esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
In [8], a satisfactory nighttime detection performance was achieved when integrating sensors and SVMs. However, multiple cues are required from vehicle lights and bodies. When the degree of illumination is poor, such as under rainy and dusky conditions, the performance may be affected. The method in [15] trained AdaBoost classifier for headlight detection in order to reduce the false detection raised by reflections; headlight pairing was also conducted, and then the paired headlights were tracked. This method was heavily reliyant on detection results of vehicles' headlights. Although using machine learning technique is a modern way to perform vehicle detection, especially in nighttime conditions, it may not obtain satisfactory detection performance under extremely dark conditions. The modern methods of performing nighttime detection involve the use of CNNs. A study [16] on nighttime human detection with CNNs indicated that the images captured by visible-light cameras can cope well with CNNs because the neural network model can adapt to the marginal difference in the nighttime images to filter and capture the features of the object. The study used feature engineering, such as histogram equalization, to maximize the difference between the desired object and the background. However, frequently using histogram equalization may increase the processing time and we may be obliged to seek an end-to-end parallelizable algorithm that can be embedded inside vehicles with changing non-model-related configurations. Another study [17] used a visible-light camera and the Faster R-CNN [5] to deal with nighttime face detection. The study used histogram equalization before passing the images to the Faster R-CNN [5]. Because the human face is difficult to capture, one instance of the Faster R-CNN [5] was first used to detect the human body, followed by cropping of the upper body areas. Each cropped part was passed to another instance of the Faster R-CNN [5] to perform face detection. An acceptable processing speed could be reached because end-to-end models were used. To overcome the aforementioned limitations, the proposed system integrates several optimized models based on the Faster R-CNN [5], as well as an efficient data labeling scheme to achieve satisfactory performance in nighttime vehicle detection.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
###### Abstract  
Tropical Storm Imelda struck the southeast coastal regions of Texas from 17-19 September, 2019, and delivered precipitation above 500 mm over about 6000 km\({}^{2}\). The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between \(-10\%\) and \(10\%\)) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.  
satellite data; GPM; IMERG; radar; Imelda; rainfall estimates; precipitation; remote sensing; performance evaluation
### Research Purpose, Questions, Value, Innovation and Structure
#### 1.4.3 Theoretical and Practical Value  
This study integrates theories from landscape design, psychology, cultural studies, and more, offering a new perspective for interdisciplinary research. Employing a methodological approach to studying the color landscapes of minority areas, it provides theoretical support for preserving and transmitting cultural heritage. A systematic color-area ratio quantification model was constructed in this research. Through the model, objective color perception can offer useful information for urban design. The color environment shares historical and cultural information and showcases the city's cultural and artistic characteristics [16]. The research outcomes can be directly applied to urban culture and art research.  
The practical significance of the article is that it provides a theoretical to quantify color in a way that translates the laymen's perceptual color understanding into an actionable, data-driven color landscape plan. It provides landscape designers and architects with the idea of new color-area ratio designs in the Tibetan region. As conservation of these uniquely colored landscapes has the potential to attract more tourists, the results of this research may be used to boost the region's tourism and subsequent economic development. Furthermore, it can increase public awareness of environmental stewardship by protecting and highlighting the importance of ethnic minority groups' special landscape colors.  
By integrating theoretical with practical concerns, this research demonstrates rigor and cutting-edge qualities in the academic field, as well as importance in practical applications. Its outcomes not only offer useful insights to designers and planners but also provide a significant theoretical and practical foundation for the protection of minority landscape cultural heritage. Moreover, by involving public participation and quantifying emotional cognition, this study pioneers a new direction in landscape design, creating new possibilities for the display of urban culture and art, as well as for creating a healthy color environment conducive to mental and physical well-being.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of the Location of Concentrated Load  
#### 4.2.1 Location Identification  
When the concentrated force is located at measuring points M4, M8, M13, M19, M21 and M24, the deflection of measuring points is optimized when the damage of cable M16 is 15% and the damage of cable M7 is 40%, and the damage cable location is identified according to the damage cable location identification method. Figure 16 is the wavelet coefficient distribution curve of the first derivative of the deflection difference of the girder when the concentrated force acts on different measuring points, and the legend represents the measuring point position of the concentrated force.  
Figure 15: Wavelet coefficient distribution curves of the first derivative of beam deflection difference under different damage conditions after adding white noise. (**a**) Single-cable damage, (**b**) double-cable damage.  
According to Figure 16, when the concentrated force acts on different measuring points, the wavelet coefficient of the first derivative of the deflection difference of the girder changes monotonically from negative to positive twice through the 0 point. One 0 point is located at the anchorage point of cable M7, and the other 0 point is located near the anchorage point of cable M16. When the acting position of the concentrated force is close to the cable tower (such as M4, M8) and the support (such as M24), the 0 point is far from the anchorage point of cable M16, and the accuracy of damage cable location identification decreases. It can be seen that the location of the concentrated force has an impact on the identification accuracy of the damage cable position. The reason is that when the location of the concentrated force is far away from the span, the impact on the deflection of the girder is small, and the error of the damage location identification is large.
## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite** & **Track** & **Pass** & \begin{tabular}{c} **Number** \\ **of Scenes** \\ \end{tabular} & \begin{tabular}{c} **Acquisition** \\ **Period** \\ \end{tabular} & **Master** & \begin{tabular}{c} **Processing** \\ **Software** \\ \end{tabular} & \begin{tabular}{c} **Avg PS Density** \\ **(PS/km2)** \\ \end{tabular} \\ \hline ERS 1/2 & 423 & Descending & 78 & 1992–2006 & 18 February 1998 & \
\begin{tabular}{c} ROI\_PAC, Doris, \\ StaMPS \\ \end{tabular} & 181 \\ \hline Envisat & 423 & Descending & 73 & 2003–2010 & 15 August 2007 &
\begin{tabular}{c} ROI\_PAC, Doris, \\ StaMPS \\ \end{tabular} & 203 \\ \hline TerraSAR-X & 48 & Descending & 74 & 2011–2014 & 13 May 2013 & Doris, StaMPS & 2713 \\ \hline Sentinel 1 & 37 & Descending & 64 & 2014–2017 & 27 October 2016 & Sarproz & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the processed datasets. PS denotes Persistent Scatterer. carried out in Sarproz, which can handle individual swaths of TOPSAR data [29]. Throughout the workflow the recommended/default settings were used. A significant difference with StaMPS is the use of weather data (temperature, atmospheric pressure, humidity, precipitation) for the estimation and removal of atmospheric phase. Figure 4: Normal Baselines versus Time acquisition graphs for the four SAR image datasets. Figure 3: Map of Belgium showing the region of interest (Brussels Region), superimposed by the different footprints of the ERS, ENVISAT, TerraSAR-X and Sentinel 1 datasets used in this study and location of the reference point (red triangle). Only PS points with a coherence value larger than 0.70 were used in further analyses. If the observed displacements are predominantly in a vertical direction and considering the differences in incidence angle between the datasets, the line-of-sight (LOS) displacements were converted to vertical displacements: \(\mathrm{ddispl}=\mathrm{dLOS/cos\theta}\), where \(\mathrm{ddispl}\) is the vertical displacement, \(\mathrm{dLOS}\) is the LOS displacement and \(\theta\) is the average incidence angle. The time series of displacements were merged for common PS points by assuming a linear displacement in time gaps and minimizing the offset between datasets. PS points were considered to be common to the nearest point in other datasets within a radius of 20 m.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 2 Fault Factors and Indicators of Wheel Hub Bearings
### Fault Indicators of Wheel Hub Bearings
#### 2.2.1 Inherent Vibrational Frequency of Wheel Hub Bearings  
A forced vibration is caused by the components of the wheel hub bearings when the wheel hub bearings are working in a normal condition. The inherent vibrational frequency depends on the mechanism, material, shape., and quality of the bearing itself, and is not affected by the running speed.  
The inherent frequency of the rolling element is obtained in Equation (1) when the material of the bearing is steel.  
\[f_{b}=\frac{0.424}{r}\sqrt{\frac{E}{2\rho}} \tag{1}\]  
Here, \(r\) is the radius of the rolling element radius (m). \(E\) is the modulus of elasticity (N/m\({}^{2}\)). \(\rho\) is the density of material (kg/m\({}^{3}\)).  
The inherent frequency of the cage is obtained in Equation (2).  
\[f_{c}=\frac{n(n^{2}-1)}{2\pi\sqrt{n^{2}+1}}a^{-2}\sqrt{\frac{EI}{M}} \tag{2}\]  
where \(n\) is the number of rolling elements, \(a\) is the radius of the rotary axis, \(I\) is the moment of inertia, and \(M\) is the mass per unit length of the material.  
The inherent frequency of the inner and outer rings of the wheel hub bearings is obtained in Equation (3).  
\[f_{n}=9.40\times 10^{5}\times\frac{h}{D^{2}}\times\frac{n(n^{2}-1)}{\sqrt{n^{2}+ 1}} \tag{3}\]  
where \(h\) is the thickness of the ring and \(D\) is the diameter of the neutral axis of the ring.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 10 Open Issues and Future Directions
### Empowering Patients with Control and Transparency  
* Context-Aware and Adaptive Privacy Mechanisms: Future research should highlight the need for continuous user education about privacy risks and protections. It should explore how AI could dynamically adjust privacy settings based on patient preferences, locations, and real-time conditions. This should be illustrated with real-world scenarios where such adjustments would be beneficial [57].
* User-Friendly Privacy Controls and Educational Tools: Complex interfaces hinder informed consent and effective data management. Research should prioritize developing intuitive privacy controls and engaging educational tools that clearly explain data usage, protection mechanisms, and user options. This will foster trust and cultivate a culture of privacy-conscious engagement [58].
# Measurement Method of Whole Mechanical Automatic Vertical Drilling Tools
## 4 Conclusions  
In this paper, a comprehensive mathematical model was introduced aimed at deriving the angular range of a plat valve, utilizing the thrust generated during vertical drilling operations. Furthermore, a precise measurement methodology was devised for acquiring the crucial parameters of the MVDT. The actual deviation angle range of N-MVDT and O-MVDT vertical drilling was calculated using data obtained from measurement and input into the model.  
The pushing forces of the N-MVDT and the O-MVDT at different angles, weights on the bit, and rotational speed were measured by the measuring device. Based on experience, this paper quantitatively analyzed the influence of the TID on the stability of the N-MVDT. Based on the experimental findings presented herein, it was verified that the thrust force exhibited by the modified N-MNDT underwent a lesser decrease compared with that of the O-MVDT, which was attributable to the pressure loss incurred by the conditioning apparatus. This issue must be addressed in the future through the optimization and enhancement of the flow structure. Moreover, if the N-MVDT is to be implemented in a field production setting, additional research is imperative to further bolster its anti-deviation performance under smaller inclination angles (specifically, less than 0.5\({}^{\circ}\)), thereby emerging as a pivotal direction for future investigations.  
Investigation and writing, Y.W. and C.M.; review and editing, K.Z.; supervision, B.L. All authors have read and agreed to the published version of the manuscript. This research was funded by [The National Key R&D Program of China] grant number [No. 2021 YFA0719104] and [No. 2018 YFC0603405].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. Data confidentiality is applicable because of the relevant information about the drilling tools mentioned in this article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prototypes** & \
\begin{tabular}{c} **Range of \(\varphi\) in** \\ **Simulation** \\ \end{tabular} & **Range of \(\varphi\) in Model** &
\begin{tabular}{c} **Relative Error in** \\ **Positive \(\varphi\)** \\ \end{tabular} \\ \hline O-MVDT & \([0^{\circ},\,53^{\circ}]\) & \([-86^{\circ},58^{\circ}]\) & 9.43\% \\ N-MVDT & \([0^{\circ},\,12^{\circ}]\) & \([-14^{\circ},\,14^{\circ}]\) & 16.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the range of \(\varphi\) in the mathematical model and simulation.
## 2 UAV-Enabled ITS Framework
### First Layer: Technical Specifications
#### 2.1.1 Design and Specifications
As an alternative to LiPo, Lithium-ion (Li-Ion) batteries offer higher energy density, making them preferable in the application of larger UAVs that require longer flight times [22]. Another alternative is lithium iron phosphate (LiFePO4) batteries, known for their high thermal stability and extended cycle life. LiFePO4 batteries are often preferred in applications where safety and longevity are prioritized, although they are typically heavier and have lower energy density than LiPo and Li-Ion [23]. Moreover, UAV software helps in flight planning, stores and processes sensor data, and provides intelligent navigation. Selecting the appropriate UAV software is crucial, de  
Figure 2: This diagram illustrates the three main categories of UAVs. pending on the specific application. The minimum requirements for effective UAV software should include automated flight plans, augmented views, and image geo-rectification [24]. Some software currently used for data capturing and processing in UAV applications includes Pix4 Dmatic, DJI Terra, Global Mapper, Trendspek, and Nira [25]. Software verification also plays a vital role in safeguarding UAVs from cyber-attacks, ensuring the integrity and reliability of the data collected, preventing unauthorized access to control systems, and maintaining overall operational security.
## 3 Results and Analysis
### The role of urbanization in the chain mediation of carbon emissions  
It can be seen from the results of Table 8\(lnG\)(1) that, when the significance level is 1%, urbanization (\(lnU\)) and the population (\(lnP\)) effectively promote the improvement of the regional economic development level (\(lnG\)). The elastic coefficients are 0.362 and 0.243, respectively. The \(lnG\)(2) column shows that the social employment level (\(lnS\)) has a significant positive effect on regional economic development (\(lnG\)), with a significance level of 10%, and its elastic coefficient is 0.098. The \(lnS\) column shows that urbanization (\(lnU\)) significantly promotes social employment (\(lnS\)) at a level of significance of 1%. However, the \(lnY\)(3) column shows that the social employment level (\(lnS\)) is included in the model regression, and it is found that \(lnS\) has no significant effect on \(lnY\) when the significance level is 10%. It can be seen that the chain intermediary effect of the pathway from urbanization (\(lnU\)) \(\rightarrow\) social employment level (\(lnS\)) \(\rightarrow\) economic development level (\(lnG\)) \(\rightarrow\) carbon emission (\(lnY\)) is not significant.  
According to the above analysis, Figure 4 shows the significant impact path of urbanization on carbon emissions.  
In Figure 4, \"+\" represents a positive promotion effect, the proportion of second industry (\(lnC_{1}\)) has no significant positive effect on carbon emissions (\(lnY\)). It can be seen that the pathway of urbanization \(\rightarrow\) the proportion of second industry (the proportion of third industry, economic development level) \(\rightarrow\) carbon emissions and economic development \(\rightarrow\) urbanization \(\rightarrow\) carbon emissions plays a significant role.
## 3 Data Analysis of Similarity Calculation Method for Infield and Outfield Environmental Tests
### Similarity Analysis of Infield and Outfield Tests  
Comparing the measured data of the infield and outfield low-temperature steady-state response of the two materials, this study found that differences existed in the low-temperature hardening. In view of the different elastic responses of the two rubber sealing materials to the low-temperature infield and outfield, three fitting curves were then used to translate the infield test curves to study the similarity of the infield and outfield curves based on the existing outfield curves. The translation takes the translation \(dT\) as the transformation parameter, and the goodness of fit of the curve derived from the infield and the data from the outfield as the transformation parameter. The similar transformation parameters of the translation transformation of the three fitting methods are shown in Table 5. The goodness of fit is shown in Table 6.  
Figure 8: Fitting of the test results. (**a**) Polynomial fitting curves in springback test (**b**) Polynomial fitting curves in elongation test (**c**) Exponential fitting curves in springback test (**d**) Exponential fitting curves in elongation test (**e**) Fourier fitting curves in springback test (**f**) Fourier fitting curves in elongation test.  
The infield and outfield translation transformation show desirable fitting results. Thus, the similarity of the infield and outfield curves can be considered good. Comparing the results of the infield and outfield transformations with the three fitting methods, this study found that polynomial fitting is equivalent to Fourier fitting in curve fitting, but that the goodness of fit of the outfield points is higher for the poor translation transformation of the infield test curve, and has poor goodness of fit and transformation of the exponential fitting.  
A comparison of the two materials shows that the similarity of their infield and outfield curves is obvious, because the change of the material properties in the infield and outfield tests of the samples from the same material obeys the same change law (i.e., it hardens gradually as the temperature decreases), the points on the infield curve and the outfield curve correspond one by one.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 4 Discussion  
When formulating this study, one of the open questions was to what extent the locally visible infrastructure had implemented 5G in accordance with the available specifications. Indeed, the user phones announce 5G coverage, yet when a radio reporting app is used to extract the parameters of the base station to which the device has associated, an LTE link status is given. Over the previous years, there have been lawsuits in the United States where service providers have been sued for deceptive advertising of the extent or capability of their 5G coverage. But regardless of the outcomes, the authors believe that part of the confusion arises from the large amount of flexibility in the definitions of what a 5G signal is or can be [32]. The publicly available specifications indicate that 5G is a multidimensional set of optional features and configurations, which might explain the inconsistent link status reporting, as well as the observed variability in the source timing stabilities and solutions implemented by the network service providers as measured in this study.