esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We propose a vertiport location-allocation methodology for urban air mobility (UAM) from the perspective of transportation network topology. The location allocation of vertiports within a transportation network is a crucial factor in determining the unique characteristics of UAM compared to existing transportation modes. However, as UAM is still in the pre-commercialization phase, with significant uncertainties, there are limitations in applying location-allocation models that optimize objective functions such as maximizing service coverage or minimizing travel distance. Instead, vertiport location allocation should be approached from a strategic perspective, taking into account public capital investments aimed at improving the transportation network by leveraging UAM's distinct characteristics compared to existing urban transportation modes. Therefore, we present a methodology for evaluating the impact of vertiport location-allocation strategies on changes in transportation network topology. To analyze network topology, we use the Seoul Metropolitan railway network as the base network and construct scenarios where vertiports are allocated based on highly connected nodes and those prioritizing structurally vulnerable nodes. We then compare and analyze global network efficiency, algebraic connectivity, average shortest path length, local clustering coefficient, transitivity, degree assortativity and modularity. We confirm that while allocating vertiports based on network centrality improves connectivity compared to vulnerability-based allocation, the latter approach is superior in terms of network efficiency. Additionally, as the proportion of vertiports increases, the small-world property of the network rapidly increases, indicating that the vertiport network can fundamentally alter the structure of multimodal transportation systems. Regardless of whether centrality or vulnerability is prioritized, we observe that connectivity increase exponentially, while network efficiency changes linearly with the increase in vertiport proportion. Our findings highlight the necessity of a network-based approach to vertiport location allocation in the early stages of UAM commercialization, and we expect our results to inform future research directions on vertiport allocation in multimodal transportation networks.  
urban air mobility (UAM); vertiport location allocation; transportation network topology; network vulnerability; network centrality +
Footnote †: journal: Applied Sciences
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### Advanced AIoT Integration in Aviation on the Base of WDAR and FMDE Synergy
* The dual analytical systems allow for both immediate, airline-specific insights and potentially more in-depth, industry-wide analyses. * With more data points and faster transmission, airlines can make more informed decisions about flight operations, maintenance scheduling, and resource allocation. * The comprehensive monitoring enabled by this system can lead to earlier detection of potential safety issues, allowing for proactive interventions. However, the implementation of such an advanced AIoT system also presents challenges:
* Handling and analyzing over 20,000 parameters require significant computational resources and sophisticated data management strategies. * The effectiveness of the system still relies heavily on ground-based infrastructure, which may vary significantly between different airports and regions. * With increased data collection and transmission comes an increased need for robust cybersecurity measures to protect sensitive aircraft and airline information. * Combining data from multiple systems (WDAR and FMDE) and analyzing it across different platforms (airline and external systems) requires careful system integration and data standardization. The aircraft communication architecture featuring both WDAR and FMDE options represents a significant leap forward in the application of AIoT to aviation health monitoring. By dramatically increasing the volume of data collected and providing advanced transmission and analysis capabilities, this architecture lays the groundwork for a new era of predictive maintenance, enhanced safety, and operational efficiency in aviation. As AI technologies continue to advance and IoT infrastructure improves, we can expect even greater capabilities in real-time analyses, automated decision-making, and predictive accuracy, further revolutionizing the field of aviation health monitoring and maintenance.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 6 Conclusions  
SDFG is widely used to model streaming applications. With the current proliferation of dedicated homogeneous multi-core processors, it is important that streaming applications make full use of these computational resources.  
We extend the DAG model using data-driven methods to propose the SDAG model as a model for parallel computation of SDFGs under the von Neumann architecture. Then, we improved the PAPS algorithm and proposed the LSEFT algorithm for scheduling SDFGs on a homogeneous multi-core processor system. Based on an experimental study of 956 randomly generated SDFGs of different sizes, the LSEFT algorithm outperforms the pre-improvement algorithm in terms of performance metrics (speedup and parallel efficiency). The LSEFT algorithm is a feasible solution to the SDFG scheduling problem on homogeneous systems.  
For the task priority calculation method of the LSEFT algorithm, we studied a new calculation method to avoid solving the longest path in the DAG. In the processor selection phase, we compared the load balancing strategy with the shortest completion time strategy. The disadvantage of the LSEFT algorithm is that it is based on HSDFG scheduling and cannot be scheduled in the original SDFG graph. Furthermore, the LSEFT algorithm does not take into account the communication overhead between processors when scheduling. We will extend the LSEFT algorithm to accommodate heterogeneous processor systems while incorporating communication overhead in the future work. We also working on future scheduling methods based on reinforcement learning and our dataset can be used for reinforcement learning as well.  
Conceptualization, L.W., C.W. and H.W.; methodology, L.W.; software, C.W.; validation, L.W., C.W. and H.W.; formal analysis, L.W.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (project no. CEMEE2018K0302B), Sichuan Science and Technology Program in China Grant Number 2020 YFG0059 and Fundamental Research Funds for the Central Universities in China Grant Number ZYGX2019J128.  
Not applicable.  
Figure 8: Average parallel efficiency with different scheduling algorithms.  
Data Availability Statement: The data presented in this study are openly available in [Github] at [[[https://github.com/wangcgzzu/SDFG-data.git](https://github.com/wangcgzzu/SDFG-data.git)]([https://github.com/wangcgzzu/SDFG-data.git](https://github.com/wangcgzzu/SDFG-data.git))] (accessed on 9 January 2022).
## 3 MFDFA Algorithm  
MFDFA is an efficient FOSP technique to detect multifractality in a time series since it does not require any knowledge about the process time delay or other process parameters [26]. MFDFA can estimate the multifractal spectrum of the generalized Hurst exponent from a time series and it does not require any knowledge about the process time delay or other process parameters [27]. Currently, MFDFA has been successfully applied to analyze various data, such as hydrographic data [28], wind records [29], financial time series [30], traffic time series [31], mechanical vibration signals [32], etc. It has proven to be a powerful tool for uncovering the multifractality of non-stationary time series in the complex systems. In this study, MFDFA is applied to detect the presence of multifractal and monofractal properties in the MIMO systems with external disturbance and noise. Then, the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
###### Abstract  
Faults play a pivotal role in controlling fluid migration and retention within sedimentary basins, particularly in the context of fault-bound hydrocarbon reservoirs. Assessing the stability and sealing capabilities of faults enhances our comprehension of these systems and aids in the identification of pathways for fluid migration. In this study, we focus on a series of fault-bound hydrocarbon accumulations located in the southern Wenchang A subbasin within the Pearl River Mouth Basin. We emphasize the significant influence of faults in governing the processes of hydrocarbon migration and accumulation. By leveraging 3D seismic data and well information, we have assessed the sealing potential of ten faults that either currently retain hydrocarbon columns or have the potential to do so. Our analysis reveals that even faults with a relatively low Shale Gouge Ratio (as low as 15%) can effectively support substantial column heights. Taking into account factors, such as the source rock maturity, fault activity, geometry, sealing potential, and the distribution of hydrocarbon accumulations, we have formulated a conceptual model for hydrocarbon migration and accumulation within the study area. This model underscores potential fluid traps within the rift basin, shedding light on the complex dynamics of hydrocarbon movement in this region.  
fault sealing; hydrocarbon migration; hydrocarbon accumulation; Wenchang A subbasin; Pearl River Mouth Basin
## 2 Material and Methods
### Crop Management  
A conventionally puddled rice field was uniformly transplanted on 11 July 2013, with a spacing of 20 cm (row to row) \(\times\) 15 cm (hill to hill) distance. The field was irrigated whenever the moisture content dropped below the saturation level during the growing season, except during the last two weeks before harvesting. Wheat was sown on 16 December 2013, maintaining a plant spacing of 20 cm (row to row) \(\times\) 5 cm (plant to plant) distance. Urea, at a rate of 120 kg N ha\({}^{-1}\), was applied in three splits of 60, 30, and 30 kg N ha\({}^{-1}\) at \(-\)1, 47, and 66 days after transplanting (DAT) in rice and \(-\)1, 25, and 67 days after sowing (DAS) in wheat. The wheat crop was irrigated at the crown root (22 DAS), filtering (44 DAS), late jointing (62 DAS), flowering (85 DAS) and milking (97 DAS). Phosphorus (26.2 kg ha\({}^{-1}\)) and K (50 kg ha\({}^{-1}\)) were incorporated into the soil at the time of sowing using single super phosphate (SSP) and muriate of potash (KCI), respectively, in both rice and wheat. No pesticides were applied during crop cultivation. The crop leaf area index was measured using a plant canopy analyzer (LAI-2000) at the key growth stages. The yield related parameters were measured at the harvest of the crops. Total plant biomass of 1335 and 1392 gm\({}^{-2}\) was recorded at harvest in rice and wheat, respectively.
# Robotic Coverage Path Planning for Ultrasonic Inspection
## 1 Introduction  
Coverage path planning (CPP) deals with the problem of finding a path for a robot to cover some region of interest. With the growing number of applications related to mobile robotics, CPP is a highly relevant problem to study. This is especially true as robotic applications cover more and more areas of everyday human life. A few applications to mention are vacuum cleaning robots, windmill inspection drones, and robotic quality control in industry.  
Depending on the application, coverage may relate to visiting, inspecting, or treating (moving, painting, etc.) the complete region. Hence, the definition of what region is covered by which robot configuration or position strongly depends on the application. For example, the region covered by a lawn mower robot at a specific position is a disk-shaped area around the center point of the rotating blade. For a robot performing optical inspection, the covered area is derived from the camera position, the field of view, and obstacles within the field of view. In any case, the definition of coverage is an important topic for all coverage planning algorithms.  
We introduce an off-line method for the coverage path planning that is built upon a heuristic based on streamlines. Our main application is robotic inspection via ultrasonic testing (UT), for which a highly automated workflow was described in [1]. In order to calculate coverage, we rely on a ray tracing approach, using a 3D model of the specimen to be inspected. Via ray tracing, it is possible to generate a volumetric representation of covered regions inside the inspected part. While the ray tracing approach is very specific to UT, the heuristic of using streamlines is applicable to other coverage planning problems.  
This article is structured in the following way. In Section 2, we review related work on coverage path planning and the coverage calculation. In Section 3, we provide an overview about streamlines in general. To derive streamlines for our CPP algorithm,we build upon a vector field defined by local surface curvature. Surface curvature is discussed in Section 4. Another building block for our method is ray tracing to simulate the propagation of ultrasound waves--outlined in Section 5. Section 6 explains how we put everything together for the proposed coverage path planning algorithm. An evaluation of the algorithm is provided in Section 7, and conclusions are drawn in Section 8.
## 3 Design and Preliminary Verification
### Parameter Design  
This section presents the parameter design steps of the proposed system. A flow chart of the parameter design is depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(L_{1}\) & 80 \(\mu\)H & \(M_{\text{P1}}\) & 1.21 \(\mu\)H \\ \(L_{\text{P}}\) & 45 \(\mu\)H & \(M_{\text{s1}}\) & 0.26 \(\mu\)H \\ \(L_{\text{s}}\) & 136.68 \(\mu\)H & \(M_{\text{ps}}\) & 26 \(\mu\)H \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation values for compensation inductance and LCT.  
Figure 5: 3D model of the LCT.  
Figure 6: Design approach of the proposed LC/S compensated IPT system.  
Step 1: The operating angular frequencies \(\omega_{\rm cc}\) and \(\omega_{\rm cv}\) of the proposed system in CC and CV modes are determined in accordance with the specific applications.  
Step 2: Determine the appropriate transconductance gain G and voltage gain E.  
Step 3: Set the size and turns of \(L_{1}\), \(L_{\rm p}\) and \(L_{\rm s}\).  
Step 4: The self-inductances of \(L_{1}\), \(L_{\rm p}\) and \(L_{\rm s}\) and mutual inductance \(M\) are estimated by Maxwell.  
Step 5: The compensation capacitances \(C_{1}\) and \(C_{2}\) can be calculated by using Equation (20).  
Step 6: Judge whether Equations (9) and (17) are satisfied. If Equations (9) and (17) hold, system parameters are successfully designed. If Equations (9) and (17) are not satisfied, the design parameters are unreasonable. Repeat step 3 until Equations (9) and (17) are hold.  
Finally, the theoretical system parameters are summarized in Table 3.
## 2 Sensing Method
### Capacitive Sensing and Inductive Sensing  
As far back as 1967, [PERSON] devised an inductance-capacitance (LC) passive wireless sensor. This sensor utilizes a spiral inductor linked to a sensing capacitor for the measurement of intraocular pressure. A readout coil is inductively coupled with the sensor to enable the wireless acquisition of sensor data. Minute disturbances cause alterations in the sensing capacitor, leading to a linear shift in its resonant frequency. Such wireless LC sensors have been further explored and applied for the monitoring of physical, chemical, or biological parameters [56; 57]. Nevertheless, in applications like implantable, wearable, or other devices subject to geometric constraints, the reduction in the size of coils diminishes inductive coupling. Consequently, there is a pressing need to enhance the sensitivity of LC sensors. In recent years, researchers have employed a diverse range of methods to boost the sensor sensitivity.  
The LC resonant pressure sensor relies on the sensitivity of capacitive/inductive components to the physical quantity under measurement. In an LC resonant circuit, alterations in the capacitance/inductance value lead to a change in the resonant frequency of the entire circuit. This variation can be detected by a coupled external inductive coil, causing a change in the impedance of the entire system, thereby achieving the conversion of the physical quantity into an electrical quantity. This approach obviates the need for active components, and passive operation does not necessitate a battery or an energy harvesting system. Depending on the distinct sensitive components, LC resonant sensors are classified into capacitive sensors and inductive sensors. This particular type of sensor features a simple structure and is amenable to straightforward manufacturing processes. Simultaneously, it exhibits a rapid dynamic response capacity, rendering it highly suitable for the real-time monitoring of rapidly fluctuating physiological signals. Incorporating an inductance coil within the sensor structure enables the implanted device to be powered via this configuration [58]. However, a notable drawback is that the stray capacitance within the lead cable and electronic circuit of the capacitive sensor can be substantially larger than the sensor's intrinsic capacitance, thereby diminishing the measurement accuracy [14].
## 4 Handover and Mobility Process in 5G HetNet
### Key HO Parameters in 5G HetNet  
There are three important parameters considered for the HO procedure in 5G HetNet: HO decision parameters (HODPs), HO control parameters (HCPs), and key performance indicators (KPIs). These parameters play a crucial role in managing user mobility and enhancing HO performance in 5G HetNet and are discussed below.
# A Study on Finding Partners through Patent Network Analysis of UAM (Urban Air Mobility)
## 4 Discussion and Conclusions  
### Discussion  
This study has significance in identifying potential partner companies for future cooperation by analyzing companies' technical power for each UAM component and deriving and analyzing the technology of major component-makers of the competitor. However, it is difficult to simply compare the patents of BOEING and BELL, which are American companies, and AIRBUS, which is based in Europe. AIRBUS is a consortium of European airline companies and has a history of being established through MOUs between British, French, and German governments after European aircraft manufacturers discussed it in the mid-1960s to compete against BOEING. As confirmed through the analysis of the joint patent application, UAM-related research is still in progress at AIRBUS branch offices in each European country, and it has a structure in which joint research is inevitable according to the historical origin of AIRBUS.  
Figure 13: Network Analysis of UAM Technology.  
Due to the nature of the defense industry, closed innovation is common in cases like BOEING and BELL, but attention is focused on what kind of innovation AIRBUS can create in the UAM field, which is developing technology through open innovation between countries. In addition, there is a limitation in that there is the possibility of omitting startups with significant R&D capabilities when new partners are derived through the analysis of patent application history. Moreover, when tracking inventors through patent analysis, there could be a difficulty in identifying female inventors due to a change of their last name before/after marriage (Japan, United States, etc.). In future research, technology analysis for technology startups that can be applied to the UAM field will be reinforced, and it seems necessary to discuss methods to address the problem in inventor-tracking. The inventor belongs to a university at a specific point in time and then gets a job at a specific company at the time of employment, and if he/she changes jobs after that, know-how related to technology development is transferred to other companies. In the case of core inventors, since they are relevant to R&D, it would be meaningful to identify their movement paths through further network analysis.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
###### Abstract  
In this paper, the micro-phasor measurement system is developed to support phasor identification of distribution transformers for the distribution mapping management system (DMMS) of Taiwan Power Company (TPC). The one pulse per second signal (1 PPS) from the global positioning system (GPS) is used to synchronize the voltage sampling between reference and measurement sides. By receiving 1 PPS from GPS, the phasor measurement of three-phase voltage at the reference site of the substation is stored in the master station with a time-stamp. This paper proposes a timing synchronous module with an embedded temperature compensated crystal oscillator (TCXO) to generate highly accurate timing for field measurement of underground distribution facilities. Mobile communication network is used to compare measurement results between reference and field sides of voltage angle that define distribution transformer phasor. This paper also designs and develops an application for mobile devices by importing DMMS database, which is used to search the attributes of the distribution transformers for measurement at the field. After the transformer phasor is identified, the results are stored in the mobile device to update the attributes of distribution transformers in DMMS.  
Micro-phasor Measurement System; Distribution Mapping Management System; Temperature Compensated Oscillator +
Footnote †: journal: Applied Sciences
## 4 Experimental Evaluation and Results
### Robustness Evaluation and Crossvalidation on KIMORE Dataset
To evaluate the reliability of our model performing on the KIMORE dataset, we used a five-fold cross-validation. Cross-validation is a statistical approach for evaluating machine learning models with a prediction aim. A round of cross-validation divides the data into complimentary subsets, with training on one set and validation testing on the other. To decrease variability, we performed five-fold cross-validation (a larger number of folds was not reasonable, considering the limited data in KIMORE).
## 3 Materials and Methods
### Anomaly Detection Techniques
#### 3.4.4 Backtesting  
The cross-validation process carried out in time series is known as backtesting, where the main and fundamental characteristic is that the temporal order of the data in the time series is maintained, instead of the classic scope of cross-validation, where random distributions of observations are made in the data.  
The temporal order of the data in the time series is maintained by introducing an incremental scope from the past to the future. In this approach, data are enabled for model training or testing as it progresses through the time series. This process is performed using a selected feed or window parameter, which determines the amount of data considered in each iteration of the model.  
Within these practices, there is the combination of sliding windows and retraining. That is, the backtesting process consists of advancing towards the future n number of steps determined by the value of the sliding window, training the model with the available data in each of the advances and evaluating it with the corresponding section of the test data, within the panorama selected for this instance of backtesting, as shown in Figure 2. The process is repeated the determined number of times or until the available data are exhausted. In each evaluation of the model, the different metrics of interest are collected to draw conclusions once the process is completed.