esa-sceva/satcom-chunk-collection huggingface dataset:
# Static Bacterial Leakage in Different Conometric Connections: An In Vitro Study
## 1 Introduction
Morse-taper connections were introduced by [PERSON] in 1864 for industrial machines and in the 1990s in implant dentistry, with an original angle inclination of 2\({}^{\circ}\)50\({}^{\prime}\). However, today on the market, Morse-taper connections can reach until 18\({}^{\circ}\), although an acceptable interference happens for taper angles smaller than 5.8\({}^{\circ}\)[17; 19]. So, to simplify the text, we will refer in the \"manuscript\" to the terms \"Cone-Morse\", \"taper Morse\", and \"conical connection\" without distinction, but in each case, we will specify the degree of the connection. Conical connections are the most often used connectors for dental implants and abutments nowadays, as they provide increased mechanical stability under loading, minimizing abutment loss [14; 20; 21]. This hypothesis--that the Morse taper is a fitting design that employs friction to embed one cone into another cone with an angulation between two and four degrees without the use of screws, creating a cold weld between the components is supported by [PERSON] et al. [22]. In recent times, the concentric system has been proposed for use in fixed prosthesis connections, performing satisfactory results in rehabilitations [10; 23]. Such a system consists of a conical coping attached to the prosthesis and inserted into a conical abutment, and when an adequate insertion force is applied, this system can provide an effective fixed connection [10]. According to [PERSON] et al. [13], the concentric system uses a wedge effect to achieve its high sealing and retention efficiency. Nonetheless, despite demonstrating hermetic contact, absolute congruence between the implant and the abutment, and high-leakage resistance, evidence of bacterial infiltration, was observed within the implant cavity, even on Cone-Morse connections [3; 21; 24; 25]. Cone-Morse implants are used in single, partial, or total oral rehabilitation due to their undeniable benefits and clinical longevity. There are several brands and models available to the clinician. The aim of this in vitro study was to evaluate the bacterial microleakage of three different types of conical connections: an abutment with a passing screw (Cone-Morse connection), a solid abutment (Cone-Morse connection), and a concentric connection with an esthetic abutment. In particular, bacterial microleakage of two bacterial species into the three implant connections was evaluated over a period of 14 and 90 days.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 3 Vehicle Motion Estimation Using an Upward-Facing Camera
### Camera Calibration and Image Rectification  
Similar to most computer vision applications, camera calibration and image rectification are two important steps in the development of the proposed method. The calibration method described in [44] is used in this paper as implemented in the OpenCV library [45]. It consists of determining the camera matrix, as well as the distortion coefficient matrix. In fact, there are two main lens distortions: the radial and the tangential distortions. The former are caused by the shape of the lens, while the later arise from manufacturing defects resulting from the lens not being exactly parallel to the imaging plane. Concerning the radial distortion rectification, given a current distorted pixel \(\tilde{p}\) with coordinates \((\tilde{\tau},\tilde{y})\), the radial location of \(\tilde{p}\) will be rescaled [46] on the undistorted output image as:  
\[x =\tilde{\tau}(1+r_{1}d^{2}+r_{2}d^{4}+r_{3}d^{6}) \tag{1}\] \[y =\tilde{y}(1+r_{1}d^{2}+r_{2}d^{4}+r_{3}d^{6}) \tag{2}\]  
where:\(r_{i}\) (\(i=1,2,3\)) denotes the radial lens distortion parameters; \(x\) and \(y\) are the new coordinates of the pixel as a result of the correction; \(d=\sqrt{(\tilde{x}-x_{c})^{2}+(\tilde{y}-y_{c})^{2}}\) is the distance of the distorted coordinates to/from the principal point. \(x_{c}\) and \(y_{c}\) are the coordinates of the principal point. Regarding the tangential distortion, it is corrected by using: \[x =\tilde{x}+2t_{1}\tilde{x}\tilde{y}+t_{2}(d^{2}+2\tilde{x}^{2})\] (3) \[y =\tilde{y}+t_{1}(r^{2}+2\tilde{y}^{2})+2t_{2}\tilde{x}\tilde{y}\] (4)  
where \(t_{j~{}(j=1,2)}\) are the tangential distortion parameters.  
Besides the lens distortion correction, the camera focal length f and the principal point coordinates \((x_{c},y_{c})\) are determined to build the camera matrix. More details can be found in [46]. The algorithm successively estimates the parameters using the closed-form solution [44] and maximum-likelihood estimation using all detected corners on a chessboard in multiple views. It minimizes the error between the projected object space coordinates on the image with the estimated parameters and the measurements of the feature points [29], using the Levenberg-Marquardt algorithm [47]. Once the image is rectified and the intrinsic parameters of the camera obtained, further processing can be carried out.
## 3 Co-Channel Coexistence Mitigation Methods
### Methods with Channel Reservation  
The last two methods, which we call M\({}_{\text{D}}\)-reservation and M\({}_{\text{E}}\)-reservation-&-preamble, are here briefly described for completeness but not considered in the following. As already anticipated, in fact, these two methods require that the V\({}_{\text{NR}}\) use CSMA/CA following the IEEE 802.11p standard to gain the access to the channel.  
In particular, the M\({}_{\text{D}}\)-reservation and M\({}_{\text{E}}\)-reservation-&-preamble rely on a new message (see Table 2), which carries resources reservation information and can be used by both V\({}_{\text{NR}}\) (equipped with IEEE 802.11p transceiver) and V\({}_{\text{11p}}\) to reserve the channel for a certain time interval. V\({}_{\text{NR}}\) cannot access the channel before they reserve resources by accessing the channel through CSMA/CA and send the new reservation message. This implies also a modification of the legacy IEEE 802.11p to be able to read the same message.  
In M\({}_{\text{D}}\)-reservation, only V\({}_{\text{NR}}\) knows the superframe structure. As shown in Figure 7, in the slot S\({}_{\text{NR}}\), before transmitting an NR-V2X packet, [PERSON]\({}_{\text{NR}}\) broadcast an IEEE 802.11p type reservation message to let [PERSON]\({}_{\text{11p}}\) and other V\({}_{\text{NR}}\) know that during the following time interval the channel is reserved.  
As shown in Figure 8, M\({}_{\text{E}}\)-reservation-&-preamble combines the use of the reservation messages (as M\({}_{\text{D}}\)-reservation) and the IEEE 802.11p preamble insertion (as M\({}_{\text{C}}\)-preamble) to further improve the ability of V\({}_{\text{11p}}\) to avoid colliding with V\({}_{\text{NR}}\).  
Figure 7: Illustration of M\({}_{\text{D}}\)-reservation. Only V\({}_{\text{NR}}\) know the superframe structure. Before transmitting the NR-V2X packet, V\({}_{\text{NR}}\) would broadcast an IEEE 802.11p type reservation message to reserve resources. The subfigure (**a**) is from the V\({}_{\text{NR}}\) perspective, and the subfigure (**b**) is from the V\({}_{\text{11p}}\) perspective.
## 6 Numerical Simulation of Harvesters with ITDs
### Stress Analysis  
The last numerical analysis here presented deals with the stress analysis inside the piezoelectric material. Four configurations were considered: harvesters equipped with ITD1 and ITD2, PPA 1001 alone and with tip mass. The harvesters were excited by a base acceleration of 10 ms\({}^{-2}\) at the main resonance frequency, which coincides with the low frequency peak of the harvesters with ITDs and with the main peak of the others.  
Normal stress in the longitudinal direction \(\sigma_{x}\) was calculated, since it is the most important stress component caused by harvester bending. Stress \(\sigma_{x}\) was evaluated along the centerline of the upper surface of the PZT layer, which is at the largest distance from the neutral axis of the composite cross-section. The trace of this line is point P of Figure 9.  
Figure 17 shows that the introduction of the tip mass does not considerably modify the trend of stress distribution along the span of the harvester, which is characterized by the largest values near the clamp. But the tip mass leads to a large increase in the values, which almost double. Conversely, when ITD1 is introduced, the trend of stress distribution along the span of the harvester changes. In particular, the stress decreases less when moving from the clamp to the tip of the harvester and it increases sharply in the proximity of the connection with the ITD. The maximum value is reached at the clamp and it is smaller than the one of PPA 1001 alone. The insertion of ITD2 lowers the stress at the clamped-end as well, but with ITD2 the stress near the free-end of the PZT layer increases less than with ITD1, since ITD2 is connected to the harvester in correspondence of the side edges of the structural layer that surround the PZT layer, see Figure 12b.  
It should be noted that the bending breaking strength of PZT 5H is in the range 100-140 MPa [40,41] and it considerably reduces in the presence of cyclic loading (see Figure 3 of [41]). Hence, with high acceleration levels the harvester with tip mass may be closer to the failure condition than the harvester with ITDs.  
Figure 17: Numerical results, maximum stress in the piezoelectric layer of the harvesters.
## 3 Preliminaries
### A Fuzzy KNN Algorithm  
[PERSON] [48] made the first fuzzy KNN classifier proposal. [PERSON] et al. [49]'s proposal became the main reference in this field. Algorithm 1 describes the pseudocode of the basic steps denoting the membership function formula in line 10. The formula is used to determine the appropriate class based only on the distance from the prototype of the class.  
```
Input:\(x\) instance Output:\(l\) class Label
1 BEGIN
2 Input \(x\), vector to be classified.
3 Initialize \(i=1\).
4 DOUNTIL (distance from each prototype to \(x\) computed)
5 Compute distance from \(Z_{i}\) to \(x\).
6 Increment \(i\).
7 END DOUNTIL
8 Initialize \(i=1\).
9 DOUNTIL (\(x\) assigned membership in all classes)
10 Compute \(u_{i}(x)\ =\frac{1/\left\|x-Z_{i}\right\|^{2/(m-1)}}{\sum_{j=1}^{n}\left(1/\left\|x-Z _{i}\right\|^{2/(m-1)}\right)}\)
11 Increment \(i\)
12 END DOUNTIL
13 Classify \(y\): set \(l\ max_{i\leq j\leq n}\{u_{i}(x)\}\)
14 END
```  
**Algorithm 1** Fuzzy KNN based on distance from the prototype of the class.  
It is a more developed edition KNN algorithm. The majority class label is used to specify the class label of an unspecified instance in the conventional KNN algorithm. In the first stage of development, the membership of training instances for each class is determined by mathematical calculation using Equation (3):  
\[\vartheta_{c}(A)\ =\ \left\{\begin{array}{ll}0.51+\left(\frac{m_{c}}{k} \right)\ *\ 0.49&if\ c=i\\ \left(\frac{m_{c}}{k}\right)\ *\ 0.49&else\end{array}\right. \tag{3}\]  
where \(\vartheta_{c}(A)\) : denotes membership of \(A\) in class \(c\),the class of \(A\) is \(j\) and \(m_{c}\) is all neighbors in class \(C\).  
Regardless of the increasing improvement and proposals of KNN, which have created different methods of solving fuzzy NN classification algorithms, the use of fuzzy sets in conjunction with the NN classifier has enhanced work in dataset identification. Most of these works were already based on the application of fuzzy nearest neighbor rules to a range of domains or on alternative ways of combining FST and KNN. The fuzzy KNN algorithm computes the uncertainty membership function for each instance before classification relative to the complete number of instances, which yields a square computation relation. This relation is very important to determine the relative class of the current instance verse other algorithms that classify it without inspecting the uncertainty value of instance for each category. The Fuzzy KNN algorithm has the worst value(s) at the complexity levels O(kn\({}^{2}\)d) where \(k\) represents the number of available clusters, \(n\) represents the number of available instances, and \(d\) represents the dimensionality of the instance data.
## 5 Modeling of Existing Processes and Situations. Results and Discussion
### Analysis of Measurement and Conversion of Quantity at Different Stages of Purchase-Production-Sale
#### 5.2.3 Specific Mass Changes Due to Buoyancy Corrections  
The mass variations due to buoyancy corrections are as follows:  
* For light and heavy oils: Changes range from 110 to 150 kg per 100 tonnes (mass corresponds to one-day measurement with one type of measuring instrument), from 1.1 t to 1.5 t per 1000 tonnes, and 11 t to 15 t per 10,000 tonnes.
* For bitumen: Mass changes are about 100 kg per 100 tonnes and 10 t per 10,000 tonnes.  
These mass changes have direct implications for pricing and customer satisfaction. Variations in measured mass can lead to pricing inconsistencies, affecting both the profitability for suppliers and the perceived fairness for customers, particularly in high-volume transactions. Addressing these variations through standardized correction protocols could enhance customer confidence and satisfaction while promoting fair pricing practices across markets.  
Figure 7: Changes in mass resulting from air buoyancy correction (supplier loss) (**a**) and correction factor of 0.99985 for LPG (client loss) (**b**).
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 3 The Proposed Algorithm
### Sub-Problem 1: Optimizing Relay Selection and UAV Power Control of a Given UAV Trajectory
According to the analysis above, Equation (21) and Constraint (20e) can be transformed as:  
\[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]1 \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]2 \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]3After such approximation, the objective function is transformed into a concave function, so Problem (19) can be optimized by solving the following problem:  
\[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]4 \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]5 (25b) \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]6 (25c) \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]7 (25d) \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]8 (25e) \[s.t. \left(6\right)-\left(10\right),\left(18b\right)\]9 (25f) \[\max_{A,P,\left\{u_{k,m}\left[n\right]\right\}}\frac{1}{N}\sum_{n=2 }^{N}\sum_{k=1}^{K}a_{k,D}\left[n\right]u_{k,D}\left[n\right] \tag{20a}\]0 (25g) \[\max_{A,P,\left\{u_{k,m}\left[n\right]\right\}}\frac{1}{N}\sum_{n=2 }^{N}\sum_{k=1}^{K}a_{k,D}\left[n\right]u_{k,D}\left[n\right] \tag{20a}\]1 (25h) \[\max_{A,P,\left\{u_{k,m}\left[n\right]\right\}}\frac{1}{N}\sum_{n=2 }^{N}\sum_{k=1}^{K}a_{k,D}\left[n\right]u_{k,D}\left[n\right] \tag{20a}\]2 \[\max_{A,P,\left\{u_{k,m}\left[n\right]\right\}}\frac{1}{N}\sum_{n=2 }^{N}\sum_{k=1}^{K}a_{k,D}\left[n\right]u_{k,D}\left[n\right] \tag{20a}\]3  
Thus, when the constraints (25g) and (25h) take the equal sign, there is an optimal solution for Problem (25). Problem (25) is convex, which can be solved effectively by the interior-point method or standard convex tools such as CVX [24]. It can be found that the optimal solution \(\left\{A,P\right\}\) can be obtained with given Q, when the constraints (25g) and (25h) take the equal sign. In addition, both A and P will serve as the inputs for Sub-problem 2 in the next subsection.
## 3 System Requirements for BVLOS Flight Operations
### Flight Termination System  
A safety requirement for the RAVEN BVLOS flight operations is for all UAS to be equipped with a flight termination system (FTS) that prevents the aircraft from flying beyond the restricted airspace assigned by the SFOC [11]. To fulfill this requirement, an FTS (Figure 4) is developed that terminates the aircraft engine to prevent from flying away when both the R/C and autopilot C2 links are lost for more than two minutes. With a redundant design, the FTS can also be manually triggered by an R/C toggle switch on the R/C console over the 2.4 GHz C2 link.  
In the R/C receiver and the autopilot, the status of the communication links can be configured and shown as servo PWM output signals. For example, when the communication links are reliable and robust the PWM pulse width is 1 ms, and when communication is lost the pulse width increases to 2 ms. In order to detect these signals, a hardware PWM signal decoder (as shown in Figure 4) is used to convert the PWM pulse width into an analog voltage which can be measured by an analog to digital converter (ADC) in a microcontroller. The microcontroller then compares all the inputs, starts a timer if both links are lost, and sends out the engine termination command when the timer is timed out.  
As depicted in Figure 5, this FTS has four PWM inputs (i.e., R/C C2 link loss, autopilot C2 link loss, R/C termination switch, and ADS-B termination switch) and one PWM output (engine termination command). When PWM input values are low (i.e., less than 1200 or 1.2 ms), they are not engaged; when they are high (i.e., greater than 1800 or 1.8 ms), they are engaged. On the other hand, when PWM output values are low, the engine keeps running; when they are high, the engine is to be terminated.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 2 Related Research  
### Low-Light Image Enhancement Based on Deep Learning Technology  
Recently, research utilizing deep learning has been taking place actively across various fields, with methods employing CNN (convolutional neural network) training gaining attention for improving low-light images. MSR-net [11] is a CNN-based learning model proposed to enhance low-light images and Retinex theory. MSR-net performs well compared with other traditional methods, but, due to the limited acceptance area of the network architecture, it can produce visually unnatural parts in the resulting images.  
While these deep learning-based learning methods outperform classic manual low-light enhancement approaches, there are still some issues. Since most deep learning-based learning methods are based on supervised learning, they require large-scale training datasets consisting of low-light images and regular images captured under normal light conditions as input. The performance of a deep learning network is closely related to the dataset used to train it, but building large-scale training datasets that take into account real-world conditions is challenging. For example, while obtaining a pair of images with different exposures and generating an HDR (high dynamic range) image from them is relatively straightforward [12], this is feasible only when capturing still scenes under well-lit conditions during the daytime. Acquiring a large amount of clear normal light images in extremely low-light environments is difficult.
## 3 Results and Discussion  
### Chemical and Elemental Analysis of BSG and SCG  
The chemical and elemental analysis of the three sources of BSG and SCG are reported in Table 3. The chemical and elemental properties show relatively low variation between the three samples of BSG and SCG.  
BSG has a moisture content of 69 \(\pm\) 4%, somewhat lower than the range of 70.6% to 82.6% reported by [PERSON] et al. (2016) and the typical value of 80% ([PERSON] et al., 2016), while the TC of 48.3 \(\pm\) 6.3% is in the reported range of 43.6% to 51.6% ([PERSON] et al., 2016), and the TN of 3.0 \(\pm\) 0.7% (corresponding to 19 \(\pm\) 4% crude protein by TN \(\times\) 6.25) is below the reported range of 3.5% to 4.2% ([PERSON] et al., 2016) and typical crude protein content of 25% to 30% ([PERSON] et al., 2016). The measured pH of 4.8 \(\pm\) 1.1 is typical of BSG, reported as a range of 3.8 to 6.9 ([PERSON] et al., 2016).  
SCG has a somewhat variable moisture content of 56 \(\pm\) 8%, higher than the range of 38% to 48% reported by [PERSON] et al. (2018) but within the wider range from 42% to 65% and a typical value of 60% reported by [PERSON] et al. (2015), presumably due to differences in grinding, roasting, and brewing conditions. The TC of 49.6 \(\pm\) 6% is in the reported range of 45% to 69% ([PERSON] et al., 2015), and the TN of 2.1 \(\pm\) 0.3% is in the reported range of 1.2% to 2.7%, or even up to 4% ([PERSON] et al., 2015). In SCG, calculating crude protein by TN \(\times\) 6.25 will likely overestimate actual protein content due to the presence of nonprotein nitrogen compounds such as alkaloids, amines, amino acids, and their reaction products ([PERSON] et al., 2017). Indeed, the measured crude protein of 13 \(\pm\) 2% falls in the higher end of the reported range of 6.7% to 14% ([PERSON] et al., 2017); however, higher values of 13% to 17% have also been reported ([PERSON] et al., 2018). The measured pH of 5.2 \(\pm\) 0.5 falls within the reported range of 5.0 to 6.0 ([PERSON] et al., 2018).
# Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation
## 5 Discussion  
Multiple attenuation can provide a good foundation for subsequent data processing and data interpretation. The traditional parabolic Radon transform is unable to separate primaries and multiples without distortion. Sparse parabolic Radon transform based on \(L_{1}\) regularization also does not achieve the desired effect of suppressing multiples. We introduce the nonconvex \(L_{q_{1}}-L_{q_{2}}(0<q_{1},q_{2}<1)\) mixed regularization sparse inversion method. This method obviously improves the accuracy of inversion, suppresses multiples and obtains clean seismic data. Moreover, the reconstructed primary data have higher accuracy, which was verified by experimental data.  
In this paper, \(L_{1/2}\) regularization is used to constrain primary and multiple waves because it has many properties such as sparsity and unbiasedness and oracle properties [25]. Meanwhile, it is more stable and sparse than the \(L_{1}\) regularization, and easier to solve than \(L_{0}\) regularization. In other practical applications, the values of \(q_{1}\) and \(q_{2}\) can be selected according to the actual problem. For example, if the coefficients are not strictly sparse, a moderate to large value of \(q\) can yield better results [34].  
In complex geological conditions, the weak effective signals in the wavefield are easily covered by noise. Therefore, the parameter setting needs to be more careful, otherwise it may lose an effective signal because of noise suppression.  
Since the proposed method introduced the \(L_{1/2}\)-norm, the computation time is increased when the sparsity of seismic signals is improved. Therefore, the computational efficiency of this method has no advantage over the \(L_{1}\)-norm sparse inversion method.  
In future research, we propose three research directions that can be improved:  
1. To solve the problem of the destruction of amplitude versus offset (AVO) signature in seismic data, the proposed method is combined with the orthogonal polynomial transform.
2. The algorithm for solving nonconvex regularization is further improved to improve the computational efficiency.
3. The proposed method is combined with other multi-wave suppression methods to process seismic data with high efficiency and high quality under complex geological conditions.  
Figure 11: Multiple attenuation result: (**a**) stacked real data; (**b**) LSPRT method; (**c**) SPRTL\({}_{1}\) method; (**d**) SPRTL\({}_{q1}-L_{q2}\) method. The black arrows indicate the continuity of the seismic events are improved in the Figure 11d.  
In addition, with the rapid development of deep learning, fields such as mechanics, medicine and geophysics [43; 44; 45] have been actively combined with deep learning, and more possibilities have been developed. Therefore, in future studies, we will also combine multiple suppression with deep learning to solve problems such as computational efficiency.