esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 4 What Is the Role of Virus in Oxidative Stress and Mutations?  
Viral infection is known to cause oxidative stress in the host cells. Earlier studies have reported that the activation of phagocytes causes the generation of several types of ROS. In addition, different pro-oxidant cytokines (TNF-\(\alpha\) and IL-1) were found to be released by phagocytes as well as the infected host cells in response to the viral infection [37]. Further, viral infection was found to alter the balance between oxidant/ antioxidant status in the host cells. The infection can enhance the cellular pro-oxidants levels of iron and nitric oxide and diminish the synthesis of antioxidant enzymes. The enzymes most frequently affected were SOD, GPx and CAT. As reported earlier, ROS has the ability to damage every organelle in the cell, including the nucleus [38].  
Several studies conducted in the past have demonstrated that after chronic viral infection (DNA/RNA virus), the integrity of the host cells is modified. The virus targets several genes that regulate cell growth, cell differentiation, and cell-cycle and these are linked to the alteration of host genomic components. These modifications are reported to contribute to mutation and occurrences of several diseases, such as cancer [39]. A study conducted to determine the etiology of mutation-related complications indicated that 20% of human cancers are associated with viral infection. Several common viruses that cause infection in humanswere found to be linked to this [40]. The most important among them are Epstein Barr virus (EBV), hepatitis C virus (HCV), human T cell lymphotrophic type-1 (HTLT-1), hepatitis B virus (HBV), human herpes type 8 (HH8)/Kaposi's sarcoma herpes virus (KSHV), human immunodeficiency virus (HIV) and human papilloma virus (HPV) [41].  
Proto-oncogenes formed after viral infection are reported to be converted into oncogenes through mutation. Studies have indicated that viral oncogenes augment this process by different mechanisms such as addition, deletion, and/or substitution of nucleic acids [42]. Both DNA and RNA virus are known to contain these oncogenes. Other conditions, such as chronic inflammation that follows viral infection, were found to play a key role in the mutation. Hyper-activation of the cytokines system during chronic inflammation can induce DNA damage in the host cells and could contribute to mutation [43]. Furthermore, the activation of oncogenes, inhibition of tumor suppressor genes, and altered function of immune cells in detecting and destroying mutated cells accelerate the process of mutation-related diseases [44].  
To date, the research conducted on coronavirus has not suggested mutations in host cells. However, considering the fact that the RNA virus has the ability to modify the integrity and function of the host nucleus, it is essential to devise plans before-hand to reduce mutation-related problems in the future.
## 3 Results and Discussion
### Characterization of MIPs and NIPs
#### 3.2.2 Surface Area Measurement via Nitrogen Adsorption-Desorption Analysis
The pseudo-first-order and pseudo-second-order rate equations were given as follows:  
\[\ln(Q_{\mathrm{e}}-Q_{\mathrm{t}})=\ln Q_{\mathrm{e}}-k_{1}t \tag{5}\]  
\[\frac{\mathrm{t}}{Q_{\mathrm{t}}}=\frac{1}{k_{2}Q_{\mathrm{e}}^{2}}+\frac{ \mathrm{t}}{Q_{\mathrm{e}}} \tag{6}\]  
where \(Q_{\mathrm{e}}\) (mg g\({}^{-1}\)) and \(Q_{\mathrm{t}}\) (mg g\({}^{-1}\)) are the binding capacity of gossypol adsorbed at equilibrium and at a specific time t (min), respectively; k\({}_{1}\) (min\({}^{-1}\)) is the pseudo-first-order rate constant of adsorption, and k\({}_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)) is the rate constant of the pseudo-second-order adsorption model. According to the data in Table 4, the regression values \(R^{2}\) of the pseudo-second-order model were higher than those of the pseudo-first-order models, indicating which fit better for describing the gossypol binding process of MIPs. Furthermore, the theoretical adsorption capacity (\(Q_{\mathrm{e}}\), cal) value calculated from the pseudo-second-order kinetic model was in good agreement with the experimental adsorption capacity (\(Q_{\mathrm{e}}\), exp) data. Thus, the pseudo-second-order mechanism was predominant, and the chemisorption was probably the rate-limiting step controlling the gossypol-binding process [53].
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
### Basic Control Ideas  
The scenario studied involved a basic urban road area with four lanes per direction for urban main road and expressways. The virtual lanes for the EVs were set in the outer lanes. It should be emphasized that we do not consider the impact of the downstream intersection (signal) on the traffic flow, i.e., the ends of the road section are away from the intersections, as illustrated in the schematic diagram of Figure 2, where only one direction in the road section is plotted. Exploiting the technical characteristics of the vehicle infrastructure cooperation system can obtain the CV's real-time position, speed, acceleration, and other information. When the CVs receive the lane-changing signal, the system searches for the optimal voids in the inner lane, guides the CVs to the front end of the EVs, inserts the neutral gear to make them drive toward the inner lane, and restores the original vehicle lane distribution after the EVs pass quickly. Hence, the CV is then back in the original lane.  
The above control process is represented by the flowchart in Figure 3. When multiple CVs are in front of the EVs, the guidance control is performed sequentially according to the flow to form a loop. Due to the complex coupling between vehicles and traffic flows, vehicles in mixed traffic flows have unique dynamics and uncertainties. To this end, the following assumptions were made: (1) CVs have a random slowing phenomenon and arrive uniformly. (2) EVs do not change lanes and drive at the desired or maximum drivable speed. The speed is within a certain interval that exceeds the CV's expected speed. (3) The CVs participating in the interaction obey the guidance signal. (4) The traffic flow composed of CVs is relatively stable and does not reach saturation. (5) Each CV selects a lane change to avoid the EV instead of accelerating.  
Figure 1: Schematic representation of the physical function area for EVs.
# A Robust Wind Turbine Component Health Status Indicator
## 1 Introduction
The main contributions of this work are as follows:  
* **Enhanced GMCM Application:** We extend the GMCM method to a variety of wind turbine components, providing a more versatile and comprehensive modeling approach. * **Scalability Improvements:** By incorporating multiple variables through an advanced selection process, the methodology ensures scalability and robustness in fault detection across different wind turbine components. * **Holistic Health Index Generation:** We propose a holistic GMCM-based methodology that generates a user-friendly and easily monitorable health index for the main systems of a wind turbine, facilitating effective maintenance decision-making. * **Early Fault Detection:** The developed Failure Index (_FI_) focuses on the early detection of faults in major wind turbine components, enhancing operational efficiency and reducing downtime. The paper is structured in the order that follows. In Section 2, selected SCADA signals, case study information, and details of learning and test data sets are introduced. It also covers the mathematical foundations of the employed methodologies. The data filtering process and feature selection are discussed, and the applied GMCM model's optimization process is explained. The presentation of the proposed framework complements this section. In Section 3, the resulting _FI_ signals from the studied cases are discussed. Finally, a summary of the contributions is given in Section 4.
## 6 Discussion on the Results
### Limitations and Future Validation Needs  
The findings presented here suggest notable advantages for the application of ACPH systems in low-to-mid-rise buildings. However, it is essential to recognize that taller structures are likely to experience significant second order (P-Delta) effects, which can lead to increased story drifts and heightened demands on structural columns. This may ultimately negate some of the benefits associated with localized ACPH hinge behavior. Therefore, the implementation of ACPH designs in high-rise or multi-span continuous frameworks necessitates advanced analytical approaches that account for complex geometric nonlinearities and rigorously detailed incremental dynamic simulations.  
Moreover, while the numerical analyses illustrate how ACPH connections facilitate repair processes, it is paramount that validation occurs through large-scale physical testing. Such testing ideally utilizes multi-degree-of-freedom shake tables to effectively capture higher-mode responses and realistic shear demands on joints. In conjunction with these experimental efforts, conducting a comprehensive life-cycle cost analysis would provide valuable insights into the financial advantages of promptly replacing hinge plates following seismic events, as opposed to facing substantial downtime associated with repairing traditional monolithic RC damage.  
Additionally, the inherent exposure of steel components within an ACPH system underscores the critical need for effective corrosion protection and meticulous detailing. The successful preservation of hinge plates and bolts in a rust-free and fatigue-resistant condition throughout the service life of the structure is contingent upon the implementation of protective coatings, gaskets, or easily applicable corrosion inhibitors. Although these design considerations introduce a degree of added complexity, they are crucial for ensuring that the hinge continues to function predictably and remains safe for either reuse or replacement when necessary.
# Exploring Structural Changes in Ge-Te Amorphous Films Through Small-Angle Neutron Scattering
## 4 Discussion
### High-Q Region: Diffraction Region
#### 4.2.2 Peak Widths  
Examining the widths of the diffraction peaks enables an estimation of the degree of order across various length scales. The peak width \(\Delta Q\) is determined as the full width at half maximum (FWHM), which, for a Gaussian function, corresponds to \(\Delta Q=2\sqrt{2\ln 2}\;\sigma\approx 2.35\;\sigma\). The characteristic distance of ordering in the system is given by \(D=2\pi/\Delta Q\), which represents the extent of the order attributed to the corresponding peak.  
The correlation distance associated with the FSDP, initially around 9 A (in zone 1) and increasing by more than 100% in zone 3 (Figure 5), reflects an enhancement in IRO. This considerable increase suggests that, with higher Ge content, the intermediate atomic ordering extends further, as if clusters or atomic groups are achieving greater spatial coherence or connectivity. This likely indicates that germanium is reinforcing or stabilising the network structure, possibly through the formation of Ge-Te or Ge-Ge clusters or similar motifs that persist over longer ranges.  
For the PP, representing SRO, the correlation distance is more or less constant at about 11 A for any germanium content (Figure 5). This implies that the initial ordering is retained as more germanium is added. The constancy in the correlation distance for the second peak points to a weakening of SRO with respect to the IRO. That is, as Ge is added, the short-range connections are maintained, possibly breaking down the previously existing Te-rich network in favour of longer, Ge-influenced connections. This provides experimental confirmation of RMC results [24], showing that the addition of Ge causes a gradual transition from a fully ordered SRO model to a disordered one. The huge difference in correlation distance for each peak implies that the glass is evolving to a structure with predominant intermediate-range domains.  
By studying both regions (low and high \(Q\)), we reach the same conclusion: there are three zones of composition (see the shaded areas in Figures 3-5), with changes occurring in both SRO and IRO. This conclusion is also supported by the shoulder that the diffractogram of the sample with \(x=0.45\) presents at about 0.1 A\({}^{-1}\); the corresponding distance related  
Figure 5: Correlation distances corresponding to the FSDP and PP as a function of Ge content \(x\). Dashed lines are smoothed splines that only serve as a guide for the eye. The red-, white-, and blue-shaded areas correspond to the different zones observed as a function of the Ge content.  
to this \"peak\" is \(2\pi/0.1\) A\({}^{-1}\approx 62\) A, a value that is in line with the general trend observed in glassy systems with chemical ordering [34].
## 3 Results
### Participant Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Coefficient** & **95\% CI** & _p-Value_ \\ \hline Total cholesterol (mg/dL) & \(-\)14.41 & (\(-\)28.52 to \(-\)0.31) & 0.045 \({}^{\star}\) \\ Triglycerides (mg/dL) & \(-\)28.19 & (\(-\)54.04 to \(-\)2.34) & 0.033 \({}^{\star}\) \\ HDL-cholesterol (mg/dL) & 4.45 & (0.86 to 8.05) & 0.016 \({}^{\star}\) \\ LDL-cholesterol (mg/dL) & \(-\)7.14 & (\(-\)22.44 to 8.17) & 0.352 \\ FBS (mg/dL) & \(-\)4.30 & (\(-\)13.89 to 5.29) & 0.370 \\ TAC (\(\mu\)mol/mL) & 14.39 & (2.04 to 26.75) & 0.024 \({}^{\star}\) \\ MDA (\(\mu\)mol/mL) & \(-\)0.05 & (\(-\)0.12 to 0.02) & 0.142 \\ GSH (\(\mu\)g/mL) & 16.27 & (\(-\)14.41 to 46.94) & 0.290 \\ Lactic acid (\(\mu\)mol/g) & 1.83 & (\(-\)0.44 to 4.09) & 0.111 \\ Acetic acid (\(\mu\)mol/g) & 0.77 & (\(-\)2.33 to 3.86) & 0.619 \\ Butyric acid (\(\mu\)mol/g) & 1.53 & (\(-\)1.90 to 4.96) & 0.372 \\ Propionic acid (\(\mu\)mol/g) & 5.45 & (1.41 to 9.49) & 0.010 \({}^{\star}\) \\ IL-10 (pg/mL) & 46.09 & (\(-\)242.48 to 334.65) & 0.291 \\ IFN-\(\gamma\) (pg/mL) & 33.12 & (\(-\)86.70 to 152.94) & 0.565 \\ TNF-\(\alpha\) (pg/mL) & \(-\)10.94 & (\(-\)21.17 to \(-\)0.71) & 0.039 \({}^{\star}\) \\ LPS (pg/mL) & \(-\)28.32 & (\(-\)48.68 to \(-\)7.95) & 0.008 \({}^{\star}\) \\ IgA (ng/mL) & 42.31 & (\(-\)55.24 to 139.87) & 0.386 \\ L/M ratio & \(-\)0.01 & (\(-\)0.04 to 0.02) & 0.519 \\ \hline \hline \end{tabular} \({}^{\star}\)  
Compared with the placebo group at week 12. The _p_-value was at a 95% confidence interval. \({}^{\star}\) = significant difference (_p_ less than 0.05); HDL = High-Density Lipoprotein; LDL = Low-Density Lipoprotein; FBS = Fasting Blood Sugar; TAC = Total Antioxidant Capacity; MDA = Malondialdehydedehyde; GSH = Glutathione Reduced; IL = Interleukin; IFN-\(\gamma\) = Interferon gamma; TNF-\(\alpha\) = Tumor Necrosis Factor alpha; LPS = Lipopolysaccharideachardie; IgA = Immunoglobulin \(\Lambda\); L/M = Lactulose/Marnitol Ratio.  
\end{table} 
Table 5: Gaussian regression analysis summary at the end of treatment for the _L. paracase_ group.
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Details** & **Drawbacks** \\ \hline \multirow{5}{*}{Systematic} & By making intuitive design decisions & \multirow{5}{*}{The links between structures and spectra are not always intuitive, making this method computationally costly and extremely slow.} \\  & based on photonics theory and existing structures, a starting structure is chosen and parameters are systematically cycled through until a desirable response is & \multirow{5}{*}{The extremely slow.} \\  & happened upon. & \\ \hline \multirow{5}{*}{Removal of non-unique instances followed by DL} & [PERSON] et al. implement a feedforward neural network to design a 1–5-layer system using up to 5 different materials, with a user-imposed ‘similarity metric’ [14]. This is used to filter out training instances deemed as non-unique, effectively reducing the design challenge to a one-to-one problem. & \multirow{5}{*}{The ‘similarity threshold’ chosen by the user is arbitrary and may not catch all similar spectra, which could prevent model convergence.} \\ \cline{1-1} \cline{3-3}  & Works by [PERSON] et al. and [PERSON] et al. use a Tandem Neural Network to address non-uniqueness [15; 16]. This is composed of two networks: the first designing a reflector to produce a target spectra, while the second pretrained network determines how well the target and predicted spectra match, regardless of the intermediate structural predictions. & \multirow{5}{*}{Struggles with classification design problems, as the intermediate network output needs to be in an acceptable form to be fed into the secondary network.} \\ \cline{1-1} \cline{3-3}  & [PERSON] et al. also use two networks, the second again being pretrained to map structural components to spectral response [17]. However, the two are not connected, and instead the first network outputs the probability of a number of structures, and a separate optimisation process evaluates each possibility with reference to its performance in the second network. & \multirow{5}{*}{Requires an optimisation process on top of a second pretrained network, and multiple possible designs must be tested and verified for each design case.} \\ \cline{1-1} \cline{3-3}  & & \\ \cline{1-1} \cline{3-3}  & & \\ \cline{1-1} \cline{3-3}  & & \\ \cline{1-1} \cline{3-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of existing methods of inverse design and their drawbacks. It is clear from this that the SNN model is an integral part of the tandem network, as inaccuracies here will prevent the inverse model from producing valid results [11]. The aim of this study is to accurately produce on-demand reflector designs using a Deep Learning approach to analyse a tightly constrained thin-film structure. A tandem network approach is adopted to address the non-uniqueness problem, and to achieve an accurate and versatile model using a reduced dataset.
# State of the Art of Tactile Micro Coordinate Metrology
## 4 System Calibration and Performance Verification
### Probe Calibration
#### 4.1.3 Probe Sphere Diameter and Form Correction  
On a conventional CMM the absolute diameter of the probe sphere is calibrated on a reference sphere. At the same time the anisotropic behavior of the probe head including stylus bending is corrected. This requires an independent calibration of the diameter of the reference sphere and assumes the roundness deviation of both, the reference sphere and the probe sphere, to be much smaller than the overall probing error, which is generally the case. For high precision micro coordinate metrology, the requirements for the reference sphere are much higher and cannot be fulfilled by independent calibrations. Therefore, an error separation method was developed involving three nominally identical spheres compared against each other in various configurations, using one of the spheres both as the probe sphere and the reference sphere, respectively [36]. In this way, both the absolute diameter and the sphericity deviation of three 1 mm ruby spheres could be mapped over their entire accessible surface. This method finally relies directly on the traceability of the CMM stage interferometer and on the repeatability of the probe being usually below 4 nm (standard deviation). Figure 6 shows a sphericity map of one of the measured spheres with a form deviation of 33 nm.
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 4 Results and Case Study
### Warehouse and Inventory Management
#### 4.2.1 Inventory Model Application  
This case study focuses on a small-sized beverage container manufacturing factory with a nationwide customer base, owing to its customization capabilities. Despite handling a large variety and volume of raw materials, the factory's warehouse management was inadequate. The absence of a Warehouse Management System (WMS) and reliance on paper-based stock recording led to high stock levels and low stock accuracy.  
To address these issues, the following steps were taken:  
1. Analysis of order history.
2. Implementation of Pareto/ABC classification to categorize materials based on their value, volume, and frequency of use [37].
3. Application of inventory models, specifically the Economic Order Quantity (EOQ) model, incorporating Reorder Point (ROP) and Safety Stock (SS) calculations [38].  
Figure 3: Interface of the developed TMS: order management.  
The implementation of these strategies resulted in a significant improvement for A-level materials, with inventory holding and carrying costs reduced by 55%. This outcome demonstrates the effectiveness of applying structured inventory management techniques in small-scale manufacturing environments.
## 2 Literature Review
### Conventional CADs for Cervical Cancer Diagnosis  
As mentioned before, conventional CAD models have utilized a classical ML approach that depends on extracting handcrafted descriptors from pap smear slides. Among them, the study [32] employed discrete cosine transform (DCT) and discrete wavelet transform (DWT) to retrieve features. Then, the fractional coefficient approach is used to reduce the dimension of these merged features. Finally, these reduced features are fed to seven ML classifiers to differentiate between different subgroups of cervical cancer leading to an accuracy of 81.11%. In another study [33], the authors used C-means clustering to segment cervical cells and then extracted texture features, including GLCM and geometrical descriptors from these cells. Subsequently, the authors used principal component analysis (PCA) to decrease the size of the features. Later, KNN was employed to classify cervical cells reaching an accuracy of 94.86%. Similarly, the research article [34] used C-means clustering to segment cervical cells and then attained shape and textural features such as the binary histogram Fourier algorithm (BHF). Next, the quantum-based grasshopper computing algorithm (QGH) was utilized to select features and apply these selected features as inputs to classifiers. Whereas the article [35] presented a CAD based on two phases. The first phase's goal was to extract texture descriptors from the cytoplasm and nucleolus together.  
The pap smear slides were segmented using a thresholding method. Then, to describe the local textural features, a texture descriptor called modified uniform local ternary patterns (MULTP) was proposed. Second, these descriptors were fed to an artificial neural network where its parameters were optimized using a genetic algorithm attaining accuracy of 98.9%.
# Performance of 5G Trials for Industrial Automation
## 4 Evaluation Setup
### Measurement Setup
We carried out our OTA measurements during 5G trial deployments on factory shopfloors where the UEs are placed at various location, including the inside of a milling machine chamber, mounted on robots and AGVs, behind cabinets, etc., and including both line-of-sight and non-line-of-sight scenarios between the 5G UE and the 5G base station. Compared to office and lab setups, factory shopfloors have strong multipath effects with lots of metal, moving machine parts, multiple automation cells and assembly lines, AGVs, and workers moving around, etc. We obtained a large sample size of at least 100,000 (and typically 1 million) measurements for each network, deployment, and traffic setting in order to generate statistically significant results. This amounts to an overall measurement time that spans several days. In this article, we present the results from a single 5G terminal device. We conducted the tests when the radio network was loaded with additional traffic  
Figure 4: A simplified FRER setup where the application traffic at the ingress point is replicated and transmitted over two independent 5G user plane paths using two different UEs and is eliminated at the egress point. that had been created by up to three background UEs. The load had little influence on the performance results. Systematic performance measurements investigating the load situations of multiple devices and traffic types have been left for future work.