esa-sceva/satcom-chunk-collection huggingface dataset:
# PDDL Task Planning for Tunnel Spraying Based on a Multivariate Coating Accumulation Model
## 1 Introduction
Through the PDDL task planning method, tunnel wet-spray tasks can be abstracted into formal descriptions, effectively decoupling domain knowledge from specific problem-solving and generating reasonable action sequences. The current tunnel spraying task planning methods primarily focus on wet-spray path planning and trajectory planning. However, these approaches cannot directly guide robotic arm operations, as the spraying quality remains dependent on worker expertise. Moreover, they require re-planning for different tunnel environments, lacking generalizability. The PDDL task planning method can decompose complex tunnel spraying tasks directly into executable action sequences for wet-spray robotic arms, thereby replacing manual decision-making processes. This significantly reduces the reliance on human experience for ensuring the spraying quality. Additionally, multiple PDDL problem files can share the same PDDLdomain file. When faced with different tunnel environments, only minor modifications to the problem file are needed for modeling and solving, endowing this method with notable versatility. This paper proposes a tunnel spraying PDDL task planning method integrated with a coating accumulation model. The approach first estimates the tunnel overbreak and underbreak quantities using vehicle localization techniques, then it divides the spraying tasks into hierarchical levels. Subsequently, it performs parameter planning for each task level based on established static and dynamic concrete coating accumulation models, achieving dwell time calculation at the spraying points and end-effector speed determination for mobile spraying operations. Finally, the PDDL task planning method is implemented by constructing PDDL files that integrate task hierarchy planning with parameter optimization, generating executable spraying action sequences. Experimental validations demonstrate the rationality of the derived spraying operation sequences. The organization of this paper is as follows.Section 2 details the hierarchical planning method for spraying tasks, the construction of a multivariate spraying model, and the PDDL based task planning approach for tunnels. Section 3 experimentally validates the effectiveness of the PDDL generated action sequences in controlling tunnel over-excavation. Section 4 summarizes the key findings of this study and proposes future research directions.
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Gas Chromatography (GC)  
Gas chromatography (GC) is a collection of material separation and detection and integration technology produced in the 1950s. GC can detect multiple components with high sensitivity and good quantitative accuracy. At present, it is very popular in the field of SF\({}_{6}\) decomposition component detection. Gas chromatography can detect most of the gas components, including simultaneously detecting SOF\({}_{2}\), SO\({}_{2}\)F\({}_{2}\), SO\({}_{2}\), and SF\({}_{4}\), with a detection accuracy of up to uL/L [53]. However, this method is costly, has a long response time, and requires a large size; in addition, the column must be cleaned regularly. Currently, GC detection is relatively common in the laboratory environment and is not suitable for in-line monitoring or portable testing.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 2 Literature Review
### Dependency Parsing in NLP  
Dependency parsing (DEP) is a crucial natural language processing task ([PERSON] et al., 2018) aimed at identifying syntactic relationships between words in a sentence. It has been widely applied in sentiment analyses ([PERSON] et al., 2018), opinion role labeling (ORL) ([PERSON] et al., 2018), and other fields. [PERSON] (2018) was the first to apply dependency parsing to the entity-relation extraction field, demonstrating that introducing dependency syntax information and performing multi-feature fusion can enrich sentence feature representations and improve model performance in entity-relation extraction tasks. The TaMM method proposed by [PERSON] et al. (2018) maps words and dependency relations that are directly linked to entities in the syntactic dependency tree to memory slots and assigns a weight to each slot based on its contribution to relation extraction. Experiments showed that the incorporation of specific dependency relations effectively enhanced triplet extraction performance. The attention-guided graph convolutional network proposed by [PERSON] et al. (2018) uses the full dependency tree as the input to the graph convolutional network, and during training, it learns to selectively focus on dependency substructures, ultimately achieving superior performance across multiple relation extraction tasks. [PERSON] et al. (2018) employed an attention-based graph convolutional neural network model to identify noise in syntactic knowledge using pruning-based dependency syntax, thereby enhancing model performance in relation extraction tasks. In summary, dependency parsing methods can effectively improve the performance of joint entity-relation extraction. However, current research typically treats dependency syntactic information and semantic representations as separate steps or incorporates syntactic information as additional features into the model, lacking effective integration of syntactic structure and semantic understanding. Our model incorporates dependency syntax information into the encoding layer and employs an additive attention mechanism to assign varying weights to the vectors' output by the encoding layer. This enables a more flexible and effective integration of syntactic and semantic information, thereby addressing the issue of insufficient syntactic information extraction in pre-trained language models.
## 4 Discussion
When antibiotics continue to act, antibiotic tolerance will aggravate the generation of drug resistance. Compared with the life cycle of drug resistance phenotype, antibiotic tolerance is a temporary state. Timely release of antibiotics can restore its tolerance, so the tolerance is reversible [43]. Repeated exposure will maintain the tolerance [44], which reaffirms the foundation for functional studies. During the research of this gene in \(Pm\), [PERSON] et al. studied the mechanism of \(MRSA\) tolerance by using triclosan, an antibacterial agent synthesized by fatty acids, to induce the change of \(MRSA\) tolerance. MDK\({}_{99}\) and the agar diffusion method were used to detect its tolerance. The results showed that triclosan increased the tolerance of \(LL\) to bactericidal antibiotics by 10,000 times, and reduced the efficacy of antibiotics by 100 times. Genetic analysis showed that the antibiotic tolerance mediated by triclosan needs the cooperation of ppGpp [33]. However, until now, there was no report about \(satP\)-gene-mediated tolerance; other membrane transporters have been confirmed to be involved in the formation of tolerance. [PERSON] et al. found a \(MerR\)-type transcriptional activator, which can activate the \(BrlR\) target encoding ABC transport system gene and affect the drug tolerance of bacteria [45]. One of the main stress responses of almost all bacteria depends on the rapid accumulation of ppGpp, an alarmone. Many scholars have found that ppGpp has a great impact on bacterial tolerance. Recently, [PERSON] et al. and others have studied ppGpp and found that the concentration of ppGpp is determined by a highly conserved and widely distributed protein family, and the family is known as Rela-Spot Homologs (RSH). Recent studies have found that RSH can affect not only the tolerance of bacteria, but also the drug resistance and virulence of bacteria [46]. One unanticipated finding was that the virulence of the \(satP\) gene deletion strain was several times lower than that of the wild-type strain, and we found that the treatment rate of the mice infected with \(\Delta PmS\) was higher. This finding broadly supports the work of other studies in this area which links tolerance with virulence [47]. However, there are also some reports contrary to this study. [PERSON] et al. knocked out the \(MazEF\) gene of three strains of bacteria and found that the tolerance of the deletion strain was decreased and the virulence was enhanced, compared with the wild-type strain [48]. These findings may help us understand the relationship between drug resistance and adaptive compensation. This finding has important implications for developing inhibitors. Nevertheless, there are still many unanswered questions regarding whether \(satP\) can be used as a target of drug resistance inhibitors and how effective it could be in clinical application. More attention should be paid to studying \(satP\) and its inhibitors.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Meta-Regression--Sprint Speed vs. Muscle Activation
#### 3.5.2 Regression Coefficients and Significance Testing  
Intercept Coefficient = 30.28 (\(p\) = 0.002). This means that at zero sprint speed, predicted muscle activation would be 30.28% MVC (hypothetically, though unrealistic in practical sprinting). Since \(p\) = 0.002 (< 0.05), this coefficient is statistically significant.  
Sprint Speed Coefficient = 6.32 (\(p\) < 0.001). This coefficient indicates that for every 1 m/s increase in sprint speed, muscle activation increases by approximately 6.32% MVC. A highly significant \(p\)-value (<0.001) confirms that sprint speed strongly influences muscle activation.
# Microstructure and Properties of AA6061/SiCp Composites Sintered under Ultra High-Pressure
## 3 Results and Discussion
### Research on Wear Resistance
#### 3.6.2 Effect of Sintering Temperature on Wear Resistance  
Figure 17 shows the temperature versus wear rate for AA6061/5 vol.% N-SiCp. Figure 17 shows that increasing temperature will cause the wear rate to first decrease and then increase. Wear rate can reach up to the minimum at 600 \({}^{\circ}\)C, \(0.225\times 10^{-5}\) g/m. As the temperature increases, the hardness will increase, suggesting a better wear resistance of the sample. At 650 \({}^{\circ}\)C, the wear rate increases again. This is mainly due to the Al\({}_{4}\)C\({}_{3}\) brittle phase generated at the interface making the bond between the reinforcement and the Al matrix not tight at high temperature and is easily peeled off during the wear process.  
Figure 16: Wear morphology of N-SiCp/Al composite with different volume fraction under 4.5 GPa and 550 \({}^{\circ}\)C with 3 (**a**), 5 (**b**), 10 (**c**), 15 (**d**) vol.% N-SiCp.  
Figure 17: Effect of sintering temperature on the wear rate of AA6061/5 vol.% N-SiCp under 4.5 GPa.  
Figure 18 shows the friction and wear morphology of AA6061/5 vol.% N-SiCp sintered at 450, 550, 600, and 650 \({}^{\circ}\)C, respectively. A large number of exfoliated blocks appear on the surface in Figure 18a. At 450 \({}^{\circ}\)C, the density and hardness are relatively low, and the surface has a large plastic deformation zone during the wear process. Additionally, the subsurface layer of the initiation deformation zone of cracks will spall. At 550 \({}^{\circ}\)C in Figure 18b, the wear resistance is improved, and some shallow furrows and a small number of peeling pits can be found. It can be assumed that temperature increases the interfacial bonding strength and the hardness, so the wear rate is reduced. With the temperature further increasing up to 600 \({}^{\circ}\)C, the wear rate is just \(0.22\times 10^{-5}\) g/m. There are fewer small slim scratches and peeling blocks on the surfaces in Figure 18c than those in Figure 18a-d, which means the lightest damage. Meanwhile, a large number of Al particles exists in the microstructure, and the light interface reaction enhances the combination strength, resulting in the best wear resistance. Then, at 650 \({}^{\circ}\)C, the wear rate increases, and wear resistance is reduced. A few deep furrows and spalling appear on the surface in Figure 18d. It might contribute to the Al\({}_{4}\)C\({}_{3}\) phase produced at a high temperature. During the wear process, cracks are easily generated at the interface, causing the surface to peel off and abrasive grains. Combined with the hardness in Figure 12b and wear rate in Figure 17, according to the role of Al\({}_{4}\)C\({}_{3}\) in the composites, the wear rate of the composite possessing large hardness is small, indicating a good wear resistance. Therefore, it was also proven that the hardness was enhanced by the formation of the Al\({}_{4}\)C\({}_{3}\) phase, resulting from the increment of wear rate.
###### Abstract  
The fifth-generation (5G) network is the current emerging technology that meets the increasing need for higher throughputs and greater system capacities. It is expected that 5G technology will enable many new applications and services. Vehicle-to-everything (V2X) communication is an example of an application that is supported by 5G technology and beyond. A V2X communication system allows a vehicle to be connected to an entity, such as a pedestrian, another vehicle, infrastructure, and a network, to provide a robust transportation solution. It uses many models and strategies that are usually based on machine learning (ML) techniques, which require the use of a vehicle dataset. In this paper, a real vehicle dataset is proposed that was generated in the city of Los Angeles (LA). It is called the Vehicle dataset in the city of LA (VehDS-LA). It has 74,170 samples that are located on 15 LA streets and each sample has 4 features. The LA dataset has been opened to allow researchers in V2X and ML fields to use it for academic purposes. The main uses of the VehDS-LA dataset are studies related to 5G networks, vehicle automation, or ML-Based vehicle mobility applications. The proposed dataset overcomes limitations experienced by previous related works.  
5G; Google Maps; IoV; ITS; Los Angeles; machine learning; V2X; vehicle dataset Article  
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# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Transformer Grouping and Fault Types
#### 3.1.1 Group X Transformers  
Group X consists of model transformers intentionally modified to exhibit specific flaws. These models share similar RLC circuit structures with actual transformers but are limited to laboratory research applications. The model transformers' windings allow for varied connections, enabling simulation of multiple defect scenarios to generate a comprehensive database.
## 4 Factors Influencing Reactivity
### Impact of PMS and Catalyst Concentrations  
The presence of PMS initiates the production of ROS, and varying PMS concentrations will result in distinct influences on the contaminant removal rate. [PERSON] et al. [179] noted that the elimination rate of diethyl phthalate increased with rising PMS concentrations; however, it declined when the PMS concentration reached 5 mM. Additionally, [PERSON] et al. [66] disclosed that the decolorization rate of RhB initially increased but later decreased with the rise in PMS concentration. The addition of PMS can increase the availability of oxidants, leading to enhanced degradation efficiency for pollutants. However, overabundant PMS can result in self-quenching reactions, which may be detrimental to the process. As depicted in the Equations (12) and (13), an excess of PMS will facilitate the transformation of \(HO^{\bullet}\) and \(SO_{4}^{\bullet-}\) into \(SO_{5}^{\bullet-}\) and \(HSO_{4}^{-}\) with reduced reactivity [180; 181]. Consequently, selecting the appropriate PMS concentration is of paramount importance for practical use [182].  
\[HO^{\bullet}+HSO_{5}^{-}\to SO_{5}^{\bullet-}+H_{2}O \tag{12}\]  
\[SO_{4}^{\bullet-}+HSO_{5}^{-}\to HSO_{4}^{-}+SO_{5}^{\bullet-} \tag{13}\]  
The concentration of catalysts is also a crucial element in PMS activation. Raising the concentration of copper-based catalysts can not only introduce additional active sites for PMS activation but also enhance the likelihood of interacting with contaminant molecules. Furthermore, augmenting the concentration of copper-based catalysts proved advantageous in the production of radicals such as \(SO_{4}^{\bullet-}\), which facilitated the elimination of NPX [29]. However, an escalation in the concentration overabundant copper-based catalysts may produce an abundance of oxidative radicals and promote self-interaction amid radicals (Equations (14)-(16)) [183]. [PERSON] et al. [184] observed that as the catalyst concentration rose from 0.1 g\(\cdot\)L\({}^{-1}\) to 0.3 g\(\cdot\)L\({}^{-1}\), the removal rate of azo dye Orange G steadily rose. Nonetheless, it showed minimal change once the catalyst concentration surpassed 0.3 g\(\cdot\)L\({}^{-1}\). The associated rationale may be that an excessive catalyst concentration leads to an overabundance of radical species, thereby raising the self-quenching rate of radicals.  
\[HO^{\bullet}+HO^{\bullet}\to H_{2}O_{2} \tag{14}\]  
\[SO_{4}^{\bullet-}+SO_{4}^{\bullet-}\to S_{2}O_{8} \tag{15}\]  
\[SO_{4}^{\bullet-}+HO^{\bullet}\to HSO_{5}^{-} \tag{16}\]  
According to [PERSON] et al. [185], in the degradation process of BPA, increasing the PMS concentration has a lesser impact on BPA elimination compared to escalating the catalyst concentration. While raising the dose of PMS can indeed improve its reactivity and facilities the removal of pollutants, the limited active sites furnished by the catalyst will inevitably lead to competition among PMS molecules, which can influence the overall elimination procedure.
## 3 Communication Results by Experiments
### Experimental Setup  
Figure 1 shows the steel pipe specimens and the acoustic sensors that were used in experiments. Two sets of experimental configurations were employed. The first set of experiments included a carbon steel pipe specimen of \(1.833\) meters in length, \(70\) mm in outside diameter and \(4\) mm in wall thickness (see Figure 1(a)) and two PZT wafers (PSI-5A4E, Piezo Systems, Inc., Cambridge, MA) that were mounted on the surface of the carbon pipe using cyanoacrylate adhesive, located \(1.5\) meters apart. Each wafer was \(12\) mm long and \(6\) mm wide. The second set of experiments included a stainless steel pipe of \(1.5\) meters in length, \(115\) mm in outside diameter and \(6\) mm in wall thickness (see Figure 1(b)). On the stainless steel pipe, two PZT wafers (PSI-5A4E) were mounted on the surface of the pipe, also using cyanoacrylate adhesive, located \(0.69\) meters apart. Each wafer was \(10\) mm long and \(10\) mm wide.  
Figure 1: Lead zirconate titanate (PZT) wafers mounted on the surface of two pipe specimens. (**a**) A carbon steel pipe; (**b**) a stainless steel pipe.  
The signal transmission and data acquisition unit was a National Instruments (NI) PXI system, which consists of an arbitrary waveform generator (AWG), a digitizer/oscilloscope and a controller. The waveform generator had a sampling rate \(100\) MS/s and a \(16\)-bit resolution. The AWG can generate arbitrary waveforms in response to the dispersive medium, thus implementing time reversal. The  
Figure 2: Illustration of experimental setup with cable connections and circuit wiring. (**a**) A carbon steel pipe specimen; (**b**) a stainless steel pipe specimen. Water can be filled into the pipe.  
digitizer had a sampling rate of \(100\) MS/s and a \(14\)-bit resolution. The waveform generator drives the transmission of the PZT sensor. The digitizer was connected to the receiving PZT sensor and records waveforms. The recorded waveforms were processed online using LabView programming for symbol demodulation and detection. For simplicity, a peak detector was used. The controller synchronizes the operations of the waveform generator and the digitizer. The experimental setup is illustrated in Figure 2. Figure 2(a) depicts the cable connection between the PXI system with a carbon steel pipe specimen. Figure 2(b) depicts the connection for a stainless steel pipe specimen. Water can be filled into the pipe for testing.
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 4 Full-Coverage Path Planning  
To achieve autonomous robot full-coverage path planning, through the analysis of the previous study results related to CPPS, we selected a series of path-planning principles based on the improved priority heuristic algorithm, which can considerably improve the efficiency of the robot. A large exterior glass curtain wall surface is often complex; in addition to conventionally shaped obstacles, U-shaped obstacles may be present, which pose challenges to the robot body moving strategy. The dead zone position problem also arises during the working process. Therefore, effectively avoiding obstacles to achieve full coverage and escaping dead zones as quickly as possible have become key to improving robot performance.  
To solve these problems, an improved priority heuristic algorithm was designed, which prioritizes the moving direction according to the priority order designed based on the center place of the robot in the established wall model. Thereby, a reasonable ergodic path can be planned. To further illustrate the improved priority heuristic algorithm, we selected three common wall models and proposed three path planning principles as examples.
###### Abstract  
Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM's superior accuracy, showcasing lower errors in multiple metrics. TRFM's alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method's precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency.  
maritime management; big data; traffic flow prediction; transformer  
1  
[PERSON], [PERSON], [PERSON], Z. Spatio-Temporal Transformer Networks for Inland Ship Trajectory Prediction with Practical Deficient Automatic Identification System Data. _Appl. Sci._**2024**, _14_, 10494. [[https://doi.org/10.3390/app142210044](https://doi.org/10.3390/app142210044)]([https://doi.org/10.3390/app142210044](https://doi.org/10.3390/app142210044))