esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
[45] proposed a path-planning method based on prior knowledge and a Q-learning algorithm to plan the static collision-free path of a single robot in the constructed model by using the improved Q-learning algorithm, initializing the Q table with the prior knowledge obtained in the previous step, and using the Q-learning algorithm to achieve conflict-free motion between multiple robots. [PERSON] et al. [46] used the DQN algorithm for multi-robot path planning problems, which combines the Q-learning algorithm, experience playback mechanism, and volume-based technology for producing neural networks to generate target Q values, and the improved DQN algorithm improves the efficiency of multi-robot path planning. [PERSON] et al. (2017) proposed a new collaborative approach to the problem of exploration and search and rescue of mobile robots in autonomous collaborative scenarios. Splitting the task of collaborative exploration into two core parts, the first part is sensor-based, and the second part is the path planner assigns actionable tasks to each agent, including the ability to provide reachable collision-free paths and multiple simulations in complex location environments. [PERSON] et al. (2018) proposed a multi-UAV collaborative path planning method based on reinforcement learning to solve the problem that traditional heuristic algorithms are difficult to extract empirical models from large sample terrain data in time, which comprehensively considers the influencing factors such as survival probability, path length, load balancing and durability constraints. [PERSON] et al. (2019) proposed a dynamic fast Q-learning (DFQL) algorithm for the path planning problem of USV in some known marine environments, which combines Q-learning with artificial potential field (APF) to initialize the Q table and provides USV with prior knowledge from the environment. [PERSON] et al. (2019) in order to overcome the problem that traditional Q-learning is prone to local optimization in coverage path planning, a new reward function derived from the predator-prey model is introduced into the traditional Q-learning-based CPP solution. For the multi-agent path planning problem, the above scholars have made effective progress in improving the efficiency of classical algorithms, heuristic algorithms, and artificial intelligence algorithms. There are many application scenarios of multi-agent path planning, and the application in different scenarios has different degrees of complexity. However, in the face of such a large and complex environment to the industrial park, the existing artificial intelligence algorithms have problems such as low learning efficiency, slow convergence speed, and incomplete path planning of agents, and it is difficult for multiple agents to achieve convergence at the same time in the same round of training and learning.
## 5 Results and Discussion  
### Hardware Requirements  
To process and train such a deep model with such a large dataset requires a particular computational effort. This experimental research is being performed on an Intel(R) \(\textit{Core}^{TM}\) desktop workstation with a Core i7-9700 CPU @ 3.40 GHz, a RAM of 32 GB, and the hard drive includes 512 GB SSD. Nvidia GeForce with a GTX 960M model and 8 GB VRAM specifications are the graphics card. The use of the GPU was not needed because of the low computational complexity of the 1D CNN architecture.
## 1 Introduction  
The replacement of an unrestorable tooth with a single implant-supported prosthesis is a treatment with predictable and successful outcomes. The esthetic outcomes, as well as the functional and physiologic outcomes, are quintessential in the treatment of the anterior maxilla area [1; 2; 3; 4]. After a tooth is extracted, the loss of the periodontal ligament lowers blood supply to the alveolar bone. The buccal plate of the alveolar bone is particularly affected, and this ultimately yields poor esthetic outcomes [5]. It is important to maintain the architecture of the fresh extraction socket for the immediate post-extraction implant placement in the esthetic zone [6; 7]. Many studies have reported that an immediate implant placement and provisional restoration provide acceptable changes in the soft tissue and esthetic outcomes [8; 9; 10; 11; 12; 13]. A correct fixture positioning, grafting technique, and immediate provisional restoration help achieve successful results in the esthetic zone [7; 14; 15; 16; 17; 18].  
With the advancement of computer-aided design and computer-aided manufacturing (CAD/CAM) technology in implant dentistry, various implant techniques developed. With three-dimensional (3D) scanning and computed tomography (CT) imaging data, a CAD program can be used to diagnose and virtually plan an implant placement. First, the implant crown can be designed and waxed up into the desired shape on the intraoral scanning or impression data. The virtual prosthesis is integrated with the cone beam computed tomography (CBCT) data, allowing the clinician to virtually plan the placement of the three-dimensional implant. Once the integrated plan is established, a stereolithographic surgical guide and a customized prosthesis can be pre-fabricated by using 3D printing technology [19].  
Stereolithographic guides made by using a CAD/CAM scanning program allow the computer to transfer planned implants onto the surgical field. This can improve the precision of the implant placement, enables a non-invasive surgical procedure, reduces the time, and allows the pre-fabricated prosthesis to connect to the planned position immediately after a surgery [20; 21; 22].  
Customized abutments can be designed with an anatomically ideal emergency profile. Recently, various methods for improving the stability of soft tissue around implants have been developed. For example, restoration with pre-fabricated provisionalization after immediate implant placement is known to help maintain the anatomical structure of the extraction socket [23; 24; 25].  
The purpose of this study was to evaluate the changes in the soft tissue in patients who were treated with an immediate post-extraction implant placement using a stereolithographic guide and an immediate restoration with a CAD/CAM customized titanium anatomic abutment in the esthetic zone.
## 2 Maximum Likelihood Estimation
The vector \(\hat{x}\) corresponds to the values \((x_{1},\ldots,x_{r},x_{r+1},\ldots,x_{m})\) associated with \(X\). The log-likelihood function \(L_{c}=(Q,\alpha,\lambda)\) for the complete sample is given as follows:  
\[L_{c}(\alpha,\lambda,Q)=n\ln(\alpha\lambda)-(1+\alpha)\sum_{i=1}^{m}\ln( \lambda x_{i}+1)-(1+\alpha)\sum_{i=1}^{m}\sum_{k=1}^{R_{i}}\ln(1+\lambda z_{ ik}). \tag{7}\]  
* E-step The pseudo log-likelihood function is expressed as follows:  
\[L_{s}(\alpha,\lambda)=n\ln(\alpha\lambda)-(1+\alpha)\sum_{i=1}^{m}\ln( \lambda x_{i}+1)-(1+\alpha)\sum_{i=1}^{m}\sum_{k=1}^{R_{i}}E(\ln(1+\lambda z_ {ik})|z_{ik}>x_{i}), \tag{8}\]where  
\[E(\ln(1+\lambda z_{ik})|z_{ik}>x_{i}) = \frac{\alpha\lambda}{1-F(x_{i};\alpha,\lambda)}\int_{x_{i}}^{\infty }(\lambda t+1)^{-(\alpha+1)}\ln(\lambda t+1)\mathrm{d}t \tag{9}\] \[= \frac{\alpha}{1-F(x_{i};\alpha,\lambda)}\int_{0}^{1/(\lambda x_{i }+1)}y^{\alpha-1}(-\ln y)\mathrm{d}y\] \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]0
* M-step In the M-step, we focus on maximizing the likelihood function \(L_{s}\) with respect to \(\alpha\) and \(\lambda\). Let us denote the \(s\)-th estimates of \(\alpha\) and \(\lambda\) as \((\alpha_{(s)},\lambda_{(s)})\). These estimates will be utilized to optimize the expression in function (8). Consequently, the updated estimate \((\alpha_{(s+1)},\lambda_{(s+1)})\) is then obtained by maximizing the following: \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]1 (10) As a result, the associated likelihood equations are \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]2 (11) and \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]3 (12) The estimate of \(\alpha\) at \((s+1)\)-th stage can be characterized as follows: \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]4 (13) Therefore, the maximization of (10) can be obtained by solving the following fixed-point equation: \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]5 (14) where \[F(x;\alpha,\lambda)=1-(1+\lambda x)^{-\alpha},x>0, \tag{2}\]6 (15) Once the difference \(|\lambda_{(s+1)}-\lambda_{(s)}|\) falls below the specified tolerance limit, the iteration process concludes. After \(\lambda_{(s+1)}\) is obtained, \(\alpha_{(s+1)}\) is computed as \(\alpha_{(s+1)}=\hat{\alpha}(\lambda_{(s+1)})\). To achieve the maximum likelihood estimators for both \(\alpha\) and \(\lambda\), it is essential to iteratively perform the E-step and M-step until the program converges. The steps required for the implementation of the EM algorithm are as follows: (1) Choose initial values \((\alpha_{(0)},\lambda_{(0)})\). (2) Calculate the expectation \(A(x_{i};\alpha_{(s)},\lambda_{(s)})\). (3) Solve the fixed-point equation in (14). (4) Set \((\alpha_{(s+1)},\lambda_{(s+1)})\) to the solutions of step (3). (5) Repeat until convergence.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)
### Bridging
#### 4.3.1 Centralized Bridging
The central station (CS) calculates the required number of drones as well as the position of  
Figure 4: Example of the boundary obtained by alpha shape. each drone on the shortest path between \(\mathcal{N}_{i}\) and its adjacent island \(\mathcal{N}_{j}\). The number of drones is equal to \(d(G_{i},G_{j})/r_{\Gamma}\).
###### Abstract
How to recover the time slot synchronous clock from the discrete random single photon pulse is the key to the demodulation of the photon-counting communication signal. In 2019, Dr. [PERSON] et al. proposed a time slot synchronous clock recovery method based on pulse counting [30]. The time slot synchronous clock extracted by this method has low precision, especially in long-distance UOWC. When the average number of photons per bit is very small, the phase error of the extracted time slot synchronous clock is very large, resulting in a lot of errors in the recovered baseband signal. Deep neural networks have recently achieved exciting successes in computer vision, speech recognition and natural language processing because of their powerful data learning capabilities. Deep learning models can be used not only for classification and regression, but also for feature extraction. Fast online recovery and powerful nonlinear mapping capabilities are the main advantages of deep learning methods. In photon-counting UOWC, the synchronous clock information is implied in the discrete single photon pulse sequence output by the SPAD receiver. In view of this characteristic, we proposed a novel time slot synchronous clock recovery method based on deep learning for photon-counting UOWC. The time slot synchronous clock can be directly extracted from the received discrete single photon pulse by a trained deep neural network, so the baseband communication data can be quickly recovered. In order to verify our proposed method, a photon-counting UOWC experimental system was built.
## 4 Results and Discussion
### Effect of the w/c Ratio  
As is shown in Figure 7, the change of the w/c ratio of the mortar block had a great influence on the chloride penetration depth under hydrostatic pressure. The internal reason can be explained by the variation diagram of the porosity and diffusion coefficient of the mortar in Figure 8. With the increase of the w/c ratio, the porosity of the mortar increased, and the chloride diffusion coefficient of the mortar increased, indicating a high correlation. When the w/c ratio was large, the mortar sample was not dense enough, and there were many pores with large pore diameters. When the w/c ratio was large, the amount of cement was lower, and cement hydration formed. The hydration products of the chloride ions were relatively lower, and the chloride ions could be adsorbed and combined with fewer attachments.  
Figure 8: Variation diagram of the porosity and diffusion coefficient of the mortar.  
Figure 7: Chloride ion transport depth at 1.0 MPa.
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 3 System Model
### Multi-Criteria Decision-Making Technique  
A multi-criteria decision-making (MCDM) technique is employed to select the optimal route among the multiple routes according to the evaluated parameter values (mobility, battery, link quality status, and contention window size) of the relay devices. It aggregates all the relay device parameter values into an individual metric and weights the relay devices according to the evaluated parameter values. The source device periodically monitors and estimates the relay devices' parameter values by broadcasting topological control messages at the time of path discovery and topology sensing phase. The MCDM value of relay device \(c\) for the route selection process is computed as follows:  
\[MCDM=\left\{W_{EC}\times EC_{(a,b)}^{c}(t)+W_{mob}\times Mob_{(a,b)}^{c}(t)+W_{ LQ}\times LQ_{(a,b)}^{c}(t)+W_{CW}\times CW_{(a,b)}^{c}(t)\right\} \tag{11}\]  
where \(W_{EC}\), \(W_{mob}\), \(W_{LQ}\), and \(W_{CW}\) are the weights provided based on the evaluated parameter value which ranges between \"0\" and \"1\". The relay devices which possess a higher MCDM value have a higher probability in the route computational process. In contrast, the relay device that is not selected in the route computational mechanism has a lower MCDM value. Thus, the MCDM technique exploits these parameters' metric values in a reliable and optimal path selection among the multiple paths from the source to the destination devices. A flow chart of optimal path selection mechanism of the MBLCR approach is depicted in Figure 4.  
Figure 4: Flow chart of optimal path selection procedure of the MBLCR approach.
# Research on the Influence of Rectifying Orifice Plate on the Airflow Uniformity of Exhaust Hood
## 3 Results
### Influence of Double-Layer Rectifying Orifice Plate on the Airflow Uniformity of Exhaust Hood
When the porosity reaches 62.98%, the wind velocity at the center is significantly higher than at the edges of the exhaust hood. Figure 3e shows the center axis wind velocity distribution along the monitoring line under different porosities. It is observed that as the porosity increases, the center axis wind velocity at 400 mm of the exhaust hood continually increases. The data from Figure 3f indicate that as the porosity increases, the average wind velocity also increases, from 0.5719 m/s at \(\alpha\) = 4.91% to 0.6183 m/s at \(\alpha\) = 62.98%. The wind velocity non-uniformity initially decreases and then increases as the porosity increases, reaching its lowest at 17.92% when \(\alpha\) = 35.43%. Integrating these findings, using an upper rectifying orifice plate with \(d\) = 6 mm and \(\alpha\) = 35.43% provides optimal airflow uniformity for the exhaust hood, with an average wind velocity of 0.5863 m/s and a wind velocity non-uniformity of 17.92%.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism
### System Design
#### 2.1.1 Passive Alignment Subassembly  
Defining the inertial frame will help describe the adjustment and docking of the mechanism. As shown in Figure 5, the docking direction and roll axis are on the \(y\)-axis, the horizontal sliding direction and pitch axis are on the \(x\)-axis, and the vertical sliding direction and yaw axis are along the \(z\)-axis.  
The passive alignment subassembly has the functions of passive alignment and self-resetting in five DOFs, except in the docking direction. The drogue is connected to the passive alignment subassembly to be floating, as shown in Figure 4a. In this way, the synchronous adjustment and docking of the fluidic and electric connectors can be realized.  
A rolling frame, yawing frame, pitching frame, and several spring pins are the main structures of the passive alignment subassembly. The drogue and the pitching frame are connected by two preloading spring pins, which become cylindrical pairs so that the drogue can slide and rotate along the \(x\)-axis to adapt to deviations. The pitching frame and the yawing frame are also connected by two preloading spring pins, such that the pitching frame can slide and rotate along the \(z\)-axis to adapt to deviations. The yawing frame and the rolling frame are connected by four rollers, which can roll on the rolling guide so that the yawing frame can rotate along the \(y\)-axis to adapt to deviations.  
Figure 4: Components of the passive alignment subassembly: (**a**) forward isometric drawing, (**b**) reversed isometric drawing.  
Figure 3: Components of (**a**) the active part and (**b**) the passive part.  
A pair of the same preloading spring pins is needed for each DOF to maintain the drogue at an initial state of position zero. During the docking process, the drogue will ad-just passively to adapt to deviations so that the spring force of the spring pin pairs will change (the spring pin along the adjustment direction will be compressed, and the spring force of the pin increases continuously, and another pin is out of contact with the corresponding connecting frame to be inactive), thereby generating a reset force or torque, which will reset automatically after refueling and separating from the docking mechanism. The principle of the passive alignment subassembly is shown in Figure 5. The docking mechanism is floating and reusable because of passive adjustment and self-resetting characteristics, which meet the requirement R1 and R2. The tolerance range of the passive alignment subassembly are shown in Table 2.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 4 STDM Watermarking Using Visual Saliency-Based JND Model  
The motivation of using a perceptual model to perform the watermarking scheme is to embed the message bit \(m\) into the host signal \(x\) and the error introduced by the quantization should not exceed the distortion visibility thresholds (called slack) \(s\), otherwise the watermark will become perceptible. In practice, for image signals, our proposed perceptual model can be served as the foundation to calculate \(s\) as follows:  
\[s(n,i,j)=JND(n,i,j) \tag{18}\]  
where \(JND(n,i,j)\) is defined as Equation (4). The maximum imperceptible changes \(s\) in the direction of \(u\) can be given as \(s_{u}\). Since the maximum quantization error of the quantizer is \(\Delta/2\), the quantization step \(\Delta\) in the STDM quantizer is given by \(\Delta=2s_{u}\)[13].
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 5 Validation of the New Objective Function  
### Deburring Test Setup  
The application of the new objective function for predicting deburring was attempted for two drilled holes of 12 mm diameter, intersecting each other at 30\({}^{\circ}\) with 10 mm offset. Figure 18 shows the specimen geometry. The three-dimensional intersection with the varying cross-section along its length posed a challenge for deburring mainly due to poor accessibility. Thinner sections with longer burrs are more difficult to deburr.  
Figure 16: Deburred area normalized by [PERSON]’s objective function [18] vs. flow length for various flow speeds.  
Figure 17: Deburred area normalized by the new objective function vs. flow length for various flow speeds.  
Figure 19 shows the test setup. The specimen body included two intersecting holes. The primary hole was partially sectioned along its length to reveal the intersection. The secondary hole underneath intersected the primary hole. Each of the holes was connected to the pipes and hoses. The upper pipe for the primary hole accommodated an insert to restrict and intensify flow to the narrow annulus along the burr edge. The lower pipe had a slotted opening to further restrict the flow locally. The lower pipe was allowed to reciprocate along its axis for uniform deburring along the length of the intersection.  
The upper pipe connected to a silicone hose was fixed with respect to the specimen. The lower pipe, also connected to a silicone hose, was driven by a linear actuator system, including a ball screw, linear motion guide, drive motor, and control circuitry.  
Figure 20 indicates the details of the specimen with the upper and lower pipes, cover, and O-ring for sealing. The lower pipe was continuously reciprocated by a NEMA 17 stepping motor. Deburring progress could be observed by opening the cover.  
Figure 19: Test setup for deburring.  
Figure 18: Specim with a hole intersection (in mm).  
As shown in Figure 21a, the flow guide was designed to confine the flow within an annulus zone of 1 mm width between its lower end and the intersection. The lower pipe had a slot of 5 mm width. By combining the flow guide and the slot, the abrasive flow began from the upper pipe, proceeded to the annulus, then the slot, and finally the lower pipe when the flow began from the upper pipe. The flow conditions were reversed when the abrasive flow began from the lower pipe.  
Figure 22 shows the photographs of the test setup and specimen for the validation test.
## 4 Ground Motions  
In this study, 10 earthquake accelerations were tested on the Iscehisar Bridge. The earthquake acceleration records used in the study were taken from the Pacific Earthquake Engineering Research Center (PEER) database [33; 34]. The Rayleigh damping ratio was accepted as 5% in the analysis. It aimed to compare the effect of arch elements of historical stone bridges on dynamic response and earthquake damage. The earthquake acceleration was applied in the direction perpendicular to the tempan wall, where the geometrically weakest strength of the bridge model would be, as used in similar academic studies [24; 25; 35; 36]. The earthquake records applied to the bridge are offered in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Earthquake** & **Station** & **Magnitude** & **Depth** & **PGA** & **PGV** & **PGD** & **PGV/** \\  & **(Mw)** & **(km)** & **(g)** & **(cm/s)** & **(cm)** & **PGA (cm)** \\ \hline Chi-Chi, 1999 & TCU045 & 7.62 & 8 & 0.361 & 21.548 & 21.883 & 0.061 \\ \hline Friuli, 1976 & \begin{tabular}{c} Tolmezzo \\ (000) \\ \end{tabular} & 6.50 & 5.1 & 0.351 & 22.020 & 4.067 & 0.064 \\ \hline Hollister, 1961 & \begin{tabular}{c} Hollister City \\ Hall \\ \end{tabular} & 5.60 & 7.4 & 0.195 & 12.355 & 4.300 & 0.065 \\ \hline Imperial Valley, 1979 & \begin{tabular}{c} El Centro \\ Array@2 \\ \end{tabular} & 6.53 & 9.96 & 0.315 & 31.496 & 14.126 & 0.102 \\ \hline Kobe, 1995 & Kakogawa & 6.90 & 17.9 & 0.345 & 27.678 & 9.694 & 0.082 \\ \hline Kocaeli 1999 & Yarimca & 7.51 & 16 & 0.349 & 62.182 & 51.302 & 0.182 \\ \hline Landers, 1992 & Coolwater & 7.28 & 7 & 0.780 & 31.598 & 16.501 & 0.041 \\ \hline Loma Prieta, 1989 & \begin{tabular}{c} Gilroy Array \\ \#3 \\ \end{tabular} & 6.93 & 17.48 & 0.367 & 44.695 & 19.615 & 0.124 \\ \hline Northridge, 1994 & \
\begin{tabular}{c} Castaic-Old \\ Ridge Route \\ \end{tabular} & 6.69 & 17.5 & 0.568 & 51.827 & 9.035 & 0.093 \\ \hline Trinidad, 1983 &
\begin{tabular}{c} Rio Dell \\ Overpass \\ \end{tabular} & 7.20 & 15.1 & 0.194 & 8.464 & 0.888 & 0.045 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Earthquake records (PEER) used in the analysis.