esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment.  
fuzzy logic control; obstacle avoidance approach; optimal control +
Footnote †: journal: Applied Robotics
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 4 Performance Evaluation
### Performance Evaluation of the Proposed Scheme
#### 4.2.1 Performance Evaluation for Active User Group  
The active user group means a group of users with more check-in data compared to the inactive group, making them more suitable for analysis. A model-based collaborative filtering scheme was employed to aggregate similar users and recommend POIs [19].  
We import and sample the data using the panda package. Subsequently, the data were transformed into a surprise dataset by utilizing the reader class included in the surprise group. The SVD algorithm was employed for model training; it uses the matrix factorization scheme. After the model was trained, its functionality was assessed by utilizing the test dataset. Furthermore, the test_size argument in the train test_split function was adjusted to alter the test data ratio. The test data were 20% of the total data. In general, collaborative filtering exhibits impressive performance when the dataset is dense and large, but it may display subpar performance when the dataset is sparse or small. Figure 7 shows the results of the performance evaluation for a group with a high level of activity. The results confirm that the performance is higher when the number of recommendations is lower.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology
### Proposed Digital Twin Architecture
#### 3.2.4 Visualization and Simulation Module  
This module provides a graphical simulation interface for interaction with the model and equipment in an interactive and immersive environment. It integrates the as-is condition information, including ground, target, and design or as-built voxels, along with the construction equipment, such as an excavator and dumper. The equipment is utilized to simulate a voxel-based earthwork environment according to its semantics, enhancing the situational awareness of construction site information, as well as facilitating scenario analysis and effective decision-making.
###### Abstract
Because the electrical discharge in plasma generation apparatus produces reactive oxygen and nitrogen species (ROS/RNS) such as ozone (O\({}_{3}\)), hydroxyl radical (-OH), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), superoxide (O\({}_{2}\)\({}^{-}\)), nitric oxide (-NO), peroxynitrite (ONOO\({}^{-}\)), and so on, by using air as a carrier gas, tests of CP report a high removal efficiency of non-biodegradable substances [35]. The formation and recombination of radicals are mainly due to their dissociation and ionization during electron collisions [36]. Collisions between electrons and the contents of normal air with humidity can cause many kinds of reactions, as shown in Equations (1)-(3) [37]. The production of hydrogen peroxide through the recombination of hydroxyl radicals can occur in the gas phase or at an interface (Equation (4)) [38; 39]. The reaction in Equation (5) is well known to generate ozone through the collision of oxygen molecules [40]. \[\mathrm{e}^{-}+\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{\cdot}\mathrm{OH}+ \mathrm{\cdot}\mathrm{H}+\mathrm{e}^{-} \tag{1}\]  
\[\mathrm{e}^{-}+\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{H}_{2}+\mathrm{O}^{ +}+2\mathrm{\;e}^{-} \tag{2}\]  
\[\mathrm{e}^{-}+\mathrm{H}_{2}\mathrm{O}\rightarrow\mathrm{H}^{+}\mathrm{ \cdot}\mathrm{H}+\mathrm{\cdot}\mathrm{O}+2\mathrm{\;e}^{-} \tag{3}\]  
\[\mathrm{\cdot}\mathrm{OH}+\mathrm{\cdot}\mathrm{OH}\rightarrow\mathrm{H}_{2} \mathrm{O}_{2} \tag{4}\]  
\[\mathrm{O}+\mathrm{O}_{2}\rightarrow\mathrm{O}_{3} \tag{5}\]  
Produced ROS/RNS move to the liquid phase, accompanying recombination, and subsequently react with target pollutants in the liquid phase; thus, CP requires no additive chemicals or auxiliary equipment due to its in situ generation of ROS/RON [33; 41; 42]. Despite such advantages, however, little research has focused on the removal of residual PFOS in the water phase via aeration-assisted CP. This study determines how rapidly PFOS removal occurs from a kinetics standpoint. Additionally, the mass balance upon PFOS degradation was verified by the analytical determination of SO4\({}^{2}\)- and F- in the suspension. We further demonstrate how PFOS degradation is associated with variations in acute toxicity based on definitive evidence. We also confirm the potential of aeration-assisted CP through the comparison of its electrical energy per order (EE/O) and hydroxyl radical production with that of other AOPs.
## 2 The Principle of the SRGB Support Structure  
The SRGB support structure is composed of a systematic bolt, shotcrete and spatial reticulated grid, as shown in Figure 1. Among them, the systematic bolt and shotcrete belong to the category of flexible support, allowing moderate deformation of surrounding rock. As a new supporting structure, the spatial reticulated grid is based on high-strength seamless steel tubes as the main force-bearing parts, and threaded steel bars as the connecting webs. The full-section enclosed support structure, connected by bolted end-plates, adopts the continuous erection method, showing the stress characteristics of surface bearing.  
The SRGB support structure takes into account the characteristics of flexible support and rigid support. It gives full play to the advantages of the bolt and shotcrete, and the bolt anchored into the rock mass is integrated with the surrounding rock, so as to improve the stress state and the stability of the surrounding rock. After the tunnel is excavated, the surrounding rock should be sealed by spraying concrete over time, so that the surrounding rock is in a state of three-way stress, forming a combined structure to coordinately bear the load. The spatial reticulated grid draws on the structural form of the large-span grid structure on the ground, and combines the implementation environment of tunnels and underground projects to make a special support structure. This structure has the characteristics of high rigidity, high strength and good stability, and can be used to bear strong loosening pressure. The above-mentioned systematic bolt, spray layer and spatial reticulated grid together constitute the initial support system. The bolt shotcrete support is applied over time in the early stage, which has the effect of quickly sealing the surrounding rock and improving the stress state of the surrounding rock, and then erecting the high-strength arches and re-spraying concrete are carried out to form a permanent lining structure. This structure gives full play to the performance advantages of high-strength steel tubes and concrete materials, and reflects the support concept that the initial support should form strong support in time and bear the surrounding rock pressure in real time.  
In tunnel engineering, the support scheme should be reasonably determined according to the different surrounding rock conditions and surrounding environment. For the challenge of strict deformation control in urban complex environment areas, the characteristics of coordinated deformation of bolt shotcrete flexible support and surrounding rock should  
Figure 1: The SRGB support structure. (**a**) The components of the support system and (**b**) sectional structure feature.  
be brought into play, and the basic load of the surrounding rock could be borne in real time, with the help of a high-strength and high-rigidity spatial reticulated grid structure to meet the bearing capacity and stability requirements of the tunnel.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 2 Related Works
### Semantic Representation in Natural Language Processing  
Recent advancements in NLP, primarily driven by large pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) [23], Generative Pre-trained Transformer (GPT) [24], and their successors, have substantially enhanced the capability to process and generate human language. These models are remarkably proficient in understanding context, generating coherent text, and performing a variety of language tasks, establishing them as pivotal in semantic communication. Techniques such as Word2 Vec [25], GloVe [26], and FastText [29] transform words into continuous vector spaces, wherein semantically similar words are positioned closer together. These embeddings have revolutionized numerous NLP tasks by effectively capturing semantic meaning. However, traditional word embeddings lack the ability to capture syntactic information. In contrast, models like BERT and Embeddings from Language Models (ELMo) generate dynamic embeddings for words based on their context within sentences, thereby enhancing the capture of polysemy and contextual nuances. Semantic parsing, which involves converting natural language into formal representations such as logical forms or knowledge graphs, is crucial for tasks like question-answering and information extraction [27]. Ontologies like WordNet [28] and knowledge graphs such as Google's Knowledge Graph [36] organize information into interconnected entities and relationships, offering a rich semantic framework that underpins various NLP applications. Advanced architectures, notably Transformers, have dramatically bolstered models' abilities to understand and generate language. These architectures leverage large-scale pre-training and fine-tuning on specific tasks, achieving state-of-the-art performance across numerous benchmarks.
###### Abstract  
In remote areas, wireless multimedia sensor networks (WMSNs) have limited energy, and the data processing of wildlife monitoring images always suffers from energy consumption limitations. Generally, only part of each wildlife image is valuable. Therefore, the above mentioned issue could be avoided by transmitting the target area. Inspired by this transport strategy, in this paper, we propose an image extraction method with a low computational complexity, which can be adapted to extract the target area (i.e., the animal) and its background area according to the characteristics of the image pixels. Specifically, we first reconstruct a color space model via a CIELUV (LUV) color space framework to extract the color parameters. Next, according to the importance of the Hermite polynomial, a Hermite filter is utilized to extract the texture features, which ensures the accuracy of the split extraction of wildlife images. Then, an adaptive mean-shift algorithm is introduced to cluster texture features and color space information, realizing the extraction of the foreground area in the monitoring image. To verify the performance of the algorithm, a demonstration of the extraction of field-captured wildlife images is presented. Further, we conduct a comparative experiment with N-cuts (N-cuts), the existing aggregating super-pixels (SAS) algorithm, and the histogram contrast saliency detection (HCS) algorithm. A comparison of the results shows that the proposed algorithm for monitoring image target area extraction increased the average pixel accuracy by 11.25%, 5.46%, and 10.39%, respectively; improved the relative limit measurement accuracy by 1.83%, 5.28%, and 12.05%, respectively; and increased the average mean intersection over the union by 7.09%, 14.96%, and 19.14%, respectively.  
wireless multimedia sensor networks; wildlife monitoring image; extraction; Hermite; adaptive mean-shift
# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 3 Coherence Factors Values Estimation  
Analyzing the case that assumes the existence of only two error signals \(e_{a}(t)\) and \(e_{b}(t)\) with expanded uncertainties \(U_{a}\) and \(U_{b}\), the Equation (7) takes the form:  
\[U_{a,b}=\sqrt{\left[\begin{array}{c}U_{a}\\ U_{b}\end{array}\right]^{T}\left[\begin{array}{cc}1&h_{a,b}\\ h_{b,a}&1\end{array}\right]\left[\begin{array}{c}U_{a}\\ U_{b}\end{array}\right]}=\sqrt{U_{a}^{2}+U_{b}^{2}+2U_{a}U_{b}h_{a,b}}, \tag{8}\]and for the known resultant value of the expanded uncertainty \(U_{a,b}\), resulting from the combination of the uncertainties \(U_{a}\) and \(U_{b}\), the value of the coherence coefficient \(h_{a,b}=h_{b,a}\) can, therefore, be determined according to the following equation:  
\[h_{a,b}=h_{b,a}=\frac{U_{a,b}^{2}-U_{a}^{2}-U_{b}^{2}}{2U_{a}U_{b}}. \tag{9}\]  
Determining the coefficient value \(h_{a,b}\) in the analyzed scenario involves conducting an experiment using the Monte Carlo method. This process entails iteratively summing the implementation values of the studied error signals, expressed as \(\hat{e}_{a,b}(t)=\hat{e}_{a}(t)+\hat{e}_{b}(t)\), and subsequently calculating the resulting expanded uncertainty \(U_{a,b}\) following Equation (4) [1,9]. Alternatively, analytical methods requiring convolution operations can be employed to determine the coherence coefficient value, as outlined in [13]. Notably, the coherence coefficient value obtained is only valid for a signal pair with parameters identical to those of the \(e_{a}(t)\) and \(e_{b}(t)\) signals, rendering this method impractical in real-world applications.  
The essence of employing the interval arithmetic reduction method is to offer a way to ascertain the coherence coefficient values for any number and parameters of error signals without the need for repetitive Monte Carlo simulations or analytical derivations. The approach involves estimating these coefficients appropriately, a process elucidated in subsequent sections of the study. Given that practical measurement scenarios often lack precise realization values and probability density function insights for the identified error signals, assumptions are made in this paper regarding the unknown nature of these parameters.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 1 Introduction
### Related Work  
For performance and efficiency reasons, most GNSS receivers are implemented in hardware rather than software. GNSS algorithm benchmarks can benefit from taking this into account as hardware implementations permit estimating additional KPIs and, thus, enhancing comparability. While it would be unreasonably time-consuming and costly to produce ASICs for this purpose alone, FPGAs provide a suitable alternative platform given their inherent low-effort reconfiguration characteristics.  
The PYNQ workflow, designed by AMD (Santa Clara, CA, USA), for hardware-software co-design enables Python-based interaction with computationally heavy _kernels_ executed on the embedded FPGA in AMD Zynq chips [12]. This workflow has rapidly grown popular in several research fields, most prominently in the ML domain whose algorithms conform well to its dataflow processing style. These algorithms can benefit significantly from the PYNQ workflow's scalability and native support for custom low-precision number formats [13]. These features allow for speeding up exploration of the design space of convolutional neural networks [14] and increasing accelerator throughput with AxC techniques [15].  
Following the recent launch of the RFSoC development platform [16], the PYNQ workflow is also increasingly being used in the communications domain for, e.g., accelerating an SDR's Fast Fourier Transform (FFT) and signal modulation operations. Thisis illustrated by [PERSON] et al., whose open-source SDR permits software control of radio functionality and real-time radio signal visualizations [17]; [PERSON] et al., who utilize the FPGA to implement high-speed packet processing for networking [18]; and [PERSON] and [PERSON], who propose a PYNQ-enabled mmWave-capable 5G transceiver [19]. These works utilize the PYNQ workflow to simplify the development of proof-of-concept systems with user-friendly interfaces. Our goals align well with this trend.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Managing the Loss of Multiple Hundreds of Containers  
Past accidents demonstrate that containers can become lost in big numbers all at once. If all these containers start sending out messages in an unorganized way, the capacity of the used frequency spectrum can easily be exceeded. This is especially critical when using a safety-related system, such as AIS, to transmit messages. Therefore, the tracking units need to organize themselves after the accident. This can be achieved by integrating the activated tracking units into a mobile ad hoc network and sending out the accident information in compressed messages by a designated unit. An overview of the means of communication proposed in Sections 4.2-4.5 is given in Figure 3.
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
## 3 Spacecraft Dynamics and Trajectory Optimization
In particular, the value of \(-t_{f}\) coincides with the scalar performance index \(J\) to be maximized during the interplanetary transfer, that is, \(J\triangleq-t_{f}\).
# Convergence and Stability of a New Parametric Class of Iterative Processes for Nonlinear Systems
## 2 Family ACTV for Nonlinear Systems
\]  
For \(z^{(k)}\), we calculate \(\Big{[}x^{(k)},y^{(k)};F\Big{]}\) up to order six using the Genochi-Hermite formula seen in (10), obtaining  
\[ \begin{split}=& F^{\prime}(x)\Big{[}I+C_{2}e^{(k)}+ \Big{(}C_{2}^{2}+C_{3}\Big{)}e^{(k)^{2}}+\Big{(}C_{4}+C_{3}C_{2}+2C_{2}C_{3}-2 C_{2}^{3}\Big{)}e^{(k)^{3}}\\ &+\Big{(}C_{5}+C_{4}C_{2}+3C_{2}C_{4}+2C_{3}^{2}-3C_{2}C_{3}C_{2 }-4C_{2}^{2}C_{3}-C_{3}C_{2}^{2}+4C_{2}^{4}\Big{)}e^{(k)^{4}}\\ &+\Big{(}C_{6}-8C_{2}^{5}+6C_{2}C_{3}C_{2}^{2}+8C_{3}^{2}C_{3}+6C _{2}^{2}C_{3}C_{2}-C_{4}C_{2}^{2}-6C_{2}^{2}C_{4}-4C_{2}C_{4}\\ & C_{2}-6C_{2}C_{3}^{2}-C_{3}^{2}C_{2}-2C_{3}C_{2}C_{3}+2C_{4}C_{3 }+3C_{3}C_{4}+C_{5}C_{2}+4C_{2}C_{5}\Big{)}e^{(k)^{5}}\\ &+\Big{(}C_{7}+16C_{2}^{6}+9C_{2}C_{3}^{2}C_{2}+12C_{2}C_{3}C_{2 }C_{3}+12C_{2}^{2}C_{3}^{2}-C_{3}C_{2}C_{3}C_{2}-C_{3}^{2}C_{2}^{2}\\ &-12C_{2}C_{3}C_{3}^{2}-12C_{2}^{2}C_{3}C_{2}^{2}-12C_{3}^{2}C_{3 }C_{2}-16C_{2}^{4}C_{3}+4C_{3}C_{4}^{4}+8C_{2}C_{4}C_{2}^{2}\\ &+8C_{2}^{2}C_{4}C_{2}+12C_{2}^{3}C_{4}+C_{4}C_{3}^{2}-8C_{2}C_{4 }C_{3}-9C_{2}C_{3}C_{4}-3C_{3}C_{2}C_{4}-C_{3}C_{4}C_{2}\\ &-C_{4}C_{3}C_{2}-2C_{4}C_{2}C_{3}+5C_{2}C_{6}+C_{6}C_{2}-2C_{3}^ {3}+4C_{3}C_{5}+2C_{5}C_{3}+3C_{4}^{2}\\ &-5C_{2}C_{5}C_{2}-8C_{2}^{2}C_{5}-C_{5}C_{2}^{2}\Big{)}e^{(k)^{6} }\Big{]}+O\Big{(}e^{(k)^{7}}\Big{)}.\end{split}  \tag{16}\]  
Following a similar procedure to the one used in (14) and (15), we have  
\[ \begin{split}&\Big{(}2\Big{[}x^{(k)},y^{(k)};F\Big{]}-F^{\prime} \Big{(}x^{(k)}\Big{)}\Big{)}^{-1}=\Big{[}I+\Big{(}C_{3}-2C_{2}^{2}\Big{)}e^{(k )^{2}}+\Big{(}2C_{4}-2C_{3}C_{2}-4C_{2}C_{3}+4C_{2}^{3}\Big{)}e^{(k)^{3}}\\ &+\Big{(}-4C_{2}^{4}+6C_{2}^{2}C_{3}+6C_{2}C_{3}C_{2}-6C_{2}C_{4 }-2C_{4}C_{2}+3C_{5}-3C_{3}^{2}\Big{)}e^{(k)^{4}}+\Big{(}-2C_{4}C_{2}^{2}+8C_{2 }^{2}C_{4}\\ &+8C_{2}C_{4}C_{2}-2C_{4}C_{3}-4C_{3}C_{4}+8C_{3}C_{3}^{2}-4C_{ 2}C_{3}C_{2}^{2}-8C_{2}^{2}C_{3}C_{2}-4C_{3}^{2}C_{3}-2C_{5}C_{2}-8C_{2}C_{5} \\ &-2C_{3}C_{2}C_{3}+8C_{2}C_{3}^{2}+4C_{6}\Big{)}e^{(k)^{5}}+ \Big{(}8C_{2}^{6}-4C_{2}^{4}C_{3}+8C_{2}^{2}C_{3}C_{2}^{2}-4C_{2}C_{3}C_{2}^{ 3}-24C_{3}C_{2}^{4}+4C_{2}^{2}C_{3}C_{2}\\ &-10C_{2}^{2}C_{3}^{2}-2C_{2}C_{3}C_{2}C_{3}+10C_{3}^{2}C_{2}^{2}+1 2C_{3}C_{2}C_{3}C_{2}+16C_{3}C_{2}^{2}C_{3}-10C_{2}C_{3}^{2}C_{2}+10C_{2}C_{4} C_{3}-6C_{4}\\ & C_{2}C_{3}-2C_{4}C_{3}C_{2}-4C_{3}C_{2}C_{4}+10C_{2}C_{3}C_{4}-4C _{2}C_{4}C_{2}^{2}+10C_{4}C_{2}^{3}-4C_{3}^{2}C_{4}-12C_{2}^{2}C_{4}C_{2}-C_{5} C_{3}\\ &-5C_{3}C_{5}-10C_{2}C_{6}-2C_{6}C_{2}-3C_{3}^{3}+10C_{2}C_{5}C_{2} +10C_{2}^{2}C_{5}-4C_{5}C_{2}^{2}-2C_{4}^{2}\Big{)}e^{(k)^{6}}\Big{]}+O \Big{(}e^{(k)^{7}}\Big{)}.\end{split}  \tag{17}\]  
Now, we obtain the expansion of \(F\Big{(}y^{(k)}\Big{)}\),  
\[ \begin{split}\Big{(}y^{(k)}-\xi\Big{)}^{2}=& C_{2}^{2}e^{(k)^{4}}+\Big{(}-4C_{2}^{3}+2C_{2}C_{3}+2C_{3}C_{2} \Big{)}e^{(k)^{5}}+\Big{(}12C_{2}^{4}-11C_{2}^{2}C_{3}+4C_{3}^{2}+3C_{2}C_{4} \\ &-4C_{2}C_{3}C_{2}+3C_{4}C_{2}-7C_{3}C_{2}^{2}\Big{)}e^{(k)^{6}}+O \Big{(}e^{(k)^{7}}\Big{)},\\ \Big{(}y^{(k)}-\xi\Big{)}^{3}=& C_{2}^{3}e^{(k)^{6}}+O\Big{(}e^{(k)^{7}}\Big{)},\end{split}
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 1 Introduction
### Challenges in Vibration Monitoring  
Based on related literature, the following challenges in the monitoring of blade vibrations can be summarised:  
* Sensors should be deployed along the full length of the blade to capture local changes in the vibrational response [14]. In particular, monitoring of the blade tip is desirable since it experiences largest aerodynamic forces [22].
* Active excitation of the blade is preferable but mounting of actuators is difficult, especially in retrofit of existing turbines [15].
* Eigenfrequencies can be monitored in a global approach, but a local approach is needed to detect local changes due to damage in the vibrational characteristics [16].
* Reference measurements under damaged conditions are unavailable; hence, analysis methods cannot be based on labelled reference data [21].
* The vibrational characteristic of the blade varies with environmental and operational conditions; hence, those conditions need to be taken into account when analysing vibrations [11; 15].
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 6 Experiment Analysis
### Time Consumption in the Data Collection Stage and the Data Obtainment Stage  
In this experiment, our PCAC scheme was compared with the RAAC when the DSs uploaded the data and Us requested the data. When the DSs uploaded the data, our PCAC scheme counted the Encrypt and Act_Audit phase, while RAAC counted the Encrypt phase. When requesting the data, PCAC counted the Act_Audit, the Gen_key, and the Decrypt phase, while RAAC counted the Gen_key, the Decrypt, and the Audit phase. As the Init phase was executed once the system was running, it has not been counted here. The RAAC scheme does not take the generation of the access structure into consideration, so the performance of Gen_Stru phase was not analyzed here. Instead, the time consumption of the Gen_Stru phase was counted alone, and it was found to be at least 2 ms. This experiment ignored the influence of network communication. The results are shown in Figures 5 and 6.  
As can be seen from the comparison, the time consumption of our PCAC scheme increased with the increase of the number of secret-sharing parties (the number of KSs), in both stages. The number of secret-sharing parties was determined by the row number of the sharing matrix, and the row number of the matrix determined the number of \(C_{i}\) in \(CT\). Therefore, the time consumption of the PCAC was proportional to the number of KSs. As can be seen from Figure 6, in the data obtainment stage, the time consumption in the PCAC was almost half of that in the RAAC.