esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting the Learning for Ranking Patterns
## 8 Conclusions  
In this paper, we proposed a novel framework called AHPRank for learning pattern ranking functions using the analytic hierarchy process (AHP) multi-criteria decision-making method. Our algorithm can operate in both passive and active learning modes, allowing users to rank subsets of data points according to their preferences. We showed that the learned weights can be used to aggregate all measures into a single ranking function. The latter was demonstrated to closely match the user's ranking preferences through experiments and statistical analysis.  
We applied our framework to the association rules mining case study and compared it with state-of-the-art learning methods. Our experimental results showed that AHPRank can efficiently learn the ranking function and outperform existing approaches in terms of ranking accuracy and running time. Furthermore, we showed that AHPRank can help users effectively prioritize and analyze patterns, leading to better decision making.  
Our proposed framework has a wide range of potential applications across various domains, including marketing in e-commerce, finance, healthcare, and social network analysis. In e-commerce, for example, AHPRank can be used to recommend products to customers based on their interests, leading to more personalized and effective marketing strategies. In finance, AHPRank can assist investors in making informed decisions about where to allocate their funds by ranking investments based on their risk and return profiles. In healthcare, AHPRank can predict the most effective treatment for a given patient based on their individual characteristics, improving patient outcomes and reducing healthcare costs. In social network analysis, AHPRank can be used to rank posts based on their relevance or importance, helping users to more easily navigate and engage with social media platforms. By leveraging the AHPRank algorithm, users can benefit from more accurate and timely ranking patterns, ultimately enhancing their ability to make informed decisions based on the available data.  
Future work could include exploring the use of AHPRank in combination with other machine learning techniques or developing an interactive system that can learn and update user preferences over time. In summary, our proposed framework offers a practical and efficient solution for learning pattern ranking functions, which can lead to better decision making in various applications.  
Conceptualization, methodology, software, validation, formal analysis, data curation, writing and editin: All authors. All authors have read and agreed to the published version of the manuscript.  
This work is carried out as part of the AI4 CCAM Horizon Europe project, funded by the European Union under Grant Agreement 101076911.  
The authors declare no conflict of interest.
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Chemical Reactions Occurring during the Hydration Process  
A series of separate/independent (parallel and/or sequential) reactions involving water molecules and the principal mineral phases forming the cement led to the hydration of OPC. In general, the hydration process is strongly driven by the starting chemical composition, the dimensions and size (e.g., specific surface areas, roughness, and particle size distribution), the quantity of water added, the water-to-cement ratio (\(w/c\)), the curing temperature, and the presence of additives [51]. In order to simplify the comprehension of the process, the following hydration reactions are discussed separately by considering minerals individually. Figure 2 reports the principal hydrated products in OPC [52].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compounds** & **Chemical Formula** & **Bogue Formula** & **Amount (wt.\%)** \\ \hline Alite, or Tricalcium silicate & Ca\({}_{3}\)SiO\({}_{5}\) & C\({}_{3}\)S & 30–50 \\  & [3 CaO \(\cdot\) SiO\({}_{2}\)] & C\({}_{2}\)S & 20–45 \\  & Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\) & C\({}_{3}\)A & 8–12 \\  & [3 CaO \(\cdot\) Al\({}_{2}\)O\({}_{3}\)] & C\({}_{4}\)AF & 6–10 \\
## 5 Experimental Validation
### Case 2: Rotor Oil-Whirl Fault Detection  
Figure 19a,b shows the waveform and FFT spectrum of the rotor oil-whirl signal, respectively. Figure 19a shows that the rotor oil-whirl signal is represented by a sum of sine waves, and that in the FFT spectrum there is a prominent amplitude at the rotating frequency \(f_{2}\) of 43.33 Hz. Besides, the amplitude of the oil-whirl frequency of 21.5 Hz (equal to about 0.5 \(f_{2}\)) is greater than that of the rotating frequency, but the amplitude of the high frequency is not obvious.  
Figure 19: The analyzed results of the rotor oil-whirl signal: **(a)** Temporal waveform, **(b)** FFT spectrum, time-frequency representations obtained by **(c)** IAVMD, **(d)** OVMD, **(e)** LMD, **(f)** EMD, and **(g)** WT.  
The oil-whirl signal is analyzed by five methods (i.e., IAVMD, OVMD, LMD, EMD, and WT), respectively. Figure 19c-g provides the TFR derived from the five methods, respectively. In IAVMD, the optimal parameters obtained by the GWO algorithm are \(\hat{\alpha}\) = 1919 and \(K\) = 4, respectively. In OVMD, the optimal parameters are set as \(\hat{\alpha}\) = 952 and \(K\) = 4. As shown in Figure 19c, the rotating frequency 43.33 Hz and its harmonics 86.67 Hz can be found. Besides, the oil-whirl frequency 21.5 Hz and its related frequency 65 Hz (approximately equal to the sum of the oil-whirl frequency 21.5 Hz and the rotating frequency 43.33 Hz) is also extracted clearly, which means that the rotor shows an oil-whirl fault. It can be seen from Figure 19d that only three frequency components (i.e., 21.5 Hz, 43.33 Hz, and 86.67 Hz) are visible due to the overlap of frequency components, their related frequency 65 Hz cannot be found, and the end effect is obvious compared with the proposed method. In Figure 19e,f, the oil-whirl frequency 21.5 Hz and rotating frequency 43.33 Hz can be seen, but they show the end effect and mode mixing problem. Furthermore, their time-frequency trajectory is not as good as those in Figure 19c. Likewise, as seen in Figure 19g, the fault feature information of the rotor oil-whirl is not obvious, and the points on the frequency components are discrete and remarkably decentralized. The contrastive analysis further confirms that the performance of the proposed IAVMD in detecting rotor oil-whirl is better than that of the other four methods (i.e., OVMD, LMD, EMD, and WT).
# An Architecture for Biometric Electronic Identification Document System Based on Blockchain +
## 3 Authentication Methods
### User Authentication
#### 3.2.9 Barcode  
A barcode allows a faster capture of data, the immediate integration of the decoded data in the system and low-cost printing. There are two main different kind of barcodes: one dimensional (Figure 1a) and two dimensional ones (Figure 1b,c). One dimensional barcode allows us to store a limited amount of information and it consists of a group of bars and spaces that are designed to be scanned and read on a computer. However, the main problem is that it has a small storage capacity that ranges between 20 and 30 digits. Among them can be mentioned Ean 13, Ean 8, Code 128, Dun 14,Upc 39. On the other hand, barcode labels have a control digit that prevents the scanning tool from losing data and data that does not pass the setpoint digit test will not be entered into the system [43]. Two-dimensional barcodes allow to store more information than one-dimensional barcode, for example, the PDF-417 can store a maximum of 1800 alphanumeric characters (ASCII) or 1100 binary codes for each symbol (each rectangle in the form of a \"point cloud\"). Another two-dimensional barcode is the quick response (QR) code, approved as ISO international standard (ISO/IEC18004) and It uses four standardized encoding modes: numeric with a data capacity of 7089 characters, alphanumeric with 4296 characters, byte/binary with 2953 bytes and kanji (Japanese writing) with 1817 characters [44]. In security terms, a QR Code could be manipulated to change the encoded information and it is possible to carry out SQL injection, command injection phishing and pharming attacks, reader software and social engineering attacks [45].
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
###### Abstract  
The presence of obstacles like a tree, buildings, or birds along the path of a drone has the ability to endanger and harm the UAV's flight mission. Avoiding obstacles is one of the critical challenging keys to successfully achieve a UAV's mission. The path planning needs to be adapted to make intelligent and accurate avoidance online and in time. In this paper, we propose an energy-aware grid based solution for obstacle avoidance (EAOA). Our work is based on two phases: in the first one, a trajectory path is generated offline using the area top-view. The second phase depends on the path obtained in the first phase. A camera captures a frontal view of the scene that contains the obstacle, then the algorithm determines the new position where the drone has to move to, in order to bypass the obstacle. In this paper, the obstacles are static. The results show a gain in energy and completion time using 3D scene information compared to 2D scene information.  
coverage path planning; obstacle avoidance; unmanned aerial vehicle; energy consumption; completion time +
Footnote †: journal: Future Internet
## 2 Related Work
For 3D point clouds registration, iterative closest point (ICP) [48] is a well-known algorithm that finds correspondences between target and source point clouds, calculates rotation and translation based on corresponding points, and iteratively refines the alignment. Generalized iterative closest point (G-ICP) [49] is an algorithm that combines ICP and point-to-plane ICP. G-ICP uses point correspondences and also incorporates the normal vectors of planes from the target and source point clouds. However, existing registration methods using point cloud descriptors lack a comprehensive consideration of semantic information for each point or feature descriptors. In this paper, we propose a method that considers semantic information to find overlapped region and effectively merges multiple local semantic maps.
## 3 Experimental Design and Implementation
### Experimental Data
#### 3.1.2 Spatial Coverage and Road Network Structure  
The road network covered by the experimental data contains major intersections, forming a typical urban road network structure with the following characteristics:  
1. Grid layout: Intersections exhibit a regular grid distribution, conforming to modern urban planning characteristics.
2. Clear hierarchy: There are distinct arterial roads (such as the arterial formed by intersections 1-8) and secondary roads.
3. Clear boundaries: The road network has explicit spatial boundaries, facilitating OD analysis.
4. Intersection coordinate data is provided in text file format, containing each intersection's number and precise geographic coordinates. This coordinate information forms the foundation for spatial analysis and path identification.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 4 Results
### Data Processing Results  
The ideal data processing method is as follows: the pile strain data is decomposed into two layers by using db5 wavelet function. The high frequency coefficients at all levels are processed by stein-based unbiased risk threshold rigrsure method and reconstructed with the second-order low frequency signal, it achieves the effect of denoising for the original signal. The denoised signal is smoothed by the translation mean value method, and the strain variation trend of the pile can reflect the vertical load. The strain of pile under vertical load of 1094 kN, 1641 kN and 2188 kN are obtained by BOTDR distributed optical fiber sensing technology. As a typical example, the db5 wavelet basis function for the noise data is used, the rigrsure Stein's unbiased risk threshold method based on two layers of the quantized treatment is applied, and the high frequency signal at all levels is decomposed. Thus, the results by secondary low frequency signal reconstruction and translation mean method are obtained. The final strain curves are shown in Figure 8.  
Figure 8: Final strain curves under three loads.  
Figure 7: Percentile-filter smoothing signal.  
According to the analysis of Figures 4-8, the main trend of strain for bored pile is gradually decreasing from top to bottom, the data is fluctuation due to the interference in the range of 1-2 m of the head of pile. There are three peaks at the pile length of 13 m, 27 m, and 32 m which may be caused by many factors such as pile defects, soft soil, and water. The data process method used in this paper can reduce this peak effect.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 3 Quantitative Evaluation Method for Aircraft Collision Risk
### Calculation of Aircraft Collision Risk Based on Potential Movement Space  
In this paper, the taxiing motion of an aircraft from the starting point to the endpoint is modeled as Brownian motion. Given the constraints on the aircraft's position discussed earlier, the resulting taxiing motion is a bounded Brownian motion. The standard Brownian motion conforms to the following equation:  
\[x(t)\sim N\Big{(}\mu_{x}(t),\sigma_{x}^{2}(t)\Big{)} \tag{11}\]  
\[\mu_{x}(t)=\frac{(t-t_{i})x_{j}+(t_{j}-t)x_{i}}{t_{j}-t_{i}} \tag{12}\]  
\[\sigma_{x}^{2}(t)=\frac{(t-t_{i})(t_{j}-t)}{t_{j}-t_{i}} \tag{13}\]when considering the upper and lower bounds of the aircraft's movement, the corresponding equation needs to be modified accordingly:  
\[h(x)=\begin{cases}\frac{f(x)}{F(U_{x}(t))-F(L_{x}(t))}&L_{x}(t)\leq x\leq U_{x}(t )\\ 0&x<L_{x}(t)\text{ or }x>U_{x}(t)\end{cases} \tag{14}\]  
\[{\sigma^{\prime}}^{2}=(V_{m}-\overline{V})^{2}\times(t-t_{i})(t_{j}-t) \tag{15}\]  
where \(f(x)\) represents the original probability density function; \(h(x)\) is the modified probability density function; \(F(x)\) is the cumulative distribution function; and \({\sigma^{\prime}}^{2}\) is the modified variance. From this, the probability of the aircraft reaching different positions at various times can be obtained. The probability of the aircraft reaching each place in the Petri net is determined by summing the probabilities of all positions within that place.  
\[n=Ceiling(\frac{k}{l}) \tag{16}\]  
\[Pro(p_{i})=\sum_{i=1}^{k}h(x_{i}) \tag{17}\]  
where \(k\) is the length of the place, \(l\) is the length of the aircraft, and \(n\) is the number of aircraft that can be accommodated within the place's length (rounded up to the nearest integer). The arrival probabilities of the corresponding positions within the place are then summed according to this number. The collision risk for a place is calculated by multiplying the arrival probabilities of two aircraft corresponding to the same place. If the arrival probability of an aircraft at a given place is zero, it is excluded from the calculation, as it is assumed that the aircraft poses no collision risk within that place.  
\[Risk=\prod_{i=1}^{n}Pro(p_{i}),Pro(p_{i})\
eq 0 \tag{18}\]  
Additionally, if multiple aircraft have a non-zero arrival probability within the current area, the collision risk for that area is calculated as follows:  
\[Risk_{total}=\sum_{i=1}^{q-1}\sum_{j=i+1}^{q}Risk_{ij} \tag{19}\]  
where \(q\) represents the number of aircraft with a non-zero arrival probability in the place. The overall collision risk within the place is obtained by summing the collision risks of all pairs of aircraft. This allows for the calculation of aircraft collision risks in different areas at various times.
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 3 An Active Noise Control System with Dynamically Adapted Reconstruction and Anti-Aliasing Filters  
This study proposes a method for the dynamic matching of reconstruction and anti-aliasing filters with fixed parameters in an ANC system for primary sound sources with different spectral structures. The designed filters have minimal system group delay, while the aliasing noise is suppressed to meet the dynamic range requirement of \(x(n)\) and \(e(n)\), as shown in Figure 1, and the mirror noise is reduced to less than a preset level in comparison with the original low frequency of \(u(n)\). A schematic diagram of this method is shown in Figure 2.  
On the basis of a traditional multi-rate ANC system, a dynamic matching algorithm is added to the ANC process, which is responsible for acquiring \(x_{h}(n)\), \(u_{h}(n)\), and \(e_{h}(n)\) at specific time intervals. The intervals can be preset before the control starts; for example,  
Figure 2: Schematic diagram of the proposed method including dynamic matching algorithm.  
they can be set to 1000 times the period of the low sampling rate or adjusted in the control state. The mirror noise portion of \(u_{h}(n)\) and the aliasing noise portion of \(x_{h}(n)\) and \(e_{h}(n)\) are estimated in the regions with the high sampling rate, and then appropriate values of \(T_{xd}(z)\), \(T_{ud}(z)\), and \(T_{cd}(z)\) are selected from the constructed digital filter library to achieve adaptation. As \(G(z)\) may change with the variations in digital reconstruction and anti-aliasing filters, the dynamic matching algorithm needs to simultaneously adapt to \(\hat{G}(z)\) to ensure the correct operation of the adaptive ANC algorithm in regions with a low sampling rate.  
Considering the circuit components in the regions with a low sampling rate, the secondary path transfer function of the system is  
\[G(z)=T_{ed}(z)\mathcal{Z}\Big{\{}\mathcal{L}^{-1}\big{[}C_{e}(s)T_{ea}(s)G_{p} (s)T_{ua}(s)C_{u}(s)\big{]}\Big{\}}T_{ud}(z) \tag{3}\]  
where \(\mathcal{L}^{-1}\) denotes the inverse Laplace transform, and \(\mathcal{Z}\) denotes the z-transform. The true transfer function from \(u_{h}(n)\) to \(e_{h}(n)\) via the loudspeaker is \(G_{h}(z)\), and we let \(\hat{G}_{h}(z)\) be the estimate of \(G_{h}(z)\). Each time the digital reconstruction and anti-aliasing filters are updated, a new estimate of the secondary path transfer function can be obtained according to  
\[\hat{G}(z)=T_{ed}(z)\hat{G}_{h}(z)T_{ud}(z) \tag{4}\]  
Therefore, during the identification phase, the values of \(\hat{G}_{h}(z)\) corresponding to each filter group can be calculated in advance after the identification of \(G_{h}(z)\) is completed.
## 3 Node Key Construction Technology
### Important Node Construction  
The research of key node construction technologies is approaching the tendency of the high structural performance and the strong anti-seismic performance, as well as sustainable performance. As the key parts of the overall structure, the connection methods and construction forms of nodes are crucial to the stiffness and the energy dissipation capacity of the structure under earthquake action. By optimizing the node design, the node stiffness and ductility can be enhanced, and the overall anti-seismic performance can also be improved. Meanwhile, the design requirements of complicated structures can be used to ensure the safety of people's lives and property.  
Therefore, according to the overall importance of the structure and the influence range after failure, the most critical nodes 1-5 (Node 1-Node 5) in the upper and lower chord rods of the suspension truss are rooted in the structure, and the typical truss nodes of the bridge are selected for calculation (as shown in Figure 4a,b). The calculation mainly includes the non-yield checking calculation of medium earthquakes, the elastic checking calculation of medium earthquakes, and the non-yield checking calculation of large earthquakes. The general finite element program ANSYS is used for the analysis, and the SHELL63 element is used for the simulation. The SHELL63 element is a 4-node elastic shell element with bending and membrane characteristics, and it can withstand in-plane and normal loads [20; 21]. Each node of the unit has six degrees of freedom, such as the translational displacement along the x, y, and z directions of the node coordinate system and the rotational displacement around each axis, as shown in Figure 5.  
Figure 3: Overall structure calculation model diagram.
## 6 Experimental Results and Analysis
### Analysis of Load Balancing Based on Genetic Algorithm
However, this method does not take into account the number of pedestrians in the video, and thus easily to cause an excessive number of pedestrians to be processed by an agent, resulting in a longer total processing time. PSO is similar to the genetic algorithm. Firstly, a random allocation scheme is determined, and then the optimal task allocation scheme is searched through iteration to obtain a shorter total video processing time. PSO has a fast convergence speed and simple operation, but it is easy to fall into the local optimal solution, and there is no good way to find the best equilibrium solution for the agent as a whole. MGA has a strong global search capability to accurately find the optimum solution from all possible solutions. In order to find the optimum solution quickly, the algorithm guarantees the diversity of the population by accepting the worse chromosomes with a certain probability in iterations. In order not to miss the optimal solution, the threshold function is set as the stopping criterion according to Equation (9). Therefore, the global optimization can be better achieved by MGA for load balancing, so that the total processing time is the shortest. In the experiment, the total processing time is the maximum processing time of the agent. The larger the number of pedestrians assigned, the larger the task amount the agent needs to handle. Therefore, the total processing time and the processing time of one frame are made longer. The more tasks there are, the more computer resources are consumed, and the utilization of the agent's CPU is also increased. Similarly, as the total number of pedestrian increases, the number of faces that the server needs to identify increases, so the CPU utilization of the server increases. By comparing the experimental results, it is obvious that with more balanced allocation, the proposed MGA obtains better performance than both PSO and RPM. Here, when there are 16 pedestrians in the video, all three methods have experienced the problem that the processing time is too long, while in another test the discontinuity has disappeared. This may be due to a problem with the network transmission or the server's response causing the task to be handled abnormally. Therefore, some experimental suggestions are provided. It is necessary to keep the network transmission as smooth as possible and close some programs that are not used to release the memory or processor space. In addition, it is necessary to eliminate problems such as bad sectors of the hard disk and excessive CPU hardware temperature.