluster utilization. Moreover, changes in station availability due to maintenance or other disruptions can also impact the reliability of the clusters over time. Accurate queuing information is crucial for optimizing EV routes, as it directly impacts the travel time and efficiency of charging station utilization. When EV drivers have access to precise queuing data, they can make informed decisions about which charging stations to visit, minimizing wait times. Additionally, accurate queuing information helps maintain the balance of demand across  
Figure 1: Route representation \((RP_{i})\), \((EP_{i})\) and charging stations.  
clusters, preventing certain stations from becoming overwhelmed while others remain underutilized. In order to enhance the accuracy of our model, the queueing time at a charging station was only treated as valid when it was part of the same cluster as the current route point. For charging stations in other clusters, an average queuing time was assumed. Figure 2 illustrates the division of charging stations into clusters. The silhouette score was used to determine the optimal number of clusters (\(K\)).
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 4 Performance Evaluation
### Evaluation Metrics  
The four machine learning methods were validated based on the root mean square error (RMSE), precision and recall scores, and F1 scores.  
We first describe the _RMSE_. The _RMSE_ computes the variance between values predicted by a hypothetical model and the real values. In other words, it measures the standard-of-fit between the actual data and the predicted model. It is expressed as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}{n}}, \tag{9}\]  
In Equation (9), \(\hat{y_{i}}\) is the predicted variable, \(y_{i}\) the actual variable, and \(n\) the number of observations. The _RMSE_ is always non-negative. In general, a lower _RMSE_ is better.  
Precision, recall, and F1 score are measurements to check the accuracy of training models. Specifically, precision is a measure of result relevancy, whereas recall represents the degree to which many genuinely relevant results are returned. Meanwhile, the F1 score is calculated using the values obtained from precision and recall. Precision, recall, and F1 score are expressed as follows:  
\[\begin{split}& Precision=\frac{\sum_{i=1}^{n}\text{TP}/(\text{TP}+ \text{FP})}{n}\\ & Recall=\frac{\sum_{i=1}^{n}\text{TP}/(\text{TP}+\text{FN})}{n}\\ & F_{1}\,\text{{score}}=\frac{\text{TP}}{\text{TP}+\frac{1}{2}( \text{FP}+\text{FN})}\end{split} \tag{10}\]  
In Equation (10), _TP_, _FP_, and _FN_ indicate true positive, false positive, and false negative, respectively; \(n\) represents the total amount of observation data.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
## 4 Discussion
### Performance on Different Types of Tumor  
We can see that these models performed better for brain metastases, meningiomas, and schwannomas, where there were more than 300 cases each. They performed best for schwannomas, probably because most of these are vestibular ones, whose locations are always around internal auditory meatus.  
On the other hand, these models performed poorly for pituitary tumors, AVMs, and other tumor types. Besides the relatively small number of cases for training, pituitary tumors and AVMs are not always readily visible for humans using only the T1+C series. For example, dynamic contrast-enhanced MRI may be required to visualize pituitary tumors. AVMs are sometimes not visible even using time-of-flight (TOF) MRI, so computed tomography angiography and/or digital subtraction angiography may be required for target contouring.
## 3 Comparative Analysis of Different Binarization Methods
### Color-Sensitive Binarization Methods
#### 3.1.2 Binarization of Background Differences  
Due to the simple structure, less image information and sensitive background noise, the traditional gray binarization method cannot completely extract the object outline of the luggage package in the X-ray image. To solve this problem, this paper proposes a binarization method based on background difference, which introduces more background information in the X-ray image into the algorithm to solve the problem of background noise sensitivity. The difference map is calculated from the grayscale image. The value of each pixel in the difference map is shown in Formula (2):  
\[Gray_{diff}\ =\ \Big{|}Gray-Gray_{bg}\Big{|}, \tag{2}\]  
where _Gray_ is the gray of the pixel point and \(Gray_{bg}\) is the gray of the background. Figure 8 shows the gray difference map and the binary gray difference map. As can be seen from Figure 8, although the wrapping contour of the binary gray difference map is clearer than that of the gray difference map, it cannot cover the entire wrapping area.  
In order to exclude the case that different RGB values may correspond to the same gray value, Formula (3) is used to calculate the difference map:  
\[Gray_{diff}\ =\ \frac{r\times\Big{|}Red-Red_{bg}\Big{|}+g\times\Big{|}Green- Green_{bg}\Big{|}+b\times\Big{|}Blue-Blue_{bg}\Big{|}}{r+g+b}, \tag{3}\]  
where _Red_, _Green_ and _Blue_ are the RGB values of the pixel, \(Red_{bg}\), \(Green_{bg}\) and \(Blue_{bg}\) are the RGB values of the background, and \(r\), \(g\) and \(b\) are the preset weight parameters.  
It can be seen from Figure 9a,b, in the gray difference map calculated with RGB values that the difference between background and object is more obvious, which can basically cover the whole luggage package area, and it is easier to find the appropriate threshold for binarization processing. However, background noise is also obvious in the figure. The open operation of the RGB difference map after binarization can eliminate the noise in the image, and the detection effect of the luggage package contour is better.  
Figure 8: Gray and binarized gray difference image.
## 4 Results
### Prediction Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**No. of Tiles**} & \multicolumn{4}{c}{**Total \% of Tiles**} \\ \cline{2-7}
**Dataset** & **Columbia** & **Las Vegas** & **Natanz** & **Columbia** & **Las Vegas** & **Natanz** \\ \hline D1 & 222 & 113 & 261 & 8.78\% & 21.65\% & 7.13\% \\ D2 & 315 & 203 & 335 & 12.46\% & 38.89\% & 9.15\% \\ D3 & 407 & 277 & 410 & 16.10\% & 53.07\% & 11.20\% \\ E1 & 466 & 162 & 604 & 18.43\% & 31.03\% & 16.50\% \\ E2 & 553 & 246 & 655 & 21.88\% & 47.13\% & 17.90\% \\ E3 & 636 & 313 & 710 & 25.16\% & 59.96\% & 19.40\% \\ F1 & 336 & 136 & 444 & 13.29\% & 26.05\% & 12.13\% \\ F2 & 420 & 224 & 518 & 16.61\% & 42.91\% & 14.15\% \\ F3 & 500 & 291 & 593 & 19.78\% & 55.75\% & 16.20\% \\ G1 & 816 & 224 & 1153 & 32.28\% & 42.91\% & 31.50\% \\ G2 & 880 & 295 & 1204 & 34.81\% & 56.51\% & 32.90\% \\ G3 & 941 & 347 & 1259 & 37.22\% & 66.48\% & 34.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: This table provides more detailed information on the composition of the training datasets introduced in Table 6.  
the use of training sets that combine high change tiles with representative cluster members continue to produce the best classification results. Precision and accuracy values hover around the 50% mark, while overall accuracy is between 70-80% for the Columbia and Las Vegas datasets. The Natanz dataset represents an anomaly, because of the relatively small amount of actual change in the dataset; precision and recall values typically top out no higher than 30%, but overall accuracy is in the mid-90% range. This occurs because the classifier is able to accurately predict a large number of true negative tiles within this pair. Generally, the results of the CART decision tree classification shown in Table 10 indicate a slight decrease in precision, recall and accuracy compared to those of the SVM. The notable exception was that the D and E series datasets showed a slight improvement using the CART decision tree compared to SVM. Finally, change prediction results using the random forest classifier shown in Table 11 are the best among the four algorithms presented.
## 2 Challenges and Routing in 6G Networks
### Challenges of Routing
#### 2.1.3 High Packet Loss Regarding Traditional Routing  
It is not possible for traditional routing techniques to respond to the dynamic topology of 6G networks, communication occurring in multiple environments at any time, and ultra-reliable and low-latency targets. Traditional routing techniques are limited when it comes to working effectively in dynamic environments. High road loss and congestion problems could cause major losses in the performance of next-generation communication networks. Existing routing approaches need to be improved for 6G integration, and new routing algorithms need to be studied. Efficient and sustainable routing structures are of great importance for 6G and satellite networks. Various studies have been carried out to reduce transmission delay, road loss, and congestion problems. In these studies, machine learning, deep learning, and deep reinforcement learning algorithms were used, together with new routing approaches for efficient traffic management [29; 55; 58].
## 3 Results and Discussions
### Mechanism Analysis of RAP Emulsification-Separation
#### 3.7.3 Influence of Aggregate Size  
The PL of RAP varies with different sizes, and the size of the aggregate mainly affects its collision efficiency, which, in turn, affects the washing effect of the asphalt. The collision and shear behaviors between aggregates of different sizes during emulsification-separation are different; so, it is necessary to study the emulsification-separation effects for different particle sizes. Figure 18 shows the mechanism of RAP separation for different sizes. After comparing the emulsification-separation effects on a single particle size with the extraction effect, the emulsification-separation effect was found to be excellent when dealing with coarse aggregates larger than 4.75 mm. When processing 2.36 mm fine aggregate, a large amount of oil sand adheres to it. Owing to the larger specific surface area of fine aggregates, their asphalt film is thicker. After being softened by heating, the main effect of shear collision occurs on the softened asphalt, and only a portion of the asphalt is floated up. By studying the mixing of RAP of different sizes, it was found that the best treatment effect was achieved when mixing 13.2 and 9.5 mm RAP aggregates in a 1:1 ratio. After analysis, when there were more coarse aggregates larger than 9.5 mm in the mixed system, their dispersion effect was found to be better. When dealing with other gradations of RAP, it is recommended to mix the nominal maximum aggregate particle size with the RAP of the next level size for dispersion, for which a theoretical optimal solution can be obtained. Future research can investigate the peeling of the asphalt film on the surface of fine aggregates and elucidate the mechanism of differential dispersion effects between coarse and fine aggregates under shear fields.
###### Abstract  
Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients' survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification.  
kidney cancer; subtype classification; miRNA as biomarker; machine learning; TCGA
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.3 Brain Tumor Images (TCGA)  
The brain tumor image dataset contains brain magnetic resonance imaging (MRI) together with manual fluid-attenuated inversion recovery (FLAIR) abnormality segmentation masks. The images were obtained from The Cancer Imaging Archive (TCIA). They correspond to 110 patients included in The Cancer Genome Atlas (TCGA) lower-grade glioma collection with at least FLAIR sequence  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Application** & **No. of Images** & **Input Resolution** & **Modality** \\ \hline CVC-ClinicDB [29] & Polyp & 612 & \(256\times 192\) & Endoscopy \\ VIP-CUP18 [30] & Lung tumor & 5204 & \(256\times 256\) & CT \\ TCGA [31] & Brain tumor & 1373 & \(256\times 256\) & MRI \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the datasets.  
Figure 5: Example of images and ground truth images from three dataset.  
and genomic cluster data available [31]. This dataset contains 1373 brain images with a resolution of \(256\times 256\) pixels.
# Detecting Suspicious Texts Using Machine Learning Techniques
###### Abstract  
Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents has been growing enormously in recent years through instant messaging, social networking posts, blogs, online portals and other digital platforms. Unfortunately, the misapplication of technologies has increased with this rapid growth of online content, which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio, or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier \"tf-idf\" with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.  
Keywords:natural language processing; suspicious text detection; Bengali language processing; machine learning; text classification; feature extraction; suspicious corpora +
Footnote †: journal: Applied Sciences
The use of PGPR added in the biofertilizers for oil palm growths has been reported in the literature [23; 24; 25; 26; 27]. The PGPRs, colonizing inside the root surface, would express plant-growth-promoting and protective functions, and stimulate plant development [28]. PGPRs are also directly associated with N\({}_{2}\) fixation, growth hormones, phosphate solubilization, and siderophore formation [29]. They can also eliminate phototoxic chemicals produced by harmful microbes [30]. N\({}_{2}\)-fixing and P-solubilizing bacteria are critical in plant nutrition because they increase P absorption [31]. PGPR can also aid plant growth by delivering soluble phosphate, which is transformed from insoluble mineral phosphates by acidification, or by mobilizing other essential nutrients [32; 33]. As a result, PGPRs may be employed to improve the nutrient availability, plant growth, and yields of the oil palm [34]. The use of _Hendersonia_ PGPR had been proven effective against _Ganoderma_ disease in the oil palm seedlings under a field-based study [35]. The early linkages would aid seedling adaptability to environmental conditions/stresses and increase survivorship [15; 16]. Previous research has shown that PGPR inoculation at an early stage of plant growth can clearly improve the survivorship of the host plants [14]. [PERSON] et al. [36] also discovered the ability of PGPR to promote plant growth as well as protect plants from pathogens. Many phosphate-solubilizing bacteria, such _Pseudomonas_[37], _Aspergillus_ and _Penicillium_[38], and Burkholderia [39], have been reported as PGPR biofertilizers in the oil palm cultivation for promoting the oil palm growth. Consequently, a sustainable yield could be achieved. P-solubilizing biofertilizers have been proven cost-effective and environmentally friendly in sustainable agricultural production [40]. PGPR holds the greatest agricultural potential since it provides an appealing alternative to conventional chemical fertilizers [19]. The quest for PGPR and research into their mechanisms of action is accelerating as efforts are made to commercialize them as biofertilizers [41]. [PERSON] et al. [42] investigated the plant-microbe interaction between the oil palm and _Bacillus salmonaya_ strain 139 SI at the seedling stage of the oil palm. This study focused on converting oil palm plantations from the use of pure chemical fertilizer to bio-chemical fertilizer. These contains certain beneficial microbes or PGPRs that can promote and maintain the soil's natural oil palm ecosystem. Thus, this study aims to characterize potential PGPRs based on biochemical tests of eight bacterial isolates, and identify these potential PGPRs based on 16S rRNA sequencing.
### Nuclear Magnetic Resonance
#### 1.3.2 Wettability Determination  
Before the advent of pore size distribution measurement and micro-structural properties being the primary NMR application method, [PERSON] and [PERSON] [25] published research on fractional wettability measurement by nuclear magnetic relaxation method. Their work focused on using uncoated sand packs and dri-film lubricant treated sand packs as water- and oil-wet porous media, respectively measuring the T\({}_{1}\) relaxation time of water in both media. The dri-film lubricant provides a low friction coefficient, which gives excellent lubrication. There was an inverse relationship between the fraction of oil-wet sand and the longitudinal relaxation time. There was extensive analysis and discussion on this done by [PERSON] et al. [29], where different heterogenous sandstones were used. Applying a low field NMR spectrometer on different sandstone reservoir types using two fluids, Soltrol 130 as refined oil and crude oil from the Gulf of Mexico, alters wettability in aged core analysis. T\({}_{1}\) was found to define wettability alteration quantitatively, culminating in the proposal of a systematic approach in interpreting wettability alteration in sandstone reservoirs with varying amounts of shales. This led to further research by [PERSON] et al. [30], where the arithmetic mean of the transverse relaxation time distribution, initial water saturation, residual oil saturation, and irreducible water saturation was defined. This result was then correlated to the fractional wettability index of the samples used. This pioneering work inspired several other notable pieces of research on NMR.  
NMR has proven to be very sensitive to the strength of the rock-fluid boundary. The fundamental physics behind it is that the wetting fluid has a relatively shorter relaxation time than the non-wetting fluid. Thus, the wetting fluid has a surface relaxation time similar to the bulk phase value with the reduction in relaxation time of the oil (oil-wet) from the bulk phase value being used as a qualitative [26; 31] and quantitative wettability index [8; 32; 33]. The T\({}_{2}\) distribution of the different fluids in a reservoir is illustrated in Figure 2.  
Figure 2: Graphical representation of NMR T\({}_{2}\) distribution of different fluid phases.