esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Horizontal Pipeline Transportation Characteristics of Coarse Particle
## 4 Results and Discussion
### Analysis of Coarse Particle Flow Characteristics
Even when the velocity reaches 1.5 m/s, a stationary deposition layer persists at a material volume concentration of 15% in the two-phase flow. With further increases in the mixed average velocity, the proportions of both the nudging motion layer and the suspension motion layer gradually increase. Figure 15: The proportion of particle layers in different motion states to the cross-sectional area of the pipeline (particle diameter d = 1.5 mm). (**a**) Material volume concentration Cv = 10%; (**b**) material volume concentration Cv = 15%; (**c**) material volume concentration Cv = 20%. As illustrated in Figure 16, for particles with a diameter of 2.5 mm, a stationary sedimentary layer persists when the mixed average velocity is below 1.5 m/s. When the velocity reaches 2.0 m/s, the proportion of the clear-water layer drops below 50%, and the transporting movement layer becomes dominant. As the mixed average velocity increases further, the proportions of both the transporting movement layer and the suspending movement layer rise, while the clear-water layer proportion decreases. By analyzing the image, it can be observed that an increase in the material volume concentration and a change in particle size mostly result in an increase in the cross-sectional area of the stationary sedimentary layer in the low-speed region (\(V_{m}<\) 1.75 m/s). At this point, the average mixing velocity required for the coarse particle group to enter the translational flow pattern is greater, and the transition of the coarse particle group flow pattern is delayed. The proportion of the cross-sectional area of the suspended motion layer for the three particle sizes increases with the increase in the average mixing velocity, but the increase  
motion layer for the other two large particle sizes is relatively stable. Reflected in the transition of the coarse particle group flow pattern, it can be found that the coarse particle group flow pattern with a diameter of 0.9 mm can quickly complete the transition from a translational motion flow pattern to a non-homogeneous flow suspension motion flow pattern, while the other two types of coarse particle group flow patterns with larger particle sizes still mainly rely on translational motion.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Advantages of C-RAN
#### 2.5.3 Convenience of Operation and Maintenance  
The centralized architecture of a C-RAN makes it scalable and simplifies its upgrade and maintenance. Although the number of RRHs in C-RAN architecture may not be decreased, its functionality becomes more convenient as the size of the tower reduces, and RRH can sit on poles with minimum site support and management. To increase the coverage of a network, MNOs only have to install an RRH on the cell site and associate a BBU with that RRH in the cloud data center. C-RAN provides the facility to install virtual resources on the cloud depending on the user demand. Moreover, the maintenance of an RRH becomes more convenient in C-RAN. In addition, as BBUs are centralized in a cloud, in addition to cost reduction, the management of the BBU pool becomes simpler compared to that for a traditional RAN.
## 4 Results and Discussion  
In the Results and Discussion section, we present and analyze the data obtained from the DInSAR technique employed to monitor surface deformations in the area of San Marco dei Cavoti. The study focuses on ascending geometry from 6 January 2017 to 12 November 2022 and descending geometry from 28 January 2017 to 29 October 2022. The components obtained from interferometry are also analyzed to characterize the detected slope movements. The section will start with an overview of the data processing and analysis methods, followed by a detailed discussion of the observed deformation patterns.
# Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter
## 2 Parameter Estimation with Kalman Filter
### Joint Kalman Filter  
Let \(\theta_{k}\) be an unknown parameter and assume that it is modeled by [12; 13]:  
\[\theta_{k+1}=\theta_{k}+w_{\theta k} \tag{8}\]  
where, the parameter \(\theta_{k}\) corresponds to a stationary process with the identity state transition matrix driven by process noise \(w_{\theta k}\). To identify the unknown parameter with a Kalman filter, an augmented dynamic model is constructed with the augmented state vector defined by  
\[x_{a}=\left[\begin{array}{c}x\\ \theta\end{array}\right]. \tag{9}\]  
The augmented system yields to:  
\[\begin{array}{l}x_{a_{k+1}}=\left[\begin{array}{c}x_{k+1}\\ \theta_{k+1}\end{array}\right]=\left[\begin{array}{c}f(x_{k},u_{k},\theta_{ k})\\ \theta_{k}\end{array}\right]+\left[\begin{array}{c}w_{k}\\ w_{\theta k}\end{array}\right]\\ z_{k}=h(x_{k},\theta_{k})+v_{k}\end{array} \tag{10}\]  
However, the augmented system is presented as a nonlinear function due to the state multiplication even if the original system in Equation (5) is linear. To handle this problem, extended Kalman filter (EKF) or unscented Kalman filter (UKF) can be used. In this paper, EKF is considered.
# Combination of Variable Loads in Structural Design
## 6 Conclusions  
This research recommends the removal of combination factors from codes as these factors are unity, \(\psi=1\), can be approximated at unity, or apply to rare load cases only:  
* Imposed loads are combined with each other without a combination factor, \(\psi=1\).
* The combination factor for a one-week constant snow load during mid-winter is \(\psi=0.8\), and the one-month factor is \(\psi=0.85\).
* The combination factor of wind is \(\psi=0.75\). Such a combination is rare as it applies to wind-imposed loads only. These factors apply to loads \(V_{\text{Q}}=0.4\); loads \(V_{\text{Q}}<0.4\) result in higher factors.  
The combination factors are meaningful in light structures only and merely in cases of about two equal variable loads and virtually in one load case only: timber or steel roofs with about equal snow and wind.  
The existing model for variable load modelling, relying on a single combination factor, is deemed inadequate. While it offers a notable advantage when dealing with rare cases with loads of similar magnitudes, its effectiveness diminishes in scenarios involving frequent cases with loads of varying magnitudes, including a harmful unsafe error.  
Despite offering a modest overall reliability advantage of around 2-3%, given the unavoidable errors and approximations of approximately 7% that are inherent in structural codes due to the difficulty in perfectly accounting for all parameters in reliability models, the effect of combination factors is negligible.  
Removing the combination factors from the codes would be a simple task. This can be achieved by removing the combination factors from the relevant equations, leaving the rest of the equations and the design process unchanged. The resulting design without combination factors maintains safety levels comparable to designs incorporating combination factors.  
Regarding the nature of the loads, the combination is calculated here either dependently or independently. However, the conclusions remain valid even if the dominant approach of independent combination is applied.  
In light of their limited reliability advantage and the significant complexity and additional work they introduce to the design process, the removal of combination factors from structural codes is warranted. This study strongly recommends their complete removal to streamline structural design processes and mitigate unnecessary complexities.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article. The data presented in the figures of this study can all be reproduced using the equations given in the study.  
[PERSON] commented on the mathematical formulation.  
The author declares no conflicts of interest.
# A Brief Review on Micro-Implants and Their Use in Orthodontics and Dentofacial Orthopaedics
## 3 Results
### Immediate Loading  
In the literature, there has been much discussion about the possibility of immediate loading of mini-implants and how it affects the stability of the screw and bone structure.  
Immediate loading doesn't affect the osseointegration of OMIs but the anchorage is not always absolutely stationary, extrusion and tipping were observed in areas with thin cortical bone [28]. Immediately loading with orthodontic forces of 200 g does not influence significantly the stability [7] and seemed to accelerate the shaping of periosteal bone after the surgical intervention; there were no statistically significant differences in bone and implant contact values between the loaded OMIs and the unloaded ones [28].  
Another study [29] found by histological analysis good osseointegration, bone apposition, and new bone formation in loaded and unloaded OMIs. The contact between bone and micro-implant was higher in the loaded ones. The study concluded that small diameters (1.2-1.3) OMIs made from Titanium alloy are strong enough for immediate loading even in thin cortical bone areas; in this situation drilling a pilot hole reduces the possibility of micro-implants' breakage.  
Comparing immediate loading with one-week post-insertion loading of orthodontic micro-implants showed a statistically higher torque loss in delayed insertion; a significant stability loss was seen in both situations in the first week of investigation [30].  
Investigating the biomechanical properties of bone around OMIs under immediate loading using nanoindentation testing [31] showed that the trabecular area on the compression site near the implant was significantly harder than in other bone locations.  
Another study based on histological, histological-morphometric, and cone beam computed tomography (CBCT) analysis were performed on autoclave-sterilized OMIs and proved that an immediate, light orthodontic load did not influence the bone healing around mini-screws [32]. The osseointegration and the cortical bone thickness increased with the time passed from the insertion of the implants. The absence of infections during the healing showed that OMIs can be autoclaved in the dental practice before insertion time, with no effect on subsequent osseointegration. The predrilling of thick cortical bone reduced the microfractures. The displacement of the periosteum stimulated the healing of the cortical bone [32].  
A review article showed the efficiency of using mini-implants as anchorage and concluded that their success depends on proper initial stability and the quality and quantity of loading [16]. Other factors that could compromise the success of using OMIs are the patient's oral hygiene, coexisting pathologies, smoking, the condition of the mucosa, the timing, quantity, and direction of the loading force direction [2; 7]. Thus, the micro-implants' success involves factors related to the patient, the orthodontist, and the OMI's design [2; 7].
## 2 Related Work
### Public Opinion Scene Text Recognition
#### 2.1.1 Text Detection  
Typically, traditional detection methods [19] and deep learning detection methods [20; 21] are classified as detection methods in the field of text detection. First, the original data need to be preprocessed, which includes data segmentation, noise reduction, correction, and compression, among others. The preprocessing step can effectively resolve issues with uneven illumination, blurry handwriting, and the removal of sensitive information. The majority of traditional text detection methods rely on manual design features to capture the target text attributes. However, the majority of them are unable to solve for the effects of image blurring [22] and text distortion [23] on the detection results. In order to effectively solve the aforementioned constraints, deep learning methods extract useful features from training data. Regression-based methods and segmentation-based methods classify the majority of deep learning text detection techniques [24]. For regression-based detection methods, academics typically make use of widely used networks, such as Faster R-CNN [25], YOLO [26], CTPN [27], TextBoxes [28], and PCR [29]. However, these detection methods are ineffective at detecting text sequences that are infinitely long, and it can be difficult to obtain text wrap-around curves that are smooth. In order to solve that problem, academics have proposed image segmentation-based text detection algorithms. The segmentation-based detection method regards the detection problem as a text classification problem by predicting text instances at the pixel level and then detecting multiangle, irregular text using the concept of semantic segmentation. Mainstream network topologies primarily consist of PSENet [30], DB [31], and FCENet [32], which are superior to regression-based detection methods. Other academics have proposed combined regression and segmentation detection methods, which would make it easier to find irregular text, reduce false alarms, and make text more robust at different scales, among other things. The main network models proposed by researchers are PMTD [33], etc. The accuracy of detection is better than that of the segmentation detection method, but the performance of text detection in sequences with arbitrary rotation angles needs improvement. In the text detection module, we apply this to solve the problem of a lack of detection models for big font text or text with an extreme aspect ratio in existing multimodal scenes.
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
## 4 Simulations and Results
### The 3D Evaluation
Results in Figure 11 show that the estimation error does not grow in the same proportion as the size of the network \(d\), as maximal estimation errors for \(d\) = 20 m and \(d\) = 30 m are in the order of 4.1 m and 4.9 m, respectively, for \(r_{0}\) = 12 m. Note that as stated before, finer discretization, as well as additional ANs, can help to improve the location estimation. It is important to observe that as the method relies on the routes connecting ANs to \(\mathbf{n}_{0}\), better route algorithms might help to obtain better positioning estimations. Note that proactive routing protocols might render better positioning estimations, as routes are updated periodically. However, reactive (on-demand) routing algorithms might lead to large positioning errors if the location estimation is calculated based on old routing data.
This research presents a novel and low-cost technique for the tool health monitoring with an efficient data acquisition process. The proposed method uses a typical microphone as an airborne acoustic emission sensor to acquire the CNC turning operation signals and employs deep learning techniques to predict tool condition. Multiple types of materials and tools are used for machining purposes in order to predict the tool health and validate the deep learning algorithm. It is for the first time that convolutional neural networks (CNN) are being used along with the visual spectrum of an acoustic signal to categorize tool health. Different types of performance evaluations with accuracy comparisons on a fairly high variety of materials and tools are discussed. The methodology and proposed technique are being discussed in detail in the next section.
## 2 Results  
The MRSI data that were collected in 3 min and 16 s were used to calculate FF maps. Examples of these MRSI FF maps are shown in Figure 1, where they were overlaid over their corresponding structural MRI images.  
Figure 1: Representative data from: (**a**) the baseline; (**b**) repeated scans. The kidney hilum was used as an anatomical marker to acquire data from the same axial slice. The color-coded area is the coregistered MRSI fat-fraction map (masked about the left kidney) overlaid over its corresponding structural MRI image.  
MRSI renal fat quantification per subject and the repeatability results were summarized in Table 1. The calculated mean CV was 4.3 \(\pm\) 2.5%, representing excellent repeatability.  
As shown in Figure 2, the comparison between the spectral signature from the repeated scans within the same anatomical region (kidney-cortex) showed a high consistency between the scans with high intrasubject repeatability of spectral signature (CV = 8.1 \(\pm\) 6.4%).  
The structural images produced by MRSI also provided general anatomical features comparable to MRI structural images, but with fewer details (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Mean FF (\%)** & **CV (\%)** \\ \hline
1 & 1.01 \(\pm\) 0.05 & 4.90 \\
2 & 1.60 \(\pm\) 0.02 & 1.30 \\
3 & 1.11 \(\pm\) 0.06 & 5.80 \\
4 & 1.69 \(\pm\) 0.03 & 2.00 \\
5 & 2.00 \(\pm\) 0.15 & 7.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fat quantification and its repeatability results.  
Figure 2: The spectral signature from the repeated scans within the kidney cortex. The image on the corner shows the region of interest (dark blue box) where the spectra were evaluated. The solid black line represents the mean signal, and the brown shade represents the standard deviation of the test–retest signal acquired from the same anatomical region. The blue shade highlights the peak’s bandwidth that has been covered to quantify the fat fraction. The labeled lipid peaks represent fatty acids of different saturation (CH\({}_{3}\) at 0.9 ppm, (CH\({}_{2}\))n at 1.3 ppm, and CH\({}_{2}\) around 1.6 ppm).
## 4 Efficient FFT Algorithms
### Partial FFT Pruning  
[PERSON] _et al._[67] proposed an energy-efficient partial FFT pruning algorithm that allows for pruning of FFT inputs for cognitive radio systems. The proposed algorithm is based on modified FFT pruning algorithm [65], with reduction in the size of configuration matrix \(M_{\text{index}}\) in order to lower the memory requirement for its storage. In the proposed algorithm, the size of the configuration matrix is reduced by a factor of 2 by storing only the identifier of the butterfly operations that must be performed, instead of storing the index of every non-zero input. Moreover, for a radix-2 \(N\)-point FFT computation, by limiting the pruning operation to the first \(\log_{2}(N)-3\) stages, the configuration matrix size can be further reduced. Thus, the proposed partial pruning FFT algorithm offers an optimal trade-off between performance in terms of computation time and memory requirement. In a dynamic spectrum access scenario, whenever there is a change in the indices of active subcarriers, the configuration matrix is re-evaluated and FFT computation is restarted. Figure 9 shows an example of 8-point decimation in frequency (DIF) FFT butterfly structure with complete and partial pruning. Considering a subset of \(L\) non-zero input values, it was shown in [67] that the number of total multiplications and additions for executing a pruned \(N\) point FFT can be reduced to  
\[MUL_{PR}=\ 2\ \times\ N\ \times\ log_{2}(L)+2\ \times\ N\ -4\ \times\ L+\ \frac{(2\ \times\ N\ \times\ L)}{2^{(log_{2}(L))}} \tag{1}\]  
\[ADD_{PR}=\ 3\ \times\ N\ \times\ log_{2}(L)+3\ \times\ N\ -6\ \times\ L+\ \frac{(3\ \times\ N\ \times\ L)}{2^{(log_{2}(L))}} \tag{2}\]  
If \(L\) is contiguous and is power of two, then the number of multiplications and additions can be further reduced to  
\[MUL_{PR}=2\ \times\ N\ \times\ log_{2}(L) \tag{3}\]  
\[ADD_{PR}=3\ \times\ N\ \times\ log_{2}(L) \tag{4}\]
## 3 Results and Discussion
### Influence of Numerical Aperture on Movement of Absorption Point
However, longer movement of absorption point in case of lower N.A. caused longer cycle of heating and cooling in the upper and lower parts of molten area,  
Figure 10: Observation of absorption point movement. and thus the imbalance of temperature distribution would result in a large thermal stress and unstable phenomena. In contrast, higher N.A., which showed increasing location of absorption point at the start of irradiation, obtained smaller location of absorption point and shorter cycle of absorption point movement in the steady region. Thus, accurate control of absorption point could be expected by using the objective lens of higher N.A. On the other hand, in the case of N.A. of 0.65 and 0.85, it is considered that micro-welding without cracks can be achieved in a wide range of irradiation conditions because of smaller location of absorption point, shorter cycle of absorption point movement, and shorter cycle of heating and cooling. In order to investigate the absorption rate \(A_{\mathrm{ab}}\) for various numerical apertures, the transmitting laser power \(E_{\mathrm{tr}}\) was measured by using a power meter. Then absorption rate \(A_{\mathrm{ab}}\) was calculated based on the measurement results of transmitting laser power \(E_{\mathrm{tr}}\). Figure 12 shows the relationship between pulse energy and absorption rate of laser energy for various numerical apertures. As can be seen from the figure, absorption rate \(A_{\mathrm{ab}}\) increased with increasing pulse energy, and drastic change of absorption rate \(A_{\mathrm{ab}}\) occurred for all numerical apertures of objective lenses, which indicated that the absorption of laser energy in glass was a nonlinear phenomenon. N.A. of 0.65 showed the highest absorption rate compared to N.A. of 0.45 and 0.85. It is considered that longer cycle of absorption point movement and no absorption of laser energy during the interval time of teardrop-shaped molten area formation resulted in lower absorption rate in the case of N.A. of 0.45. However, N.A. of 0.85 showed a lower absorption rate, although the cycle of absorption point movement was short, which would be caused by the increased dispersion of laser beam at the focal point inside the glass. Figure 11: Influence of numerical aperture on absorption point movement. Figure 12: Influence of numerical aperture on absorption rate.