esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 2 Materials and Methods
### Path Planning Methods
#### 2.3.3 Path-Guided Hybrid Artificial Potential Field (PGHAPF) Method  
Based on the above, the PGHAPF method proposed in this paper can take into account the global prior path optimization and the influence of the local minimum. In the process of path execution, this method can deal with dynamic TSs and possible irregular static obstacles in advance to take appropriate CA actions in time.  
If the prior path is reliable, in the case of no TSs or with TSs but no collision risk, the own ship will be guided only by gravitational force (\(F_{att}\)) and navigate along the prior path. As shown in Figure 10, when there is any TS that has collision risk with the own ship, the negative gradient of the potential field is calculated. Then, the resultant force (\(F_{total}\)), i.e., the sum of repulsive force (\(F_{nt}\)) about the relevant TSs and the gravitational force (\(F_{att}\)) about the goal, is calculated to determine the steering direction of the own ship (\(\psi_{os}\)) and the dynamic position (\(p_{os}\)) in the PD controller. The \(F_{rd}\) drives the own ship to avoid all TSs under the COLREGS, and the \(F_{att}\) makes the own ship move toward the goal or current waypoint WPT\({}_{k}\). Once the TS enters the emergency CA zone and meets the emergency CA condition described in Section 2.3.2, the repulsive force for emergency CA (\(F_{re}\)) other than \(F_{rd}\) should be calculated to accomplish the successive procedures.  
When static obstacles appear suddenly near the prior path or the own ship deviates from the prior path due to the action of avoiding TSs, these nearby static obstacles may lead to a new danger for the own ship, especially in restricted waters. Because of the non-cooperative feature of static obstacles, this algorithm gives higher priority of CA to static obstacles. No matter what the precondition is, when a static obstacle is encountered in front of the own ship (i.e.,\(f(p_{2})\geq\lambda\)), the minimum potential following method shown in Figure 9 is used to calculate the steering direction (\(\psi_{os}\)) of the own ship. Once the danger originated by a static obstacle is eliminated, that is, \(f(p_{2})<\lambda\), the algorithm next determines \(\psi_{os}\) by calculating the negative gradient and resultant force (\(F_{total}\)) of the potential field that is formed by dynamic TSs and the goal or current waypoint.  
When the distance between the own ship and WPT\({}_{k}\)\(d_{k}\leq r_{k}\), or when the own ship has crossed or is crossing the angular bisection of the angle composed of path legs L\({}_{k-1}\) and L\({}_{k}\), it is deemed that the current waypoint WPT\({}_{k}\) has been reached, and then the next waypoint WPT\({}_{k+1}\) is the new goal point. When the distance to the final goal \(d_{g}\leq r_{g}\), the algorithm loop will terminate. Figure 10 shows a flowchart of the PGHAPF method, where some variables refer to the authors' previous work [13] except those stated in this paper.  
Figure 9: Course alteration determined by the minimum potential following method.
## 3 Drone Routing Algorithms
### Trajectory Planning
#### 3.1.1 Attention-Based Pointer Network (A-Ptr-Net)  
Finding optimal trajectory for drone-based delivery systems is indispensable from a design perspective, and considerable design effort is required from a research perspective. The A-Ptr-Net [6] recognizes the drawbacks of the traditional delivery logistics drone using heuristic approaches when drones need to make multiple deliveries to the customer from depot and return for the purpose of recharging and reacquiring parcel. From the drone trajectory data, the scheme puts additional emphasis or attention on the newest drone trajectory regardless of the discovered distance or distance matrix. The developed model is self-taught and utilizes reinforcement learning to update the distance matrix for the newly updated flight trajectory. In the decoder part of the devised model, a multi-head attention mechanism with a fully connected feed-forward layer is added to further enhance the trajectory data. The novelty of this step is to obtain multiple outputs instead of a single entity to increase the generated data and give attention based on the desired requirement. Thus, a convex function is designed to mitigate delivery error and ensure that the targeted delivery is within a reasonable time frame.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
### Organization  
The remaining paper is presented as follows. In Section 2, the system model of Multi-user MISO A-RIS-assisted SWIPT system is first established and the optimization problem is formulated. Section 3 presents the overall algorithm and the computational complexity analysis. Simulations are executed in Section 4. Section 5 arrives at the conclusions of this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Full Name** \\ \hline
5G & Fifth-Generation Communication \\
6G & Sixth-Generation Communication \\ RIS & Reconfigurable Intelligent Surface \\ MIMO & Multiple-Input Multiple-Output \\ MISO & Multiple-Input Single-Output \\ A-RIS & Active Reconfigurable Intelligent Surface \\ P-RIS & Passive Reconfigurable Intelligent Surface \\ SWIPT & Simultaneous Wireless Information and Power Transfer \\ ID & Information Decoding \\ EH & Energy Harvesting \\ TS & Time Splitting \\ PS & Power Splitting \\ BS & Base Station \\ UAV & Unmanned Aerial Vehicle \\ BCD & Block Coordinate Descent \\ CSCG & Circularly Symmetric Complex Gaussian \\ SDR & Semi-Definite Relaxation \\ SCA & Successive Convex Approximation \\ SDP & Semi-Definite Program \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviation correspondence table.
# A Review on Decentralized Finance Ecosystems
## 2 Literature Review  
### Traditional Finance (TradFi)  
In TradFi, intermediaries like banks facilitate transactions among market participants [15]. It emphasizes KYC (know your customer) and AML (anti-money laundering) compliance to mitigate financial crimes and relies on a legal framework for trust and transaction fairness. KYC involves verifying customers' identities, while AML encompasses a comprehensive framework for combating money laundering and financial crimes. KYC serves as a mechanism within the broader AML framework, ensuring customer authenticity through identity verification measures.  
TradFi offers financial services with payment flexibility and investment support, although it involves limited fund control. Its extensive infrastructure allows for broader-scale financial services compared to cryptocurrency. TradFi institutions are exploring blockchain integration to improve their operations and consider offering financial products and services in the cryptocurrency space. However, TradFi approaches the crypto market cautiously, prioritizing areas such as payment systems and gradually incorporating blockchain technology in banks and financial services [18; 19].  
TradFi is often perceived as lacking innovation compared to DeFi, primarily due to regulatory requirements, legacy systems, and centralization. Regulatory constraints and established infrastructure in TradFi can impede innovation, while DeFi, operating in a decentralized and unregulated environment, enables faster development and adoption of new technologies and financial products. TradFi intermediaries, such as banks, limit accessibility and transparency, whereas DeFi aims to democratize finance by eliminating intermediaries and fostering inclusivity. Furthermore, TradFi's limited accessibility, requiring established relationships with financial institutions, contrasts with DeFi's open-source blockchain technology, making it accessible to anyone with an internet connection and promoting financial inclusion. TradFi's slower adaptability to new technologies is contrasted by DeFi's ability to quickly introduce and deploy innovative solutions, facilitated by its decentralized nature and rapid software development. Additionally, TradFi institutions' risk aversion, driven by concerns about reputation and financial stability, stands in contrast to DeFi's willingness to take on more risk in the pursuit of innovation and growth. Despite these disparities, TradFi remains significant, serving many people and offering advantages such as a well-established infrastructure, regulatory compliance, and established relationships with financial institutions [9; 20; 21].
## 4 Results and Discussion
### PID Controller
\begin{table}
\begin{tabular}{c l l l l} \hline
**Parameters** & **GWO** & **HGS** & **MPA** & **SMA** \\ \hline \(k_{p_{g}}\) & 10 & **10** & 10 & 10 \\ \(k_{i_{x}}\) & 9.67154727244253 & 9.68792081138642 & 9.92750116646622 & 0.0503735259587369 \\ \(k_{d_{4}}\) & 5.55507505212891 & **5.48183670919142** & 5.4719400159824 & 2.97829672909644 \\ \(k_{p_{g}}\) & 10 & 10 & 9.26519695366179 \\ \(k_{ij}\) & 9.94398012231298 & **10** & 10 & 0.027492447132257 \\ \(k_{d_{g}}\) & 4.90915262148882 & **5.11509775200818** & 5.07430677163768 & 7.32731572860309 \\ \(k_{p_{g}}\) & 10 & 10 & 10 & 0.2731572860309 \\ \(k_{i_{i}}\) & 6.24600523295865 & 0 & 9.9847000333801 & 0.501988975857219 \\ \(k_{d_{i}}\) & 10 & 10 & 10 & 6.96510555267074 \\ \(k_{p_{g}}\) & 7.20349062244415 & **2.2435462117906** & 2.6508561094601 & 10 \\ \(k_{i_{g}}\) & 6.2669806605969 & **10** & 6.0368191032762 & 0 \\ \(k_{d_{g}}\) & 5.3959150491106 & **7.15275868300352** & 4.55665073094864 & 8.90239259865836 \\ \(k_{p_{g}}\) & 4.73709682183962 & **4.71756871088933** & 4.71221992462718 & 4.52648291628 \\ \(k_{i_{g}}\) & 0.0326031028012331 & 0 & 1.51704532400754e-07 & 0 \\ \(k_{d_{g}}\) & 0 & 1.92350263205458e-05 & 0 \\ \(k_{p_{g}}\) & 3.88522491995354 & 0.00013406587418094 & 9.85486072057678 & 10 \\ \(k_{i_{g}}\) & 0.191594435931627 & 0.133663724883617 & 9.99867297798012 & 0 \\ \(k_{d_{g}}\) & 3.19550238953637 & **10** & 9.99637332392654 & 5.68443608990096 \\ \hline \end{tabular}
\end{table} 
Table 10: The parameter estimation of metaheuristic algorithms with 100 individuals, 40 evolutions and 30 repetitions. Figure 15: Convergence curves of RMSE and boxplot distribution of the different algorithms, 40 iterations and different individuals. (**a**) 80 individuals, 40 iterations; (**b**) 100 individuals, 40 iterations. Figure 16: Comparison of tracking performance in X-axis of the different algorithms evaluating parameters obtained with 100 individuals. Figure 17: Comparison of tracking performance in Y-axis of the different algorithms evaluating parameters obtained with 100 individuals. Figure 18: Comparison of tracking performance in Z–axis of the different algorithms evaluating parameters obtained with 100 individuals. Figure 19: Comparison of tracking performance in \(\phi\)-angle of the different algorithms evaluating parameters obtained with 100 individuals. Figure 20: Comparison of tracking performance in \(\theta\)–angle of the different algorithms evaluating the parameters obtained with 100 individuals. Figure 23: Tracking performance in three–dimensional space with optimized parameters of PID controller using HGS algorithm with 100 individuals. Figure 24: Tracking performance in three–dimensional space changing desired trajectory. The optimized parameters of PID controller obtained and HGS algorithm with 100 individuals.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
###### Abstract  
Heterogeneous networks (HetNets), which are combined with a macrocell and picocell in the same coverage, are expected to further increase the system capacity in fifth-generation mobile systems and beyond. In HetNets, the cell range expansion (CRE) technique plays an important role and can allow more user equipment (UE) to access the picocell, i.e., virtually expand the picocell coverage. However, conventional CRE techniques that provide a fixed cell selection offset (CSO) for all UE may worsen user throughput if UE is forced to connect to the picocell because the received signal-to-interference plus noise ratio of the UE becomes lower. Therefore, we propose a personal picocell scheme using an adaptive control CRE technique to improve user throughput in which different CSOs are assigned to UE to form each optimal picocell for each UE. In this paper, we first describe the aspects and algorithm of the proposed scheme. Then, we show the user throughput for adaptive control CRE in comparison with conventional CRE by using system-level computer simulations for the two types of HetNets, i.e., single-band and multi-band HetNets. In the simulations, we first clarify the optimal parameters of the adaptive control CRE. We then show the average and 5-percentile user throughput of the optimized adaptive control CRE in comparison with that of conventional CRE. From these results, we confirmed that the personal picocell scheme using the adaptive control CRE can improve the 5-percentile user throughput while maintaining the average user throughput compared with that of conventional CRE.  
mobile communication; heterogeneous networks; cell range expansion; cell selection offset; personal picocell scheme; user throughput +
Footnote †: _J. Sens. Actuator Netw._**2020**, 9, 48; doi:10.3390/jsar9040048
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 2 Literature Review
### Legislation and Policy
The monitor can be moved down-stream for situation assessment in individual feeders;
* If the objective is the harmonics, a monitor should be placed at the terminals of filters or other equipment (selected for investigation);
* The monitor should be installed as close to the investigated symptomatic load as possible. It should be verified that filters, transformers, or other PQ mitigating devices are not connected in between;When measurements are taken in a higher voltage grid, monitoring allocation flexibility is reduced, because measurement transducers are essential (i.e., current and voltage transformers). According to IEEE Std 1159, many instruments are capable up to 600 V RMS direct measurement. The magnitude and phase frequency response of most measurement transformers can significantly distort measurement results since they were originally designed for fundamental grid frequency (only for energy accounting). To conclude, provided ideas highlight the importance of appropriate monitors allocation for correct PQ situation assessment (not only for economic reasons, but also for technical and data processing). * IEEE Std 1159-3-2019 \"IEEE Recommended Practice for PQ Data Interchange Format (PQDIF)\" [4]. The standard defines a unique file format (data packet structure) suitable for exchanging PQ related measurement, thus this is out of this paper scope. * IEEE Std 519 provides limit values for voltage and current harmonics at the PCC; thus, this is out of this paper scope. * IEEE Std 1564 defines different characteristics and indexes associated with voltage sags; thus, this is out of this paper scope. In addition, the review can be supplemented with Smart Grid standards, and especially IEEE Std 2030-2011, the \"IEEE Guide for Smart Grid Interoperability of Energy Technology and Information Technology Operation with the Electric Power Systems (EPS), End-Use Applications, and Loads\" [5]. The document focuses on the Smart Grid as a whole, and its components' interoperability. IEEE Std 2030 can be treated as the best in the world in the field of Smart Grid communications providing a guideline, knowledge base addressing terminology, fundamental principles, characteristics, functional performance, and evaluation criteria for an electric power system with end-use applications and loads. However, in the document, there are almost no mentions of PQ or any description.
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts  
Transition metals from the groups VII to X, especially Co and Ni, have demonstrated good catalytic performance for DRM (Table 1). Due to their high catalytic performance and low cost when compared to noble metals, they have been considered as the way to go on improving DRM towards industrial hydrogen production [15; 16]. However, the resistance of Ni- and Co-based catalysts against sintering and deactivation by coke deposition is much lower when compared to noble metals [18]. In this sense, several studies have been focused on improving synthesis methods, catalyst support and metal-support interactions in order to increase both the activity, hydrogen selectivity and the stability of transition metal-based catalysts [62].
###### Abstract  
To study the influence of dehydration conditions on the pore structure and the mechanical property of expansive soil, two experimental conditions with high-temperature drying (temperature 50 \({}^{\circ}\)C-humidity 10%) and high-temperature humid (temperature 50 \({}^{\circ}\)C-humidity 70%) were carried out. Taking the remolded expansive soil in the province of Anhui in China as the research object, this paper used mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) to analyze the pore size distribution and pore structure of remolded expansive soil samples under different dehydration conditions and duration. After these tests, their mechanical properties were further obtained by uniaxial compression tests. The results showed that the distribution of pore structure of expansive soil was various under different dehydration conditions. Under high temperature and dry environment, the volume of large pores decreased first and then increased with the time of dehydration, while the medium pore volume decreased until the dehydration was stable. Under high temperature and humid environment, the volume of large pore and medium pore both showed a trend of decrease until the dehydration kept stable. The pore volume of expansive soil did not change significantly under the two dehydration conditions. The uniaxial compressive strength (UCS) of remolded expansive soil samples in high temperature and dry environment reached the highest on the 5 th day of dehydration, and then the soil strength decreased slightly until it stabilized. The UCS of remolded expansive soil reached the highest on the 15 th day of dehydration under high temperature and humidity environment, and the soil strength changed little after continual dehydration. These tests showed that the UCS of dehydrated expansive soil samples under the condition of high temperature and humidity is higher than that of dehydrated expansive soil samples under the condition of high temperature and dry environment.  
caparsive soil; dehydration; MIP; pore structure; mechanical strength Article  
Study on Pore Structure and Mechanical Property of Expansive Soil under Different Dehydration Conditions. _Appl. Sci._**2022**, _12_, 5981. [[https://doi.org/10.3390/app1225981](https://doi.org/10.3390/app1225981)]([https://doi.org/10.3390/app1225981](https://doi.org/10.3390/app1225981))
# Regional Traffic Event Detection Using Data Crowdsourcing
## 3 Proposed Regional Traffic Event Detection Technique
### Region Extraction from Event Occurrence  
The importance of traffic events can vary according to the location of the user. Therefore, location detection is important. Extracting the region and place where the event occurred is necessary for location detection. In general, social data provide the location information of users. However, this information is limited to only 2% of all users. For this reason, selecting keywords that allow the region to be known from the text data is necessary. These keywords can then be used to identify administrative districts. This study used an entity name recognition API based on the BERT model to extract keywords indicating regions from the text. The BERT model, a natural language processing model developed by Google, is unlike conventional models in that it can understand the context by learning sentences in both the left and right directions [27]. An entity name recognition API recognizes certain entity names in text and provides semantic information for words representing entities. Table 2 shows the keywords representing regions among the entity name tags [28]. These tags are used to extract region-related keywords from the data. We used an entity name recognition API to recognize and assign entity names to each word in the text data. Then, the words assigned to location-related entity names are extracted. Before the keywords are extracted, location-related entity names are defined, and entity names are assigned to each word. Then, words that include matching entity names are extracted. Figure 4: Example of Categorize Selected Event Types stage. Following the Keyword Extraction stage is the Return Administrative District stage, which returns the administrative districts. If there are several keywords, the administrative district is returned based on the last keyword because Korean addresses are arranged with specific locations listed last. Korean administrative districts consist of one special city, six metropolitan cities, eight islands, one autonomous province, and one autonomous city, totaling 17 administrative districts that are classified into regional and local governments. The administrative subdistricts of regional and local governments are called basic local governments, and they consist of cities and areas referred to by the Korean terms \"gun\" and \"gu.\" Geocoding is used to extract administrative districts based on previously extracted keywords. Geocoding refers to converting locations on the Earth's surface into addresses or coordinates based on unique names. In other words, it generates location information from geographical coordinate information. The input address or location information is
## 4 Experiment and Results
### Ablation Study  
A series of ablation experiments were conducted to explore how to build LFE blocks and how to insert them into a network to achieve the best detection accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline** & _[EMAIL_ADDRESS] & _[EMAIL_ADDRESS]:0:95_ \\ \hline YOLOv7 & 90.3\% & 71.2\% \\ YOLOv7 + two LFE blocks & **90.5\%** & **71.2\%** \\ FA\_SSD & 70.3\% & 67.5\% \\ FA\_SSD + two LFE blocks & **71.1\%** & **68.4\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of the LFE module in other advanced algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pretest Dataset** & **Pretest Task** & \begin{tabular}{c} **Downstream** \\ **Dataset** \\ \end{tabular} & **Baseline** & \begin{tabular}{c} **Training** \\ **Parameters** \\ \end{tabular} & _[EMAIL_ADDRESS] & _[EMAIL_ADDRESS]:0:95_ \\ \hline Random & - & & ResNet18 & \begin{tabular}{c} Epoch = 50 \\ Batch size = 32 \\ Epoch = 50 \\ Batch size = 32 \\ Epoch = 150 \\ Batch size = 64 \\ Epoch = 150 \\ Batch size = 64 \\ Epoch = 150 \\ Batch size = 64 \\ \end{tabular} & 48.2\% & 18.3\% \\ \hline Random & - & & ResNet18 & \begin{tabular}{c} Epoch = 50 \\ Batch size = 32 \\ Epoch = 50 \\ Batch size = 32 \\ Epoch = 150 \\ **63.1\%** \\ \end{tabular} & 48.2\% & 18.3\% \\ \hline Random & - & & ResNet18 & \begin{tabular}{c} Epoch = 50 \\ Batch size = 64 \\ Epoch = 50 \\ Batch size = 32 \\ Epoch = 150 \\ Batch size = 64 \\ \end{tabular} & 80.5\% & 59.2\% \\ \hline Random & **Simdis2x** & & ResNet18 & \begin{tabular}{c} Epoch = 100 \\ Batch size = 64 \\ Epoch = 100 \\ Batch size = 64 \\ Epoch = 50 \\ Batch size = 32 \\ Epoch = 50 \\ Batch size = 32 \\ \end{tabular} & 80.6\% & 59\% \\ \hline Random & - & & ResNet18 & \
\begin{tabular}{c} Epoch = 50 \\ Batch size = 64 \\ Epoch = 100 \\ Batch size = 64 \\ Epoch = 50 \\ Batch size = 32 \\ \end{tabular} & 80.5\% & 59.7\% \\ \hline Random & **Simdis2x** & & ResNet18 &
\begin{tabular}{c} Epoch = 150 \\ Batch size = 64 \\ Epoch = 150 \\ Batch size = 64 \\ Epoch = 100 \\ Batch size = 64 \\ \end{tabular} & 81.5\% & **61.5\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Joint experimental results of improved self-distillation pre-training and location feature extraction structure.