esa-sceva/satcom-chunk-collection huggingface dataset:
# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 2 Related Work
### Alignment for Chinese encyclopedia  
The main method of existing Chinese encyclopedia knowledge base entity alignment is based on the calculation of attribute information similarity. The paper [39; 40] only uses the attribute value of the entity to calculate the similarity of the entity and determine whether the entity can be aligned. The comprehensive index is poor due to the irregularity of attribute names and attribute values in the web encyclopedia.  
In addition, the similarity calculation of contextual information composed of entity abstracts and descriptive texts is also an important method in the alignment of encyclopedia knowledge base entities. On the one hand, it is necessary to use additional text information as a supplement because of the heterogeneity and ambiguity of structured attribute information in different knowledge bases; on the other hand, using a large number of unstructured entity abstracts or descriptive text information in the knowledge base, we can obtain latent semantics and relationships and construct entity features, which can be helpful in further alignment. One way to consider contextual information is to use TF-IDF [39] for text modeling. However, traditional text modeling methods, such as TF-IDF, only consider the characteristics of word frequency, and do not consider the semantic relationship between terms. The LDA topic model can mine the underlying topic semantics of the text unsupervisedly. The model in [41] combined LCS and LDA, and achieved a high accuracy rate. However, the model lacked the granularity of a classification/partition index, and entailed no coarse-to-fine process. The text co-training model [42] use the semi-supervised method to learn the multiple features of entities. The paper [43] improvedthe LDA model, the comprehensive index improved under some datasets. However, it is difficult for a single LDA model to achieve a high accuracy rate on a wider dataset, and the time consumption of LDA similarity calculation and matching is relatively high. WA-Word2 Vec [44] use LTP (language technology platform) to identity the named entities in the text. Then, they deal word vectors and obtain feature vectors by Word2 Vec. The latest method is MED-Doc2 Vec [45]. It used minimum edit distance and the Doc2 vec model to obtain feature vectors containing semantic information. This model obtained comprehensive entity similarity by weighted average to complete the entity-alignment task and achieved better results in the experiment.  
In summary, the entity alignment method currently applied to the Chinese Encyclopedia knowledge base is struggles to achieve a balance between accuracy and time. Therefore, we propose combining the edit distance-based method with the LDA topic modeling method, and apply the coarse-to-fine idea to the unsupervised Chinese encyclopedia entity-alignment task.
###### Abstract  
This paper proposes an Adaptive Robust Control (ARC) strategy for pump-controlled pitch systems in large wind turbines to address challenges in control accuracy and energy efficiency. First, a mathematical model integrating pitch angle dynamics and hydraulic characteristics is established, with pitch angle, pitch angular velocity, and hydraulic cylinder thrust as state variables. Then, an ARC strategy is designed using the backstepping method and incorporating parameter adaptation to handle system nonlinearities and uncertainties. The controller parameters are optimized using Particle Swarm Optimization (PSO) under wind disturbance conditions, and comparative analyses are conducted with traditional PID control. The numerical simulation results show that both controllers achieve similar tracking performance under nominal conditions, with PID achieving a 0.08\({}^{\circ}\) maximum error and ARC showing a 0.1\({}^{\circ}\) maximum error. However, the ARC strategy demonstrates superior robustness under parameter variations, maintaining tracking errors below 0.15\({}^{\circ}\), while the PID error increases to 1.5\({}^{\circ}\). Physical test bench experiments further validate these findings, with ARC showing significantly better performance during cylinder retraction with 0.1\({}^{\circ}\) error compared to PID's 0.7\({}^{\circ}\) error. The proposed control strategy effectively handles both the inherent nonlinearities of the pump-controlled system and external disturbances, providing a practical solution for precise pitch control in large wind turbines while maintaining energy efficiency through the pump-controlled approach.  
pump-controlled; adaptive robust control; wind turbines; pitch angle +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Statistical Analysis  
The normality of the distribution of SampEn values, calculated for all \(m\) values within six different perturbation possibilities, was assessed using the Shapiro-Wilk test. In most cases, the results indicated distributions that were different from normal. A Friedman ANOVA with Dunn-Bonferroni post hoc tests was used to assess the influence of \(m\) levels on the SampEn, calculated for each of the six perturbation possibilities. Subsequently, a similar analysis was performed to identify which of the six perturbation possibilities and which direction exhibited the lowest gait regularity. These last two analyses were conducted independently for each \(m\) level. Statistical analysis was performed using PQStat 2021 software version 1.8.2.238 (PQStat Software, Poznan, Poland). The level of significance was set at \(p\leq 0.05\).
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
###### Abstract
On construction sites, computer vision-based systems, accompanied by other sensor-based systems, such as Radio Frequency Identification (RFID) technology [3], are capable of monitoring progress, quality control, and ensuring safety [4]. In particular, the deployment of digital twins can further improve workforce safety on sites [5]. During the operation and maintenance phases of infrastructure assets, CV, as an efficient and accurate detector, could help engineers to identify defects in existing structures. Such computer vision systems typically contain either a combination of image processing techniques and machine learning algorithms, or deep learning with additional support (e.g., parallel computing). Given the importance of pavement markings in traffic regulation and guidance, and the challenges facing the industry in terms of real-life inspection projects, an effi  
Figure 1: Examples of pavement markings in poor condition. (**a**) Edge line (left side) is worn down and even missing; (**b**) turning arrows and continuity line (right side) are blurred. cient inspection and management scheme is needed. Inspired by the potential of emerging computer vision technology, this study aims to develop an automated inspection method for pavement markings by making full use of the pavement video data readily archived in most road agencies/authorities. Specifically, a vision-based framework is proposed to (1) identify different types of line markings in pavement images by adopting a hybrid descriptor, and (2) estimate the worn condition of identified markings using a threshold-based method. The contributions of this study are two-fold. For one, a vision-based detection method for both line markings and audible markings is proposed. Unlike other work in the literature which has integrated line marking detection into autonomous vehicles [6; 7], this work targets a practically meaningful aim for road authorities, i.e., inventory purposes. Furthermore, condition assessment of line markings is completed in a quantitative way, on top of the detection of blurred markings [8; 9]. Additionally, compared with previous work which has employed 3D laser scanners [10], this work is cost-efficient, and thus, naturally feasible in practice. The rest of the paper is structured as follows: Section 2 summarizes the related work about computer vision in roadway pavement scenarios; Section 3 illustrates the proposed framework for pavement marking detection and condition assessment; Section 4 presents a case study, covering topics ranging from data collection to discussion of the results; Section 5 indicates limitations and potential future work of the framework; finally, Section 6 concludes.
# Improving the Functional Reliability of an Urban Public Transport Line
## 3 Results and Discussion
### Anticipated Occasional Overloads
#### 3.1.2 Numerical Experiment
The average passenger arriving intensity, \(\lambda=V_{d}/\Omega_{\text{sos}}\) being larger than the takeover offer, \(\mu\), \(\lambda>\mu=\frac{C_{d}}{\Omega_{d}}\), means that the number of users waiting for boarding increases according to the relation \((\lambda-\mu)t\), with the maximum value for \(t=\Omega_{\text{sos}}\). The clearance duration of this queue is \((\lambda-\mu)\Omega_{\text{sos}}/\mu\). For \(V_{d}=C_{d}=6000\) passengers and \(\Omega_{\text{sos}}=45\) min, \(\Omega_{d}=60\) min (the period for which the offered capacity was previously calculated) results in \(\lambda\cong\)133 passengers/minute and \(\mu=100\) passengers/minute, which leads to a duration of line clearance of 15 min (an  
Figure 8: The discharge flow and the offered capacity (\(\Omega_{\text{sos}}\)–the period during which spectators arrive in the boarding area with the average intensity \(\lambda=V_{d}/\Omega_{\text{sos}}\) ; \(\tan\alpha=\lambda\); \(\Omega_{d}\)—the period during which the boarding offer is maintained at the average value \(\gamma=C_{d}/\Omega_{d}\) ; \(\tan\beta=\mu\); \((\lambda-\mu)\Omega_{\text{sos}}-\) the flow of users remaining in the waiting lines at the moment \(t_{\text{sos}}\), to be served until \(t_{d}\)). average boarding wait time for the last passengers attached to the line, that is tolerable, in the authors' opinion). Waiting times are even shorter for users attached later in the queue. To prove this statement, consider, for example, the user who arrived at time \(t=15\) min., when the queue length is about 500 passengers. Since the boarding rate is \(\mu=100\) passengers/minute, the waiting time of this user in the queue is 5 min. But according to Equation (20), the estimated waiting time for boarding is \(\omega_{\text{es}}=\lceil\frac{500}{300}\rceil\cdot 4=8\) min. This time was used in the definition of the functional reliability (including for the last passenger of the 500, attached to the queue), and therefore the statement is demonstrated.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 3 Results and Discussion
### Results of the CPP Method  
The CCP method results were obtained from six experiments that implemented the five metrics previously described. Each experiment was conducted by selecting a PV plant and a UAV, then choosing the CPP method and the width, speed, and height of the UAV over the flight path. All of this was done in the implemented GUI interface. The six tests were conducted by varying the CPP width parameter, while other parameters were not varied, and the same experimental conditions were maintained throughout.  
For these experiments two UAVs were selected, the Typhoon [97] and 3 DR Iris [98], as mentioned in Section 2.4. Three simulated PV plants were also chosen, as highlighted in Section 2. The metrics referenced in Section 2.3.1 were assessed for each test. Battery consumption was obtained from the GCS software with the SITL parameter activated. The rest of the data was collected from the Gazebo simulations. The experiments are explained in detail in the following sections, and some results are discussed.  
Figure 8: Steps of boundary extraction by DL server, and OpenCV functions in CSUSL PV plant.  
Figure 6: Steps of boundary extraction by the DL server and OpenCV functions in the Unioeste 1 PV plant.  
Figure 7: Steps of boundary extraction by DL server, and OpenCV functions in Arak PV plant.
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 2 Parameters Characterizing the Capacity of the Multi-Airport System
### Twenty-Four-Hourly Slot Coordination Parameters  
A firebreak is an innovative mechanism based on dynamic capacity management, the core objective of which is to block the cumulative effect of delays across time by structurally configuring peak and valley periods in airport operations, thus realizing a dynamic balance between supply and demand and system stability, as shown in Figure 2. From the theoretical point of view, the essence of the slot firebreak is to construct a peak and valley structure with time-varying capacity parameters by inserting a capacity buffer \"middle period\" between neighboring peak periods (e.g., arrival and departure peaks).  
This design draws on the idea of \"partitioning\" in system control theory: by setting up a dynamic buffer with low-capacity valleys, random delays in the preceding peak hours are absorbed, thus preventing delays from spreading through the queuing effect to the following hours (i.e., the chain reaction of congestion evolution). By quantifying the capacity gradient relationship during \"peak-valley-peak\" hours, a cyclical deviation framework for capacity-demand balance is established such that the airport capacity allocation not only maintains the flexibility to cope with demand fluctuations but also has the systematic resilience to prevent the spread of congestion.  
Figure 1: (**a**) Airport capacity curve. (**b**) Multi-airport system capacity frontier hypersurface.  
In 24 h time coordination parameterization, the value of the slot firebreak stems from its precise intervention in time-dimensioned congestion dynamics. Traditional static capacity parameters are difficult to adapt to cyclical fluctuations in flight demand, while purely dynamic deviation increases the complexity of time management. Through the static peak and valley structure design, the slot firebreak achieves the purpose of providing \"temporary storage space\" for the preceding peak delays with the capacity redundancy in the valley time and quantifies the minimum duration of the buffer time through the delay absorption rate model, thus forming the capacity-demand elasticity matching [19,20]. Through the periodic configuration of the peak and valley capacity parameters, a dynamic deviation similar to the \"respiratory rhythm\" can be formed, such that the system can absorb the delays and gradually restore the steady state at the same time.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion  
To check the performance of the proposed secure and efficient fog-based routing mechanism for IoT networks, it has to be understood that the data must not be intercepted by any hostile nodes as it travels from source to destination. Therefore, to ensure secure data communication for all participating nodes they must not carry out any attacks, such as black hole attacks, flood attacks, sinkhole attacks etc. Energy preservation is also considered the most important factor for any IoT network during all this communication to enhance the network lifespan. For this purpose, the proposed technique implemented the clustering technique, which is to produce clusters aiming to save the energy of a sensor node. Hence, the data received by the intended node must be the same as the sent. This implies that there must not be any intrusion or alteration by any attackers in packets on their way to the destination. The proposed methodology was simulated using the parameters as shown in Table 1. The overall results are compared on the basis of comprehensive trust values, encoding and decoding of packets at three different levels such as from CH to Fog, Fog to Cloud and directly from CH to Cloud in the following sub-sections.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### Software-Defined Radios  
Software-defined radios (SDRs) have enabled implementation and validation of RF systems without specialized hardware or advanced programming skills. Growing support from the open source community continues to support these increasingly accessible and affordable technologies. We briefly summarize the key SDR technologies that enable the advanced features offered by WISCANet.
# A Probabilistic Hyperspectral Imagery Restoration Method
## 3 Revisited Low-Rank based HSI Denoising from Linear Mixed Model Perspective  
In this study, noisy HSI is denoted as \(\mathbf{X}\in\mathbf{R}^{n_{p},\alpha_{t},\alpha_{t}}\). \(n_{r}\), \(n_{c}\), and \(n_{b}\) represent the height, width, and bands of \(\mathbf{X}\), respectively. By vectorizing the image of each band of \(\mathbf{X}\) we obtain a 2D matrix \(\mathbf{X}\in\mathbf{R}^{n_{b}\times n_{p}}\), where \(n_{p}=n_{r}\times n_{c}\) denotes the number of pixels. In \(\mathbf{X}\), each row represents the spectrum of one pixel, while each column denotes the reflectance values of all pixels in one specific spectral band.  
Suppose a noisy image \(\mathbf{X}\) is consisted of a clean/noise-free image \(\mathbf{L}\in\mathbf{R}^{n_{b}\times n_{p}}\) and multiple kinds of noise \(\mathbf{S}\in\mathbf{R}^{n_{b}\times n_{p}}\), i.e.,  
\[\mathbf{X}=\mathbf{L}+\mathbf{S}, \tag{1}\]  
HSI restoration is to recover \(\mathbf{L}\) from \(\mathbf{X}\).  
Due to the low spatial resolution and materials mixture in remote sensing, the spectrum of each pixel is always considered as the mixture of some spectral signatures of pure materials. The spectral signatures of pure materials are termed as endmembers, while the proportion of each endmember for mixing is termed as abundance. Thus, \(\mathbf{L}\) can be decomposed into endmembers with an abundance matrix as  
\[\mathbf{L}=\mathbf{M}\mathbf{H}^{\mathbf{T}}, \tag{2}\]where \(\mathbf{M}\in\mathbb{R}^{n_{p}\times n_{e}}\) is an endmember matrix, \(\mathbf{H}\in\mathbb{R}^{n_{p}\times n_{e}}\) is an abundance matrix and \(n_{e}\) is the number of endmembers. Figure 1 illustrates above representation. Based on the abovementioned issues, we first analyze why HSI denoising can be formulated as a LRMA problem. Then, we analyze how to use the provided prior knowledge LMM to initialize the rank number of LRMA.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Supervised Classifications
#### 2.5.1 Support Vector Machine (SVM)  
SVM is based on structural risk minimization, the basic idea of which is to map multi-dimensional feature into a higher dimensional space and use a hyperplane to separate them linearly with the maximum margin between different classes [35]. SVM has superb performance in dealing with classification problems with a small number of training datasets. It firstly maps training vectors \(x_{i}\) into a higher dimensional space by using kernel function \(\varPhi\) and, hence, finds a linear separating hyperplane with the maximal margin in this higher dimensional space. In this paper, the radial basis function is adopted as the kernel function.
## 3 Research Methods
### Construction of the PSO-BP Neural Network Model
#### 3.2.3 PSO-BP Neural Network Algorithm  
The BP neural network possesses robust nonlinear mapping capabilities. However, its reliance on gradient-descent-based error backpropagation for adjusting network weights and thresholds often results in local optimum solutions, thereby diminishing prediction accuracy. To mitigate this, the PSO-BP neural network integrates the weights and thresholds of the BP neural network into the framework of PSO particles, allowing for iterative optimization of these weights and thresholds by updating the particles' velocities and positions. This integration enhances the BP neural network's convergence speed and prediction accuracy. The methodology is depicted in Figure 1 and progresses through the following stages:  
1. Constructing the PSO-BP neural network, entailing the initialization of network parameters and the random assignment of velocities and positions to the particle swarm.
2. Computing the fitness for all particles, employing a fitness function that calculates the mean squared error between the BP neural network's output and the target values. A lower fitness value signifies reduced error and thus an improved particle position. The fitness function is defined as follows: \[Fitness=\frac{1}{Z}\sum_{s=1}^{Z}\sum_{j=1}^{C}\left(W_{sj}^{d}-W_{sj}\right)^{2}\] (4) where \(Z\) is the number of samples; \(C\) is the number of output network neurons; \(W_{sj}^{d}\) is the ideal value of the JTH output network node of the s sample; and \(W_{sj}\) is the actual value of the JTH output network node of the STH sample. In the PSO-BP neural network, each particle's individual best-known position is denoted as Gbest, while the global best-known position across the entire swarm is represented as Zbest. The process involves comparing each particle's fitness value with its current Gbest. If a particle's fitness is superior to its existing Gbest, this fitness value is then adopted as the new Gbest for that particular particle. Moreover, if any particle's Gbest surpasses the current Zbest of the swarm, this Gbest value is then updated to become the new Zbest.
3. In accordance with Equations (1) and (2), the PSO-BP neural network iteratively updates the velocity and position of each particle, guided by individual and global optimal positions. Upon reaching the maximum number of iterations, or achieving the desired error accuracy, the PSO-determined optimized initial weights and thresholds are integrated into the neural network for predictive analysis. The network configuration is then saved. If these criteria are not met, the process reverts to the preceding step for further adjustments.
4. The final stage involves applying the data from Goodnotes to this trained network model to validate its efficacy and accuracy in a different yet relevant context. The flowchart of the PSO-BP neural network is depicted in Figure 2.  
Figure 2: Flowchart of the PSO-BP neural network.