esa-sceva/satcom-chunk-collection huggingface dataset:
# The Terpene Mini-Path, a New Promising Alternative for Ternpenoids Bio-Production
## 2 Premises  
The TMP is _a priori_ totally artificial. Indeed, DMAOH and IOH are not very common chemicals found in living organisms and, if generated, they are considered to occur from DMAPP and IPP by (di)phosphatase action rather than the opposite. Nevertheless, some experimental evidence has been provided that DMAOH and IOH can be used either in a mixture or individually by some microorganisms to obtain higher titers in terpenoids when exogenously added to the culture medium. It should be noted that no enzymes were described nor suggested to be responsible for the incorporation of the two alcohols in these initial studies. Using a bacterium, different yeasts, and a filamentous fungus, [PERSON] et al. [16] demonstrated that the addition of DMAOH, IOH, geraniol (GOH), or mixtures of IOH with either DMAOH or GOH at concentrations of 0.5 mM-1 mM, improves the ubiquinone content of the considered cells by 30 to 72% at the end of the fermentation process. The same was also demonstrated later on using _Rhizobium radiolucter_[17] with a cellular increase of 30% in ubiquinone content following the addition in the culture medium of 1 mM IOH. In a completely different type of experiment aiming at the time to discover the late enzymes (genes) of the MEP pathway, it was demonstrated that an insertion in the promotor region of the _lytB_ gene of _Synnedocystis_ strain PCC 6803 caused impaired growth and a greenish-yellowphenotype instead of the dark blue-green normal one. The latter was recovered, as well as the growth capacity when DMAOH and IOH were supplemented to the solid culture media [18]. Slightly later, the LytB enzyme was proved to be the last enzyme of the MEP pathway catalyzing the transformation of 1-hydroxy-2-methyl-2-(_E_)-butenyl 4-diphosphate into a mixture of IPP and DMAPP with a 5:1 ratio [19]. In the three reported cases, the role of added DMAOH and IOH in increasing ubiquinone production or in restoring the normal phenotype can be explained by their transformation into DMAPP or IPP by endogenous kinases as proposed earlier [18]. This suggests the existence of a potential natural capacity to produce terpenids from DMAOH and IOH. Nevertheless, in the absence of a specific biosynthetic route to access DMAOH and IOH, except from DMAPP and IPP by action of (di)phosphatases, the capacity to use both C5 alcohols to generate terpenids is probably metabolically irrelevant or could be a futile cycle in the worst case or a DMAPP/IPP endogenous salvage pathway in the best one (see below). When put together, all these experiments point to the existence of a natural enzyme(s) able to doubly phosphorylate either one or both of the C5 alcohols, DMAOH and IOH.
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 2 Pharmacological Mechanisms of Drug-Food Interactions
### Pharmacokine Drug-Food Interactions (PK-DFIs)
#### 2.1.3 Metabolism
In Phase II, metabolism transferases such as UDP-glucuronosyltransferases, sulfotransferases, N-acetyltransferases, glutathione S-transferases and methyltransferases insert hydrophilic groups to the initial drug or the formed metabolite prior to renal elimination. Dietary polyphenols found in fruits, vegetables, wine, olive oil, tea and cocoa products are potential substrates and/or inhibitors of Phase II enzymes due to the presence of hydroxylic groups (-OH) that gives them structural similarity with drugs' metabolites. Until now, studies (mostly in vitro) have shown natural polyphenols' potential inhibitory activities on UDP-glucuronosyltransferases. For example, molecules such as glycyrrhetinic acid (licorice), chrysin (passiflora, mushrooms), silymarin (milk thistle), quercetin (onions, apples, grapes, berries, broccoli etc. ), myricetin (tomatoes, oranges, berries, red wine etc. ), naringenin (citrus fruits), luteolin (celery, parsley, broccoli, onion leaves), phloretin (apples), piceatannol (passion fruit and blueberries), resveratrol (grapes, wine, grape juice) etc. have been found to exhibit inhibitory activity against UGTs thus they can play role during Phase II metabolism of drugs and possibly contribute in DFIs [99; 100; 101; 102].
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.6 Abc Mac  
The adaptive beacon control protocol is proposed to avoid collision at the rear ends due to congestion. The authors proposed an adaptive beacon rate scheme based on rear end collision, considering the kinematics status of adjacent vehicles and considering a danger co-efficient \(\rho\). Based on the bandwidth requirement and channel capacity, a distributed beacon rate adaptive (DBRA) problem is formulated. A vehicle bearing high \(\rho\) estimates is assigned high beacon rate to avoid collision. During congestion, the vehicle adopts a greedy algorithm to solve the DBRA problem and TDMA-based broadcasting is conducted for  
Figure 7: Mechanism of TCG MAC [78].  
neighboring vehicles. The protocol works in three basic steps: (i) detection of congestion, (ii) adaptation to distributed beacon rates, and (iii) broadcasting the adapting result to other vehicles.  
The protocol guaranteed high performance in dynamic traffic scenario alleviates collision and provides an efficient transmission ratio. QoS in terms of throughput can still be improved. However, it does not emphasis non-safety messages. The off-road collision, real-time traffic management is not analyzed.
# Prediction Method of Unsteady Flow Load of Compressor
## 3 Parametric Methods and Data Collection
### Flow Equations and Meshing  
There are three conserved variables in the equation, namely the mass, the momentum, and the energy. For the 3D CFD simulation, it can be uniformly described by the following formula:  
\[\mathrm{div}(\rho u\varphi)=\mathrm{div}(\Gamma\mathrm{grad}\varphi)+S \tag{7}\]  
Figure 3: The numerical model.  
In detail, there are three terms in the equation from left to right, which represent the convection term, the diffusion term, and the source term in the flow field. It should be noted that the meaning of \(\varphi\) is a universal variable. If \(\varphi\) is equal to 1, then the equation is continuous. When \(\varphi\) means temperature, the equation becomes an energy equation. Similarly, if \(\varphi\) is the velocity in any direction, the momentum equation is obtained. \(\Gamma\) represents the generalized diffusion coefficient. \(S\) represents the source item. the three-dimensional flow of a compressor blade is studied in this paper. The continuum hypothesis and the vortex viscosity hypothesis were adopted. Therefore, the RANS method is used in this paper to simulate the compressor flow field. The Reynolds average method (RANS) is a widely used numerical simulation method of modelling the turbulence in engineering. The turbulence model that was used in this study is \(k\)-\(w\). An ideal air condition was used as the working medium. Additionally, the wall surface adopts no slip boundary condition. When the calculated residual is less than \(10^{-5}\), the calculation is considered to converge.  
In order to save the time that it used for data acquisition, the reduced model was used to calculate the compressor blade three-dimensional flow. The mesh generation of the model was performed by calling the TurboGrid macro command in MATLAB. The flow channel is extended axially downstream of the model to enhance the computational convergence. It is helpful to improve the quality of boundary layer meshes by using O-mesh to generate meshes near the blades. For improving the solution accuracy, the mesh density near the front and back edges is increased. Since the mass flow is a working condition parameter in a sample calculation, its calculation accuracy is closely related to the sample data quality. The model that is used in this paper represents the actual equipment in the project, and one working point of the model is used as the reference for grid independence verification in a steady-state calculation. Table 1 shows the research results of grid independence of the model. It can be found that when the grid number increases to 4.4 \(\times\)\(10^{5}\), the relative deviation of the calculated value of flow is less than 0.4%. So this grid scale is adopted as the benchmark. Figure 4 shows a grid diagram of the fluid domain during the CFD calculation.
# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 1 Introduction
### Task Complexity and Electroencephalography  
Electroencephalography (EEG) is a recording of electrical changes in a population of neurons of the brain [29]. Neuronal oscillations in different frequency bands of EEG are related to specific cognitive activities [30; 31; 32]. Among five major frequency bands--delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-100 Hz), the beta-band is associated with attention-related mental activities [33; 34; 35].  
Although the beta-band is thought to be sensitive to task complexity [36; 37; 38], there is no consensus as to the direction of the relationship. On the one hand, beta-band power is known to increase with task complexity. For example, [PERSON] et al. [39] observed higher beta-band power when the number of characters to be memorized increased from one to five. [PERSON] [35] reported that the average beta-band power at Cz, Fz, and Pz increased with the complexity of the N-back task where participants were asked to respond when the letter in the current trial was the same as the one shown N trials before. In a word discrimination experiment, researchers found enhanced beta-band power when the semantic complexity increased [40]. On the other hand, other studies have reported opposite results. [PERSON] et al. [41] indicated that beta-band power trended higher on a simple number-reading task than on arithmetic tasks. [PERSON] et al. [42] showed a higher beta-band power on simple letter-copying tasks than on a complex task in which participants were instructed to write a letter other than the one shown.  
Such inconsistency in the relationship between task complexity and beta-band power has not even been discussed in the VRE. Recently, researchers have successfully applied EEG systems together with consumer-level VR HMDs [43; 44]. For example, [PERSON] and colleagues employed a 32-channel portable EEG system with the HTC VIVE VR headset to generate an adaptive VR training system by collecting alpha-band activities in real-time [44]. Taking advantage of recent technological advancements in EEG devices that are portable and provide data in real-time [45; 46], our second research question was to determine whether brain signals indicate task complexity in the VRE.  
**RQ2:**_Do changes in brain signals indicate task complexity in the VRE?_
# A Visual Object Tracking Algorithm Based on Improved TLD
## 4 Experiments and Analysis
### Tracking Speed  
Real-time characteristics were evaluated by frames per second (FPS), the unit of which is frames/s. The tracking speeds of the same algorithm vary across different computer configurations. So, these experiments were all done on the same computer. Moreover, the FPS values were the average of repeated tests. Table 3 shows the number of successfully tracked frames and FPS.  
As shown in Table 3, compared with the original TLD, the improved TLD not only increased the number of successfully tracked frames but also enhanced the FPS. Tracking speed is directly related to the tracking algorithm. It is also affected by the image size, object scale, and color. Large images will generate more scan windows and every patch is larger, resulting in longer tracking time. It can be seen that the FPS of \"Girl\" was quicker than that of the others. Analyzing the FPS of the improved TLD, the increase in FPS was different among video sequences. If the target was relatively small in the image, the advantage of local search was more obvious. More specifically, the effect on reducing the amount of calculations was more significant. Examples include \"CarScale\" and \"Redteam\".  
Moreover, it was observed that the tracking speed slowed down with the accumulation of patches in the object model. Further, early samples were less representative. Therefore, sample management is a meaningful issue in further research. More challenging applications are available on the basis of a real-time tracker. For example, multitarget tracking is a direction worth trying.
# YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
## 1 Introduction
However, existing defect detection models pursue high accuracy and overlook the performance on low-performance devices. [PERSON] at al. [17] introduced a lightweight method for the detection of sewer defects based on the improved YOLOv5, which can accurately identify four types of sewer defects. However, the model is still complex, with a large number of parameters, model size, and floating-point operations. YOLOv5, being a newer iteration in the YOLO series, outperforms many other object detection algorithms, including Faster R-CNN [18] and the Single-Shot Multi-Box Detector (SSD) [19], in terms of recognition performance. However, in practical sewer defect detection tasks, certain drawbacks persist. For on-site computing devices with limited computational power, the computational demands of YOLOv5 can be excessive, leading to suboptimal detection speeds. This limitation highlights the need for further optimization or alternative approaches to make such advanced algorithms more feasible for real-world sewer inspection scenarios. This paper introduces a lightweight yet highly accurate model for the detection of sewer defects. While computer vision has been widely applied in inspecting buildings, dams, bridges, roads, industrial artifacts, and various pipelines (water, gas, petroleum), including sewers, this study stands out by addressing applications for field devices with limited computational capacities. The approach is built upon an optimized version of You Only Look Once (YOLOv5) for the detection and classification model. Certain stages of the pipeline have been replaced with carefully crafted and tailored components. The resulting iteration is named YOLOv5-Sewer. Experimental validation on the dataset, coupled with a comparison against alternative pipeline methods, demonstrates superior performance while maintaining acceptable accuracy levels. In conclusion, the proposed model successfully implements a more lightweight design for low-performance devices, achieving commendable detection accuracy and overall enhanced performance. The rest of this paper is organized as follows. In Section 2, we introduce the method of model lightweighting and the method of compensating for accuracy losses. In Section 3, we introduce the dataset acquisition and improvements, the experimental environment, comparative experiments among different lightweight backbone networks, fusion experimentswith various improvement modules, comparative experiments with other popular object detection models, detection speed comparisons on portable devices, and the visualization of the detection results. Finally, the conclusions drawn from this study are presented in Section 4.
## 4 Experiment Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1-Score** \\ \hline KNN & 0.9812 & 0.9817 & 0.9815 \\  & & & No. of neighbors to use: 4 \\  & & & Weight function used in prediction: By distance \\ \hline SVM & 0.8961 & 0.9344 & 0.9091 \\  & & & & Penalty parameter C of error term: 0.001 \\  & & & Decision function of shape: One-vs-one \\  & & & Kernel type to be used in the algorithm: Linear \\ \hline LR & 0.7208 & 0.7188 & 0.7196 \\  & & & & Inverse of regularization strength of term C: 0.0001 \\  & & & Norm used in penalization function: \(\ell_{1}\) \\  & & & & Algorithm to use in optimization problem: Linear \\ \hline DT & 0.9816 & 0.9836 & 0.9826 \\  & & & & Maximum tree depth: 9 \\  & & & No. of features to consider for best split: 12 \\  & & & & Strategy used to choose split at each node: Best \\ \hline RF & 0.9814 & 0.9833 & 0.9823 \\  & & & & Use bootstrap samples when building trees: False \\  & & & & Function to measure split quality: GINI \\  & & & & Max tree depth: 9 \\  & & & & No. of features to consider for best split: 12 \\  & & & & Min. no. of samples required to be at leaf node: 2 \\  & & & & Min. no. of samples required to split internal node: 9 \\  & & & & No. of trees in forest: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: CIDDS-001 (SMOTE + GS) scores. The efficiency of each model was determined by Receiver Operating Characteristic Area Under the Curve, which measures performance by indicating the ability of the model to distinguish classes; the higher the AUC is, the better the prediction of the model. Figures 2 and 3 depict each tuned classification model. Figure 2: Receiver Operating Characteristic (ROC) for balanced subset CIDDS-001. \begin{table}
\begin{tabular}{l c c c l} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F1-Score** & **Best Hyperparameter Value** \\ \hline KNN & 0.9677 & 0.9676 & 0.9676 & Algorithm used to compute nearest neighbors: Ball tree No. of neighbors to use: 4 Weight function used in prediction: by distance \\ \hline SVM & 0.8152 & 0.8261 & 0.8117 & Penalty parameter C of error term: 0.01 Decision function of shape: One-vs-one Kernel type to be used in the algorithm: Linear \\ \hline LR & 0.7836 & 0.7789 & 0.7816 & Inverse of regularization strength of term C: 0.0001 Norm used in penalization function: \(\ell_{1}\) Algorithm to use in optimization problem: LBFGS \\ \hline DT & 0.9797 & 0.9800 & 0.9799 & Maximum tree depth: 9 No. of features to consider for best split: 12 Strategy used to choose split at each node: Best \\ \hline RF & 0.9796 & 0.9799 & 0.9798 & Use bootstrap samples when building trees: False Function to measure split quality: GINI Max tree depth: 9 No. of features to consider for best split: 7 Min. no. of samples required to be at leaf node: 2 Min. no. of samples required to split internal node: 9 No. of trees in forest: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: ISCX-Bot-2014 (SMOTE + GS) scores.
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 3 The Gated High-Low Resolution Frames Network
### Gate-Guided Adjacent High-Low Resolution Frame Alignment Network
Specifically, the gated motion compensation module consists of gated convolution and motion compensation. Firstly, feature F\({}_{\text{t-1}}\) is fused with the down-sampled high-resolution feature h\({}_{\text{t-1}}\). Secondly, a layer of activation function convolution and a layer of linear convolution is performed to generate feature X. Then, the convolution operation, Sigmoid operation, and motion compensation are performed on feature X to obtain the gated weight G and alignment feature X\({}_{\text{MC}}\), respectively. Finally, the alignment feature information X\({}_{\text{MC}}\) is multiplied with the gated weight G to obtain the final alignment feature fA. The calculation formula is as follows:  
\[\text{X}=\text{W}\left[\text{down }(\text{h}_{\text{t-1}}),\text{F}_{\text{t-1}}\right] \tag{4}\]  
\[\text{G}=\text{S}\left(\text{R}\left(\text{X}\otimes\text{W}_{\text{G,1}} \right)\otimes\text{W}_{\text{G,2}}\right) \tag{5}\]  
\[\text{X}_{\text{MC}}=\text{MC }(\text{X}) \tag{6}\]  
\[\text{f}^{\text{A}}=\text{G}\odot\text{X}_{\text{MC}} \tag{7}\]  
where X denotes a high-low resolution feature summing input, down \((\cdot)\) denotes sub-pixel down-sampling, G denotes gating weights, \(W_{\text{G,1}}\), and \(W_{\text{G,2}}\) denote first- and second-layer linear operations, R and S denote Relu and Sigmoid functions, respectively, MC \((\cdot)\) is a motion compensation module, \(\otimes\) denotes matrix operations, and \(\odot\) denotes element-by-element multiplication. The advantage of this method over existing methods is that it can use both high- and low-resolution neighboring frame information, effectively improving the accuracy of motion compensation and the reconstruction effect. The introduction of the gating mechanism, on the other hand, enables the adaptive selection of useful information to avoid the effects of motion estimation errors.
## 4 Discussion  
It is already well known that mucosal HR HPV-types (HPV16) are involved in OPSCC [25], and the contribution of HR HPV to the carcinogenesis of HNSCC is different by geographical region [26]. HPV-positive OPSCC was defined as positivity to HPV DNA and p16 IHC, and HPV-driven OPSCC was defined as being HPV DNA and RNA positive [27; 28; 29]. The HPV-positive OPSCC refers to carcinomas of the oropharynx presumed to be associated with HPV, mostly based on positivity to HPV DNA and p16 IHC [27].  
In our country, there is a known high burden of cervical cancer, and it is expected to detect the same high prevalence of HPV in HNSCC, as it is in cervical cancer. Our detected HPV-positive OPSCC prevalence was 4/54 (7.4%) HPV-positive patients and 1/54 (1.8%) HPV-positive OPSCC prevalence was 4/54 (1.8%) HPV-positive OPSCC prevalence.  
* NOS (not otherwise specified).  
\end{table}
Table 1: Cont.  
patient with an HPV-driven HNSCC case. Our results OPSCC HNSCC originate from samples such as tonsil, base of tongue, and uvula, in line with other studies [20].  
Potential reasons for the generally low rates of positive agreement between p16 and RNA with HPV DNA positivity might be several technical issues, such as fixation artifacts and over-fixation that may impact both p16 IHC and RNA analysis out of FFPE. Another reason can be the potentially low rate of HPV-driven carcinogenesis in this Romanian cohort, where most HPV DNA positive cases may not be, in reality, cases where HPV drives the carcinogenesis (i.e., detection of clinically irrelevant HPV DNA infections in the tissue). The high percentage of smokers (54%) and even chronic alcohol users (21%), known as risk factors, could be another explanation [17; 19].  
Moreover, it is interesting that, in our samples, we did not detect any FGFR mutation, in comparison to the Swedish cohort in which 11/109 HPV+ TSCC/BOTSCC patients exhibited FGFR3 mutations. For the Swedish patients, the high FGFR3 expression among patients with wild-type FGFR3 was correlated to better disease-free survival [20].
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Link Efficiency Oriented Network Model
#### 3.1.1 Hello Packets (HP)  
Hello packets are used to maintain adjacencies between neighboring nodes. BSNs share their updated residual energy, number of hop-counts, link efficiency, and distance to the BNC by circulating the HPs periodically. The field of the HP header is elaborated in Table 1.  
The link efficiency can be calculated based on the receive signal strength indicator (RSSI), the link quality indicator (LQI), and packet reception rate (PRR) [37]. However, this work determines the link efficiency based on PRR because it is a memory efficient method and requires little computations. The link efficiency can be computed in Equation (1).  
\[LE=\sum\frac{PR_{N}}{PS_{S}} \tag{1}\]  
where \(LE\) denotes link efficiency, \(PR_{N}\) refers to the number of packets received at the neighbor node, and \(PS_{S}\) indicates the number of packet sent from the source node.  
The proposed protocol keeps track of residual energy of each BSN by calculating the consumed energy in each round using Equation (2).  
\[RE=E_{init}-E_{cons} \tag{2}\]  
where \(RE\) is the residual energy of a BSN, \(E_{init}\) is the initial energy, and \(E_{cons}\) is the energy consumed in each round.  
The distance from the source node to BNC can be calculated from \(X\) and \(Y\) coordinates as in Equation (3).  
\[d_{(i,BNC)}=\sqrt{\left(X_{i}\,-X_{BNC}\right)^{2}+\,\left(Y_{i}\,-Y_{BNC} \right)^{2}} \tag{3}\]