esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory
### Common Approach of TDG Methods
#### 2.1.1 Differences Between the Conventional TDG and the Optimized TDG
Specifically, the first-order temporal derivative at each discrete point is estimated by the DG method from the weak formulation. Subsequently, higher-order temporal derivatives \(\partial^{2}\mathbf{u}/\partial t^{2}\), \(\partial^{3}\mathbf{u}/\partial t^{3}\)--commonly employed in the conventional TDG (Lax-Wendroff temporal discretization) framework--are determined by following the approaches outlined in Equations (11) and (12). In this step, the estimated first-order temporal derivative, along with the temporal derivative of matrix A and its higher-order temporal-derivative \(\partial\mathbf{A}/\partial t\), are leveraged to infer the \(\partial^{2}\mathbf{u}/\partial t^{2}\) and \(\partial^{3}\mathbf{u}/\partial t^{3}\). Thereafter, employing the Taylor series expansion in time allows one to estimate the solution at the next time. Notably, because the higher-order temporal derivatives of matrix \(\mathbf{A}\) like \(\partial^{2}\mathbf{A}/\partial t^{2}\) can be systematically obtained, while maintaining the matrix operators in the two-dimensional matrix form, the usage of the \(\partial\mathbf{A}/\partial U\) is rendered unnecessary. Therefore, this approach can accommodate higher-order Taylor expansions in time, such as fourth-order or fifth-order expansions, while keeping the temporal derivatives of matrix \(\mathbf{A}\) in a two-dimensional matrix form, thus avoiding the need for three-dimensional (tensor) or higher-dimensional matrices.
## 1 Introduction  
Soil pollution is a pressing environmental issue around the world. In this regard, heavy metals (HMs) present special concerns. Globally, more than five million soil sites are contaminated with HMs [1]. Soil serves as a major sink of HMs released into the environment. Unlike organic contaminants, HMs are biologically or chemically non-degradable. However, their chemical forms and bioavailability are changeable [2]. Therefore, they can accumulate in soil and remain persistent for long periods of time, posing serious environmental threats [2, 3]. Rapid industrialization and urbanization have caused the discharge of wastewater containing high concentrations of HMs, which has contributed to the elevated levels of HMs in urban environments, especially in developing countries [4]. Residents in industrial areas are most vulnerable to exposure to HMs, since many of these elements are closely related to industry [5]. Growing evidence demonstrates that HM pollution in mining areas has caused serious health issues in local residents, as well as damage to soil health [3]. Therefore, the prevention of toxic effects from these contaminants has become an existential challenge [6].  
Many studies have been conducted on the reduction in or removal of HMs from polluted soil, including in situ and ex situ remediation techniques [7]. Physical and chemical remediation techniques do not completely solve the environmental problems associated with HM contamination, whereas biological techniques (e.g., use of plants) have been proven to be the most environmentally and economically effective approaches [6].  
The mining industry makes significant contributions to the Armenian economy. It is highly concentrated in Lori Province, northern Armenia. Generally, improper management of industrial discharges is one of the most concerning environmental issues facing this area [8]. Earlier hydro-ecological investigations suggested that the rivers in the mining areas of Lori Province were negatively influenced by HM contamination, which was caused by discharges from the Alaverdi copper smelter and Akhtala mountain enrichment combine, as well as effluents from the non-operational tailing dump of the former Alaverdi mining and metallurgical combine and the landfills of the non-operational Alaverdi underground copper mine [8; 9; 10], while other studies have shown that the As and Pb contamination in soil in Alaverdi Town was significantly associated with the Alaverdi copper smelter [11].  
The present study aims to characterize HM contamination in metallurgically affected soil, its environmental effects, and its bio-mitigation potential for the soil. To achieve the goal of this study, the authors investigated HMs in the soil, plants, and animals near the copper smelting plant in Alaverdi Town, Lori Province.
## 3 Simulation Results and Comparison
### Simulation of PAUT Imaging and Experiment Validation  
In practical detection, the accuracy of phased array ultrasound imaging is also very important. Therefore, the type-B sleeve fillet weld specimen with defects (in Figure 3) was tested using 16-channel transverse wave phased array ultrasonic testing equipment to obtain the phased array fan scan image of the actual defects (as shown in Figure 9a). According to the fast finite element numerical simulation program established above, the echo signals of the focused sound field at different angles were simulated, and the  
Figure 5: PA longitudinal wave signal of the defect simulated using **(a)** the fast FEM code and **(b)** commercial finite element software.  
simulated phased array fan scan imaging was finally generated (as shown in Figure 9b). In the numerical calculation model, the position coordinates of the defect center relative to the center of the rightmost array element were 22 mm and 15 mm, and in the fan scan simulation imaging results, the position coordinates of the defect imaging center were 22.44 mm and 15.13 mm. The absolute error between the defect position of the fan scan imaging and the defect position set by the numerical calculation model was within 0.5 mm in the horizontal and vertical directions, and the relative error was within 2.0% in these two directions. In addition, the fan scan images obtained through the simulation and experiment in Figure 9 had a high consistency.  
Figure 6: Simulated PA transverse wave focusing fields with a focusing angle of 56°and focusing depth of 27 mm using (**a**) the fast FEM code and (**b**) commercial finite element software.  
Figure 7: PA transverse wave signal of the defect simulated using (**a**) the fast FEM code and (**b**) commercial finite element software.
## 5 Shear Lag Coefficient  
As an essential indicator of the shear lag effect, the shear lag coefficient \(\lambda\) represents the relative relationship between the stress considering the shear lag effect and the stress derived by the elementary beam theory. The shear lag coefficient can be defined as follows:  
\[\lambda=\sigma/\sigma_{0}=1+\sigma_{1}/\sigma_{0} \tag{41}\]  
where \(\sigma_{0}\) is the stress calculated by the primary beam theory and \(\sigma_{1}\) is the stress caused by the shear lag. In addition, the phenomenon that \(\lambda\) is has a value greater than one at the web and less than one at the flanges' interior is called the positive shear lag effect, the opposite of which is the negative shear lag effect [36].  
As shown in Figure 5, \(a_{11}\) and \(a_{22}\) represent the zero points of the shear flow of the top slab; \(a_{12}\), \(a_{21}\), and \(a_{23}\) stand for the junctions of the top slab and webs; \(a_{13}\) and \(a_{24}\) represent the zero points of the shear flow of the cantilever slab; \(a_{14}\) and \(a_{26}\) represent the zero points of the shear flow of the bottom slab; and \(a_{15}\), \(a_{25}\), and \(a_{27}\) stand the for the junctions of the bottom slab and webs. These points are the maximum or minimum stress points on each plate, so their shear lag coefficients are representative.  
For single-cell box girders, the shear lag coefficients of critical points can be expressed as follows:  
\[\left\{\begin{array}{ll}\lambda(a_{11})=1-\frac{(N_{1}d_{1}-N_{2})E}{M} \varphi^{\prime}&\lambda(a_{12})=1-\frac{(N_{1}\eta_{1}+N_{1}d_{1}-N_{2})E}{M }\varphi^{\prime}\\ \lambda(a_{13})=1-\frac{(N_{1}d_{2}-N_{2})E}{M}\varphi^{\prime}&\lambda(a_{1 4})=1-\frac{(N_{1}d_{3}-N_{2})E}{M}\varphi^{\prime}\\ \lambda(a_{15})=1-\frac{(N_{1}\eta_{3}+N_{1}d_{3}-N_{2})E}{M}\varphi^{\prime} \end{array}\right. \tag{42}\]  
Figure 5: Schematic of critical points on the box section: (**a**) single cell; (**b**) double cell.  
For double-cell box girders, the shear lag coefficients of critical points can be expressed as follows:  
\[\left\{\begin{array}{ll}\lambda(a_{21})=1-\dfrac{(N_{1}\eta_{1}+N_{1}d_{1}-N_{2 })E}{M}\varphi^{\prime}&\lambda(a_{22})=1-\dfrac{(N_{1}d_{1}-N_{2})E}{M} \varphi^{\prime}\\ \lambda(a_{23})=1-\dfrac{(N_{1}\eta_{2}+N_{1}d_{2}-N_{2})E}{M}\varphi^{\prime}& \lambda(a_{24})=1-\dfrac{(N_{1}d_{3}-N_{2})E}{M}\varphi^{\prime}\\ \lambda(a_{25})=1-\dfrac{(N_{1}\eta_{4}+N_{1}d_{4}-N_{2})E}{M}\varphi^{\prime}& \lambda(a_{26})=1-\dfrac{(N_{1}d_{4}-N_{2})E}{M}\varphi^{\prime}\\ \lambda(a_{27})=1-\dfrac{(N_{1}\eta_{5}+N_{1}d_{5}-N_{2})E}{M}\varphi^{\prime} \end{array}\right. \tag{43}\]
###### Abstract  
This retrospective study aimed to evaluate the prevalence, morphological types, and distribution patterns of palatoradicular grooves (PRGs) in maxillary anterior teeth using cone-beam computed tomography (CBCT) in a Turkish population. CBCT images of 1553 patients from the radiology archive of Ordu University Faculty of Dentistry (2021-2022) were reviewed. A total of 920 patients (4012 teeth) met the inclusion criteria. The presence, type, and localization of PRGs were assessed. Groove types were classified as Type 1, 2, 3A, or 3B; localization was recorded as mesial, distal, or midpalatal. Bilateral and unilateral occurrences were also analyzed. Statistical analysis involved chi-square tests, [PERSON]'s HSD, and Cohen's kappa for intra-observer reliability. PRGs were detected in 23.6% of patients and 10.42% of teeth. Lateral incisors were most affected (87.56%). Type 1 grooves were most common (71.53%), with midpalatal localization being most frequent (54.07%). Bilateral grooves were significantly more prevalent than unilateral ones (\(p<0.001\)). No significant association was found between groove type and tooth type or between gender and bilaterality. This study revealed a high prevalence of PRGs, especially in maxillary lateral incisors, with a significant tendency toward bilateral and midpalatal presentation. CBCT proved essential for detecting palatoradicular grooves, aiding diagnosis and treatment.  
palatoradicular groove; cone-beam computed tomography (CBCT); maxillary anterior teeth; tooth abnormalities
## 3 Results and Discussion
### Phase 4: Data Transformation Approaches  
Although the NLTs can be independently applied to each attribute, for consistency, during our EDA, all-numeric variables uniformly undergo the same column-wise transformation. Because three Oil-Slick  
\begin{table}
\begin{tabular}{l c c c} \hline
**Oil Category** & **I: Previous Work** & **II: Our Reseach** & **III: Difference** \\ \hline Oil Seeps & 2,021 (41\%) & **1,994 (44\%)** & -27 (-1\%) \\ Oil Spills & 2,895 (59\%) & **2,568 (56\%)** & -327 (-11\%) \\ \hline Oil Slicks & 4,916 (18\%)* & **4,562 (13\%)* & -354 (-7\%) \\ \hline RADARSAT-2 scenes & 277 & **244** & -33 (-12\%) \\ \hline \end{tabular}  
* Initially available oil slick data archive [19-20].
* Our workable dataset, i.e. large collection of oil slick polygons imaged with RADARSAT-2 scenes of the two ScanSAR Narrow beam modes: SCNA and SCNB.
* Removal of RADARSAT-2 beam mode (Wide: W1 and W2) during Phase 1 (Data Quality Control).
* Difference between oil spills and oil seeps.  
\end{table}
Table 2: Number (and percentage) of explored oil slicks (seeps and spills) and satellite images.  
Information Descriptors--i.e., Fractal, SKW, and KUR--have values that range from negative to positive, they are not used on half of the NLTs that require only positive values: NLT.1; NLT.2; NLT.3; and NLT.4.
## 3 Application of the Proposed Method
### Case Study Robotic Tasks
#### 3.1.1 Pick and Place Task  
The pick and place task is defined as one where the robot will have to pick up different types of pegs placed on a base in its workspace and place them on a similar base at some distance. The pegs are different in shape, one being cylindrical and the other one being cuboid (Figure 3a,b). Each base has two holes of according shape and dimensions for the insertion of each peg, (Figure 3).  
The [L] for the considered task is placed at the geometrical center of the top face of the pick up base (Figure 3). On each of the two pegs, a grasping frame was placed describing the end effector position and orientation in grasping the object.  
The placement base was placed at the distance of 50 mm from the center of the [L] (pick up position) and the \"place position\" distance of 100 mm from \"pick up position\", i.e., 50 mm from the center of [L].  
As such, regarding the cylindrical object, the following points were used for the task (presented in distances relative to the [L]).  
* \({}^{1}\)P\({}_{1}\): Hover point over cylindrical object pick up [\(-\)0.05,0.0.25]\({}^{\rm T}\),
* \({}^{1}\)P\({}_{2}\): Grasping point of cylindrical object [\(-\)0.05,0.0.1]\({}^{\rm T}\),
* \({}^{1}\)P\({}_{3}\): Hover point over cylindrical object placement [0.05,0.0.25]\({}^{\rm T}\), and
* \({}^{1}\)P\({}_{4}\): placement point of cylindrical object [0.05,0.0.1]\({}^{\rm T}\).  
Figure 3: Side (**a**) and top (**b**) faces of pick up and placement bases.  
Therefore, the task sequence was: \({}^{\rm L}\)p\({}_{1}\), \({}^{\rm L}\)p\({}_{2}\), \({}^{\rm L}\)p\({}_{1}\), \({}^{\rm L}\)p\({}_{3}\), \({}^{\rm L}\)p\({}_{4}\), and \({}^{\rm L}\)p\({}_{3}\). For the orthogonal part, the task point sequence was similar.
## 2 Methods
### Definition and Calculation of the Fitness
#### 2.3.1 Task Fitness  
Task fitness is calculated as follows:  
\[TF\left(a_{i,j},t_{m}\right)=W_{i,j}\times L_{m} \tag{3}\]  
The capabilities of the \(i\)th astronaut and \(j\)th lunar robot to perceive, make decisions, and operate are denoted by \(W_{i,j}\), while the abilities required for the mth task are represented by \(L_{m}\).  
There is a sequence of LSO missions that encompasses ground exploration, robot rescue, material transport, and lunar surface samples. In the ground exploration task, the agent is supposed to collect environmental data and plan a moving path while avoiding obstacles. In robot rescue tasks, agents locate, inspect, repair, and move malfunctioning robots. In material transport tasks, agents are supposed to lift, move, and lower material. In the lunar surface sample task, the agent is supposed to plan the cut trajectory and perform the cut operation.  
In Table 1, the capabilities of agents for different LSO tasks can be found. The symbol \"\(\sqrt{\)\" represents that the agent possesses the corresponding ability, while the symbol \"\(\times\)\" represents that the agent lacks the corresponding ability. The LSO tasks are rigorously categorized to provide a clear understanding of the fitness calculations for the tasks. The robots involved are small exploration robots, large rescue robots, material transport robots and lunar rovers. The crew consists of astronauts inside the capsule and outside.  
The normalization of task fitness is calculated as  
\[TF\left(a_{i,j},t_{m}\right)=\frac{TF\left(a_{i,j},t_{m}\right)}{\underset{k} {\max}TF\left(a_{i,j},t_{m}\right)} \tag{4}\]  
To quantify the task fitness, a normalizing computation with specific rules is used. The task fitness has a range of 0.0 to 1.0. A score of 0 indicates that the agent is not sufficiently adaptive to the task, while a score of 1 indicates that the agent is sufficiently adaptive.  
Figure 4 shows the results of the task fitness calculations for different agents. It can be found that the task fitness is low for all four task types, and collaboration between astronauts and robots could significantly improve task fitness. For ground exploration tasks, any combination of astronaut-robot collaborations has a higher task fitness than a single robot. For the robot rescue task, the highest fitness is calculated by combining the astronauts in the capsule and the large rescue robot. For material transport tasks, small exploration robots and large rescue robots have the lowest task fitness. In contrast, the task fitness of other robot-astronaut combinations is relatively high. Only the lunar rover or astronaut-rover collaboration has a relatively high fitness for lunar surface sample tasks.
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 3 Results and Discussion  
### Evolution of Melt Pool Characteristics  
The characteristics of melt pools, including morphology, size, temperature distribution, and internal fluid dynamics, directly reflect the melting and solidification processes of metal powders. Therefore, an in-depth analysis of melt pool characteristics is of great significance for understanding the formation mechanisms of surface morphology features of intermediate layers and the generation mechanisms of internal defects in the LPBF process [25]. Our study is based on the analysis of video image data captured by a coaxial monitoring system,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Scanning** & **SED** & **Laser** & **Scanning** & **SED** \\
**Power** & **Speed** & **(_Es_, Jmm\({}^{-2}\))** & **Power** & **Speed** & **(_Es_, Jmm\({}^{-2}\))** \\
**(_P, W_)** & **(_V_, mm/s)** & **(_Es_, Jmm\({}^{-2}\))** & **(_P_, W)** & **(_V_, mm/s)** & **(_Es_, Jmm\({}^{-2}\))** \\ \hline
200 & 60 & 66.67 & 200 & 100 & 40 \\
250 & 60 & 83.33 & 250 & 100 & 50 \\
300 & 60 & 100 & 300 & 100 & 60 \\
350 & 60 & 116.67 & 350 & 100 & 70 \\
400 & 60 & 133.33 & 400 & 100 & 80 \\
200 & 80 & 50 & 200 & 120 & 33.33 \\
250 & 80 & 62.5 & 250 & 120 & 41.67 \\
300 & 80 & 75 & 300 & 120 & 50 \\
350 & 80 & 87.5 & 350 & 120 & 58.33 \\
400 & 80 & 100 & 400 & 120 & 66.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Single-layer printing process parameter design.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Laser Power (_P, W_)** & **Scanning Speed (_V,_ mm/s)** & **VED (_Ev,_ Jmm\({}^{-3}\))** \\ \hline
1 & 200 & 60 & 1333.33 \\
2 & 250 & 60 & 1666.67 \\
3 & 300 & 60 & 2000 \\
4 & 350 & 60 & 2333.33 \\
5 & 200 & 80 & 1000 \\
6 & 250 & 80 & 1250 \\
7 & 300 & 80 & 1500 \\
8 & 350 & 80 & 1750 \\
9 & 350 & 100 & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-layer (5-layer) printing process parameter design.  
Figure 2: Position distribution of experimental samples on base plate for two groups: (**a**) multi-layer (5-layer) samples, (**b**) single-layer samples, and (**c**) scanning strategy and sample position dimensions. The red arrow indicates the bidirectional serpentine scanning strategy.  
revealing the dynamic evolution of melt pool morphological characteristics, internal heat transfer processes, and melt flow behavior under different process parameter settings.
## 5 Conclusions  
For the purpose of enhancing efficiency in manufacturing industry, it is important to improve performance of robot path planning and tasking scheduling. This paper presents a reinforcement learning-based motion planning method of robot manipulators with focus on smoother and shorter path generation, which means better operation efficiency. To this end, motion planning problem is reformulated as a MDP (Markov Decision Process), called RAMDP (Robot Arm MDP). Then, TD3 (twin delayed deep deterministic policy gradient, twin delayed DDPG) with HER (Hindsight Experience Replay) is designed. DDPG is used since the action in RAMDP is a continuous value and DDPG is a policy gradient tailored to an MDP with a continuous action. Moreover, since many failed episodes are generated in the RAMDP meaning that the episode ends at non-goal state due to mainly collision, HER is employed in order to enhance sample efficiency.  
Future research topic includes how to solve motion planning problem for multi-robot arms whose common working space is non-empty. To solve this problem, configuration space augmentation might be a candidate solution. Since the augmented configuration space becomes high dimensional, it would be interesting to compare performance of the proposed reinforcement learning-based approach by that of sampling-based approaches such as FMMs, PRM, and RTT. Moreover, reinforcement learning-based motion planning for dynamic environment is also a challenge problem.  
[PERSON] and [PERSON] surveyed the backgrounds of this research, designed the preprocessing data, designed the deep learning network, and performed the simulations and experiments to show the benefits of the proposed method. [PERSON] and [PERSON] supervised and supported this study. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Path generation using DDPG with HER.  
Figure 12: Comparison of paths by PRM and the proposed method.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program) (10080636, Development of AI-based CPS technology for Industrial robot applications) funded by the Ministry of Trade, Industry & Energy(MOTIE, Korea), and by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Ministry of Trade, Industry & Energy of the Korea government (No. 20154030200720).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analysis of IM Loads Characteristics in Power System
### Voltage Recovery Criteria  
When a power system is prone to short-term voltage instability, fast reactive support near the load center is essential. The reactive support should be able to restore a stable equilibrium point under severe contingency, and it should be fast, before the IM load decelerates beyond the post-control unstable equilibrium point. IM loads will usually stall when subjected to low voltages due to a fault, even for a very fast clearing time. However, IM loads away from the fault location can ride through faults without stalling if the fault is cleared fast enough.  
UVLS is the ultimate countermeasure for short-term voltage instability. This scheme is used to prevent the stalling of IM loads [23]. To evaluate the voltage recovery of the power system, a transient voltage recovery criterion (TVRC) is required. The objectives of a voltage recovery criterion are to ensure fault ride through by the majority of the loads and to minimize the risk of motor stalling. The authors of [3; 45] proposed a TVRC for the transmission system; however, they did not consider the delayed voltage recovery problem (e.g., IM stalling) in the transmission system. Based on the TVRC transmission system developed by [PERSON] [46], modified criteria were established in this paper and the criteria were applied to the transient voltage recovery characteristics of the Korean power system. In the modified criteria, the time of voltage-recovering up to 95% of the nominal values after the clearing of a fault is added. Table 3 shows the determination of the established voltage stability conditions.  
If the voltage recovered slower than the criteria given in the above conditions, load shedding should be performed. Based on the conditions, this paper establishes a load shedding scheme to prevent short-term voltage instability.