esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Temperature Profile  
Figures 10-14 are plotted to get insight into the variations of \(M\), \(Nb\), \(Nt\), \(Pr\), and \(N\) on the temperature profile. The magnetic field strength shows favorable behavior for the temperature profile in Figure 10. Here, hydromagnetic case is stronger than the hydrodynamic case (\(M=0\)). It can be observed that the thickness of the thermal boundary layer thickness improves with growth in \(M\)[41]. Figure 11 illustrates the behavior of temperature profile against the variations in the Brownian motion factor. Brownian motion is an irregular movement of the fluid particles which produces collisions among the particles. Therefore, the heat of the fluid increases with growing the Brownian motion impact. Consequently, free surface nanoparticle volume fraction reduces. The variations in \(Nt\) increase the temperature profile (see Figure 12). The restrictions on the thermophoresis favor the rise of the surface temperature. The irregular movement (Brownian motion) produces kinetic energy because of nano suspended particles that cause an enhancement in the temperature; consequently, thermopheretic force develops. Due to the intensity generated by this force, the fluid starts in the reverse direction of the extending sheet. Finally, an enhancement in \(Nt\) causes an improvement in temperature; as a result, the surface temperature also increases. The effect of Prandtl number on temperature profile is  
Figure 8: Variations in velocity profile for several value of \(\zeta\).  
Figure 9: Variations in velocity profile for several value of \(\beta_{1}\).  
presented in Figure 13. The ratio of momentum diffusivity to thermal diffusivity is termed the Prandtl number. It is a well-known fact that higher thermal conductivities are linked with lower Prandtl fluids; therefore, heat diffuses rapidly from the surface compared to higher Prandtl fluids. Thus, _Pr_ can be employed to control the rate of cooling in conducting flows [42]. Finally, the temperature profile decreases with the growth of Prandtl number. Figure 14 shows that the favorable behavior corresponds to the radiation effect. The higher surface heat flux rate corresponds to the higher radiation impact, causing the fluid to be warmer.  
Figure 11: Variations in temperature profile for several values of _Nb_.  
Figure 10: Variations in temperature profile for several values of \(M\).  
Figure 14: Variations in temperature profile for several values of \(N\).  
Figure 12: Variations in temperature profile for several values of \(N\).  
Figure 13: Variations in temperature profile for several values of \(Pr\).
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
## 3 Monitoring Data Validation Analysis  
### Project Overview  
The monitoring interval selected for the Jinan Metro Line 4 extends from Iiangshuiquan Road Station to Yanshan Overpass West Station. This interval line begins at Yanshan Overpass West Station, follows the 10 th Road to the east with an underground alignment beneath the Yanshan Overpass, and connects to Yanshuiquan Road Station. The total length of the single-line interval is 1709.126 m, with a V-shaped longitudinal profile and a maximum longitudinal slope of 27%. The tunnel depth ranges from 16.2 to 41.5 m, with line spacing varying between 11.0 and 36.5 m. A contact channel and a pumping station are established at the lowest point of the interval, along with an additional single contact channel.  
The interval tunnel traverses strongly weathered amphibolite, moderately weathered amphibolite, and moderately weathered limestone. The mechanical properties of these lithologies are as follows:  
Strongly weathered amphibolite: its compressive strength is 5030 MPa, with low wave velocity and high porosity, indicating a loose structure with relatively low tensile and shear strength.  
Moderately weathered amphibolite: its compressive strength is approximately 8045 MPa, exhibiting a more compact structure with lower porosity and higher tensile and shear resistance.  
Moderately weathered limestone: Its compressive strength is 10,060 MPa, demonstrating strong compressive properties. However, the abundance of calcium carbonate minerals makes it susceptible to water, acidic environments, and dissolution, which may compromise its long-term stability.  
To ensure the safety and stability of the tunnel structure, construction must adopt appropriate support measures tailored to the varying lithologies encountered along the interval.
# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs
### Proposed Solutions
#### 2.2.2 MAC Layer
wireless access in VANETs under different operating conditions. The fuzzy logic based MAC outperforms the standard MAC in terms of throughput and provides effective differentiation among different applications' access categories. Research in [13] also shows better reception rates by the dynamic adjustment of CW in moderate network traffic. The DBM-ACW control scheme proposed in [46], aims at controlling the CW based on network density estimation in highway environments. The dynamic adjustment of CW is a CSMA/CA strategy to control the congestion [37]. 6 Clustering based MAC Protocols: To improve the stability and scalability of VANETs, many cluster based algorithms have been proposed in recent research [47]. In this approach the network is divided in virtual groups i.e., clusters. Each cluster has a Cluster Head (CH). The CH acts like the master of the group and manages all the communication and organization in its cluster. A Comparative Survey of VANET Clustering Techniques in [47] explores the design choices made in development of the clustering algorithms. It presents techniques to solve the problem of cluster head election and cluster management. A summary of all the techniques are mentioned in Table 2.
# Monitoring Forest Phenology in a Changing World
## 4 Monitoring Challenges to Be Addressed
### Compiling and Reconciling Data across Monitoring Methods  
A major challenge with long-term, multi-method, multi-biome monitoring is the need to reconcile data from different sources. Simply compiling these data into a single database represents a significant challenge. To start with, the basic formats of data vary significantly and include everything from classified imagery (e.g., UAV photographs with ROIs categorised into different phenological states) through to gene sequences (molecular monitoring). Space and time represent the obvious axes that will allow data to be indexed in a searchable way, but phenological datasets also come in many different spatiotemporal formats and resolutions. These include temporal snapshots in 1, 2 and 3 spatial dimensions from collection traps, imagery and LiDAR, respectively, through to time series data from accelerometers. An obvious starting point for combining the variable spatiotemporal dimensions of different monitoring methods into the same study is to establish pre-collection criteria on the frequency and spatial arrangement for monitoring, but this will often not be possible if data is collated from different sources. Summarising phenological observations through statistical averaging is the next most obvious step for integrating observations from multiple methods. This would require analysts to specify a common spatial and temporal resolution to apply to all datasets and then either upscale or downscale the resolution of individual datasets to meet this common resolution. This involves a trade-off, however, as the most defensible resolution to aggregate data at is given by the resolution of the coarsest dataset. This means users would lose the ability to examine fine-scale patterns obtained from high resolution methods.  
Datasets from alternative methods that overlap in space and time represent both an opportunity and a challenge. Overlapping datasets are needed to validate phenological observations made using competing methods, as is commonly achieved by pairing visual observations with aerial or satellite imagery. There remains, however, an opportunity to expand these validations to remove the need for visual observations through the formation of 'validation chains', in which one validated product is used to validate the next. Key to achieving this in a robust manner will be appropriate propagation of error to ensure data uncertainty is accurately represented in the final set of classified observations. Classification accuracy could then be used as a quantitative decision tool for deciding on phenology at given sites where competing methods provide different phenological classifications. Such a process would be aided by setting clear standards for phenological metrics that apply across data sources, and by having database systems designed from the start to record classification accuracy alongside observations.
# The Predictive Grey Forecasting Approach for Measuring Tax Collection
## 5 Conclusions  
Taxation has always been a rich source of revenue for the government. Governments are vigilant and active enough to upgrade the regulations and collection mechanisms related to taxes. The grey forecasting model has proven to be a reliable tool for predicting India's direct tax revenue.  
The findings of this study indicate that the cost of tax collection increased from INR 929 crores in 2000-01 to INR 8452 crores in 2022-23, but the cost of tax collection as a proportion of tax collected decreased significantly from 1.36% in 2000-01 to 0.51% in 2022-23. The proportion of direct tax relative to total tax revenue increased from 36.31% in 2000-01 to 54.62% in 2022-23. The analysis also revealed that, apart from the COVID-19 epidemic, direct tax collections consistently increased.  
After applying the grey forecasting model, the forecasted total revenue collection from direct tax, around INR 16.63 trillion in 2022-23, is set to become INR 30.67 trillion by 2029-30. On the other hand, indirect tax collection, which is currently around INR 13.82 trillion, is set to become INR 25.70 trillion. Total tax collection from direct and indirect taxes is set to increase from INR 30.45 trillion in 2022-23 to INR 56.37 trillion in 2029-30.  
Moreover, the findings reveal consistent revenue growth, as the prediction indicates a robust increase in direct and indirect tax revenues. This suggests that the government can expect increased revenues to sustain public spending and long-term investments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & \multicolumn{2}{c}{**RPE**} & \multicolumn{2}{c}{**Year**} & \multicolumn{2}{c}{**RPE**} \\ \hline  & **Direct Tax** & **Indirect Tax** & & **Direct Tax** & **Indirect Tax** \\ \hline
2000–01 & 0.00\% & 0.00\% & 2012–13 & \(-\)0.20\% & \(-\)2.71\% \\ \hline
2001–02 & 169.50\% & \(-\)40.88\% & 2013–14 & 3.00\% & \(-\)8.16\% \\ \hline
2002–03 & 148.10\% & \(-\)37.47\%  
Increased tax revenue correlates with expected economic growth. This means that India's economic policies will likely continue to promote growth, boosting the country's budgetary position. Revenue growth is expected to be driven not only by economic causes but also by improvements in tax compliance. Continued efforts to improve tax enforcement and administration, including digitalization, will be critical in realizing this promise.  
Despite the hurdles of the COVID-19 pandemic, the grey forecasting model remained consistent. This demonstrates its stability under unpredictable situations, making it an important tool for future economic forecasting. Based on the robustness of this model, this study emphasizes that policymakers may become more reliant on it for strategic fiscal planning and resource allocation. With total tax income predicted to increase from INR30.45 trillion in 2022-23 to INR56.37 trillion by 2029-30, the government is expected to have more funds to invest in social programs, infrastructure, and other public services.
# Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
## 2 Proposed Methodology  
Broken bar fault detection is one of the most challenging conditions in an induction motor because the motor continues working without notable malfunctions. However, one motor bar's damage extends the fault condition to the attached bars, consequently increasing energy consumption. The presence of a broken bar can be detected in the current signal. Therefore, the increased current demand represents multiple broken bars facilitating the diagnosis.  
Figure 1 shows a global description of the proposed method for detecting one broken rotor bar (1 BRB), two broken rotor bars (2 BRB), and health in induction motors. An instrumented test bench allows the acquisition of one phase's current signal represented by \(S(n)\) to gather the database used in the experimental tests. Several electronic components allow the signal acquisition and conditioning of the motor current signal. The sum histogram \(h_{s}\) and difference histogram \(h_{d}\) are computed for a predefined window size \(Ws\). Then, the texture feature module extracts nine features from \(h_{s}\) and \(h_{d}\) histograms considering different displacements for the sliding window, defined as \(\Delta\). Considering different displacements and their combinations, one vector is stored for each texture feature and displacement. The fault classifier module performs three tests, Test A, Test B, and Test C. During Test A and Test B, all texture features are considered, and different \(\Delta\) values are predefined. The displacements used for each test are defined in Section 2.3. The best classification resulting from Test A and B is used to train a decision tree classifier in Test C for all texture features. Then, Test C allows selection of the texture feature with the highest performance. After that, the chosen selected feature is used to train two more classifiers: a bagged decision tree and a k-nearest neighbor (KNN) classifier.
## 3 Results and Discussion
### Self-Discharge Study
The results of the mechanical flexibility testing performed on the MSC device show that it has excellent flexible properties and does not degrade even under extreme bending conditions, making it an ideal choice for applications that require flexibility and robustness.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 3 Proposed Scheme
### Image Protection Method
#### 3.1.1 The Initialization Phase  
Based on the users' intention, the image manager institutes the access policy \(T\) which comprises \(\alpha\) disjoint authorized classes \(C_{j}\) for \(j=1,\,2,\,\ldots,\,\alpha\). Each user \(U_{i}\) should belong to a single authorized class \(C_{j}\), and each authorized class may contain one or several users. The access policy defines \(C_{w}\leq C_{r}\) if \(C_{r}\) can access the data of \(C_{w}\).  
First, the image manager chooses two prime numbers \(p\) and \(q\) and \(g\) which is the primitive root modulo \(n\), where \(n=p\times q\). Then, the image manager executes Key Generation Algorithm (Algorithm 1). After \(C_{j}\) generates a public key \(e_{j}\), an encrypted key \(SK_{j}\), a derivative key \(DK_{j}\) and partial authorized information \(PT_{j}\), the image manager sends \(PT_{j}\)= \(\{C_{r}\mid C_{r}\leq C_{j}\}\), \(DK_{j}\), and \(\{e_{r}\mid C_{r}\leq C_{j}\}\) and \(C_{r}\in\{C_{1},C_{2},\,\ldots,\,C_{\alpha}\}\) to user \(U_{i}\in C_{j}\) through a secure channel. The image manager keeps \(SK_{j}\) secret and sends \(T\) and \(e_{j}\) to the cloud server. The initialization phase is given in Key Generation Algorithm (Algorithm 1).  
Figure 1: A framework for supporting different privacy-preserved access rights in a single image.
## 5 Change Rule of Displacement, Velocity, and Acceleration
### Elastic Modulus of Soil Layer  
When the elastic modulus of soil layer changes, the change rules of displacement, velocity, and acceleration at different measuring points of civil air defense construction caused by vehicle vibration are shown in Figure 21. The results of displacement, velocity, and acceleration peak values at mid\(-\)span measuring points are shown in Table 6. The change rules of displacement, velocity, and acceleration peak values at mid\(-\)span measuring points are shown in Figure 22. When the elastic modulus of soil layer is 1, 3, 5, and 7 MPa, the peak value of mid\(-\)span displacement of the roof of civil air defense construction is 81.4519, 27.3133, 16.625, and 12.0389 mm, the peak value of velocity is 15.4133, 5.2883, 3.27, and 2.37 mm\(\cdot\)s\({}^{-1}\), and the peak value of acceleration is 56.3889, 18.25, 16.6667, and 16.6667 mm\(\cdot\)s\({}^{-2}\). The results show that the peak values of displacement, velocity, and acceleration of roof mid\(-\)span decrease with the increase in elastic modulus  
Figure 19: Peak value variation rule of different measuring points when the density of soil layer changes (**a**) displacement, (**b**) velocity, and (**c**) acceleration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density of Soil Layer (kg\(\cdot\)m\({}^{-3}\))** & **Displacement (mm)** & **Velocity (mm\(\cdot\)s\({}^{-1}\))** & **Acceleration (mm\(\cdot\)s\({}^{-2}\))** \\ \hline
1750 & 26.623 & 5.1417 & 31.1389 \\
1800 & 27.3133 & 5.2883 & 31.9167 \\
1850 & 28.0004 & 5.4267 & 32.5278 \\
1900 & 28.6927 & 5.57 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of peak displacement, velocity, and acceleration at the mid\(-\)span measuring point when the density of soil layer changes.  
Figure 20: The law of mid\(-\)span peak value changing with density of soil layer (**a**) displacement, (**b**) velocity, and (**c**) acceleration.  
of soil layer. With the increase in elastic modulus of soil layer by 200%, 400%, and 600%, the corresponding peak values of roof mid\(-\)span displacement decrease by 66.47%, 79.59%, and 85.22%, respectively, the peak values of velocity decrease by 65.69%, 78.78%, and 84.62%, respectively, and the peak values of acceleration decrease by 43.40%, 54.83%, and 61.03%, respectively.
# Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review
## 2 Existing Breast Cancer Detection Techniques & Technologies
### Body Image-Based Technology
#### 2.1.1 Ultrasound  
Ultrasound imaging (sonogram) is a medical tool that uses a high frequency of sound waves (echoes) to obtain real-time images of the body's internal structures or detect suspicious nodular formations without involving ionizing radiation compared to MRI and mammogram. Ultrasound is a low-cost and non-invasive procedure for patients. There are two significant functions of ultrasound which are for diagnostic of internal body and pregnancy. The frequency range commonly employed in medical ultrasound is 2 to 18 megahertz, hundreds of times above the human hearing range [34; 35; 36]. A transducer is a device that rubs the patient's skin across the area being investigated during an ultrasound. Although ultrasound is often used to prevent an invasive measure for diagnosis, sometimes it may overlook detecting the smaller size of masses that will carry false-positive and false-negative results. Ultrasound is suitable for women below 45 years and women with dense breasts, while mammography has progressive sensitivity in women 60 years and above [37]. The samples of ultrasound breast images are illustrated in Figure 4 and categorized into three classes, which are normal, benign, and malignant.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Type** & **Advantages** & **Disadvantages** & **Technique** \\ \hline Ultrasound & Inexpensive & Low resolution & This technology uses a high frequency of sound waves to produce an image of organs and structures within the body \\ \hline Mammogram & The old standard & False negative & This imaging uses a low-dose (ionizing radiation) \\  & Regular check-up & Not convenient & \(\text{Compressed}\) & \(\text{X-ray system to discover inside the breast}\) \\  & Provide high & & Ionizing & \(\text{MRI uses magnetic fields}\) \\ \hline MRI & High sensitivity & Long scan time & \(\text{Claustrophobia}\) & \(\text{MRI uses magnetic fields}\) and radio waves to create detailed images of organs and tissues in the body \\  & Image in any angle & \(\text{Painless}\) & \(\text{Expensive}\) & \(\text{and radio waves to create detailed images of organs and tissues in the body}\) \\  & Nonionizing & & \(\text{meas widely available}\) & \\ \hline Microwave & Nonionizing & & \(\text{Not available}\) & \(\text{This technology}\) uses a microwave \\ Imaging & Non-invasive & & \(\text{in clinic}\) & \(\text{This technology}\) uses a microwave \\  & Inexpensive & & \(\text{or hospital}\) & \\  & Comfortable & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Advantages and disadvantages of various imaging modalities in breast cancer screening [30; 31; 32; 33].
## 4 AI Applications and Their Impact
### Textile Sorting and Recycling
#### 4.2.2 Garment Categorization for Reuse  
[PERSON] et al. [13] proposed an AI-driven machine vision system utilizing attention mechanisms for classifying post-consumer garments. The dataset comprised an initial 19,800 images across \(11\) garment categories, followed by a further 7033 for testing, ensuring robustness in real-world applications for sorting garments by archetype. The system achieved superior performance compared to conventional sorting methods but faced limitations in handling garments with overlapping features or heavily degraded fabrics and was unable to sort according to fiber type.
## 4 Conclusions  
This study explored the removal efficiency, mechanism, and a novel advanced oxidation route for Et-PABA in a water environment using the UV/PDS combined process. The results showed that the combined treatment could significantly improve the degradation of Et-PABA compared to the single processes. The removal efficiency of Et-PABA after the UV/PDS combined process can reach 98.7% in 30 min. The removal rate of Et-PABA increased rapidly upon increasing the dosage of oxidant.  
Figure 5: The degradation of Et-PABA in real water using the UV/PDS process. Conditions: [Et-PABA] = 0.05 mM, [PDS] = 2.0 mM, and without pH adjustment.  
Figure 6: Proposed reaction pathways for the oxidation of Et-PABA using the UV/PDS process.  
The presence of anions had no obvious effect on the removal, while HA suppressed the removal of Et-PABA. Moreover, the UV/PDS process had good adaptability to Et-PABA removal of real water samples. Finally, a degradation path of Et-PABA was proposed based on the six detected TPs during the oxidation process. Overall, the UV/PDS process can be used as a promising method for the removal of PABA-type UV filters in water.  
Conceptualization--[PERSON] and PS.; Formal analysis--[PERSON] and [PERSON]; Investigation--[PERSON], [PERSON], and [PERSON]; Supervision--[PERSON]; Validation--[PERSON] and [PERSON]; Writing of original draft--[PERSON]; Writing of review & editing--[PERSON].  
This research was funded by the National Natural Science Foundation of China (21607058); the Zhejiang Provincial Department of Education General Research Project (Y201840526); the Student Research Innovation Team Funding Project of Zhejiang (2019R417027); the Student Research Training Program of Jiaxing University (2019); and the Key Project of Nanhu college of Jiaxing University (2018).  
We wish to thank anonymous reviewers for their comments, which improved the paper greatly.  
The authors declare no conflict of interest.