esa-sceva/satcom-chunk-collection huggingface dataset:
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 2 Overview of Location Technologies
### Cellular-Based Radio  
Cellular-based localisation has been used as a simple and coarse solution when there is a lack of satellite visibility in GNSS, typically indoors and in scenarios, such as urban canyons [17]. The arrival of 5G brings new specifications for high-precision positioning as described in [9], which can be summarised in:  
* Horizontal and vertical positioning error \(<\) 3 m for 80% of user equipments (UEs) in indoor deployments;
* Horizontal and vertical positioning error \(<\)10 m and \(<\)3 m, respectively, for 80% of UEs in outdoors deployments.  
5G works on 700 MHz, 3.5 GHz and millimetre wave of 26 and 28 GHz. High frequencies allow high-precision ranging in direct line of sight (LoS) with the target but highly suffers from attenuation, multipath and reflections in non-line of sight (NLoS). In contrast, lower frequencies are more robust to attenuation reaching longer distances, however, multipath effects can deteriorate the precision of the ranges. In [1], in order to eliminate the need for clock synchronisation, the use of different timing techniques such round-trip time (RTT) are proposed for indoors.  
Nevertheless, the existing and deployed LTE networks can be used as a back-up for other location technologies [10]. Despite of the coarse ranging information that LTE provides, LTE network is globally available in contrast with 5G that has not been yet fully deployed. End-users may benefit from LTE in cases where no high-precision technologies provide localisation. However, LTE utilises the received signal strength indicator (RSSI) for ranging. RSSI highly suffers from multipath and fadings which leads to high variations and an increase in the ranging error.
## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
### Procedure of the Comparison Using Dlib-Based Face Recognition
#### 5.1.2 Step 2: Feature Point Location for Face Alignment  
To acquire comprehensive information about the detected face image, it is crucial to pinpoint facial feature points. This process enables the identification of facial contour boundaries, including the eyebrows, eyes, nose tip, and mouth corners, among other significant features. By accurately locating these points, we can precisely determine the face's position and structure [75].  
For feature point localization in the Dlib-based pipeline, the CNN-based five-point shape predictor (shape_predictor_5_face_landmarks.dat) provided by Dlib was employed, which detects five key facial landmarks: the inner and outer corners of both eyes and the tip of the nose [84]. Based on these landmarks, facial alignment is performed using Dlib's get_face_chip function [84], which rotates, scales, and crops the face region to produce a standardized, centered, and frontal view, thereby reducing pose-related variance. Research indicates that face alignment enhances face recognition accuracy [85]. To normalize the faces, basic image transformations such as rotation and scale, which preserve parallel lines (referred to as affine transformations), are applied [75] until the key points are centered as accurately as possible.  
In contrast, the YOLOv8-based setup [79] does not support explicit landmark-based alignment since YOLOv8 does not return facial landmarks. As a result, step 2 is omitted in this pipeline. Nevertheless, the preprocessing ensures that the faces are cropped based on the bounding box and rescaled to a fixed input size, which introduces a basic level of normalization.  
The test images used in this study are controlled to a high degree. Image 1 is a passport-style frontal photograph, which does not require alignment. Image 2, however, exhibits a slightly lateral view, which may introduce minor pose-related differences. While this could be mitigated by applying facial alignment, it was not considered substantial enough to necessitate an additional alignment procedure in this context. Prior studies suggest that such alignment becomes especially important when dealing with larger pose variations or uncontrolled input conditions [75; 85].
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 4 Workbench Description and Test Conditions
### Channel Response Models under Test  
Channel models are usually represented by its channel response function \(H(f)\) defined in module and phase. For the tests described in this work, the following channels have been considered.
# On Finding Optimal (Dynamic) Arborescences
## 2 Optimal Arborescences
### Edmonds' Algorithm
#### 2.1.2 Expansion Phase  
The final content of \(E_{i}\) holds an arborescence for the graph \(G_{i}\). Let \(H\) denote a subgraph formed by the edges of \(E_{i}\). For every contracted cycle \(Q_{i}\), we add to \(H\) all the cycle edges except one. If the contracted cycle is a root of \(H\), we discard the cycle edge with the maximum weight. If the contracted cycle is not a root of \(H\), we discard the cycle edge that shares the same destination as an edge currently in \(H\). The algorithm proceeds in reverse with respect to the contraction phase, thus examining the graph \(G_{i-1}\) and the cycle \(Q_{i-1}\). This process continues until all contractions are undone, and \(H\) is the final arborescence.
###### Abstract  
The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology.  
6G; AI-native RAN; end-to-end AI; cross-domain Article
# An Improved Particle Swarm Optimization Algorithm for Data Classification
###### Abstract  
Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques' experiment result depicts much better performance than other existing techniques.  
Key words and phrases:feed-forward neural network; quasi-random sequence; opposition rank-based inertia weight; particle swarm optimisation 2022
# The Impact of Power Definitions on the Disaggregation of Home Loads for Smart Meter Measurements
## 1 Introduction
For residential appliance identification, there are two main approaches: intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). The first approach (ILM) is characterized by the use of sensors applied to appliances or to specific points of the load [38; 39]. The main disadvantage of this approach is associated with its high costs. In the second approach (NILM), the identification of turned-on appliances is realized by a single point, usually the entry point of the power supply. One of the main problems associated with this last approach is that the identification of turned-on appliances from total electric power is very complex and difficult. The first NILM techniques were based on the analysis of active and reactive power measurements, in which statistical tools were applied with the purpose of detecting changes in these measurements that are related to the turned-on/off appliances. For example, in [40; 41], works are presented in which these kinds of techniques were used. However, these techniques showed some issues in the identification of complex appliances, especially with multiple states. So, other approaches have been used in order to solve this problem. One of the approaches used Hidden Markov Models (HMMs) [42; 43], although this presented some problems when the time duration was not relatively constant for each of the states. Other approaches have also been proposed, in which instead of using active and/or reactive power, grid current harmonics, voltage-current trajectory or images were analyzed [44; 45; 46; 47]. In the past few years, new approaches have been proposed, for which interesting results have been obtained. These approaches include techniques based on graph signal processing [48], a cepstrum-smoothing-based method [49], a fuzzy clustering algorithm [50], an artificial bee colony [51] and deep learning [52; 53]. A review of NILM techniques can also be seen in [54]. In [55], a cloud-based on-line disaggregation algorithm for home appliance loads is presented. However, these methods have not been studied in the context of grid distortion and the definitions of powers taking into consideration this distortion. Numerous approaches have been employed to identify individual appliance loads from aggregated smart meter data. However, the impact of different power definitions on load disaggregation has not been extensively explored. This paper aims to investigate the suitability of smart meter measurements, considering grid distortions, for non-intrusive load monitoring (NILM). Different power definitions will be analyzed to assess their impact on NILM. A genetic algorithm-based NILM approach will be employed to evaluate this impact. The proposed methodology will be validated using real-world data from multiple households.
## 4 Conclusions  
To address the issues of low thermal conductivity and liquid phase leakage in the application of solid-liquid PCMs in building walls, an aluminum alloy phase change panel was proposed. The phase change panel was applied to the inner side of exterior walls. Thermal experiments were conducted to investigate the heat transfer process and mechanisms of the phase change panel, the role of pin-shaped aluminum rods, and the practical application effects of the phase change panel. The main conclusions are as follows:  
Firstly, as the mass fraction of stearic acid n-butyl ester increases, both the melting point and latent heat of fusion of the composite PCMs decrease. When the mass ratio of stearic acid n-butyl ester to paraffin is 3:7, the composite PCM exhibits a melting point of 28.30 \({}^{\circ}\)C, meeting the suitable phase change temperature requirements for the Hainan region.  
Secondly, during the three stages of the heat transfer process in the phase change panel, the solid-phase sensible heat storage stage is dominated by thermal conduction, the latent heat storage stage involves both the latent heat storage properties of the PCM and liquid-phase convective heat transfer, and the liquid-phase sensible heat storage stage is primarily driven by the liquid-phase convective heat transfer of the PCM.  
Thirdly, the aluminum rods extend the heat transfer path and reduce the liquid-phase convective heat transfer rate of the PCM. Compared to phase change panels without aluminum rods, those with aluminum rods exhibit a 20% longer phase change time and a smaller temperature difference between the two surfaces.  
Finally, in practical application, when the exterior side of the panel reaches 35 \({}^{\circ}\)C, the interior side reaches 28 \({}^{\circ}\)C after 120 min, initiating the PCM melting. The heat transferred through the wall structure is absorbed by the PCM, maintaining the interior temperature between 28 \({}^{\circ}\)C and 29 \({}^{\circ}\)C for up to 180 min. This demonstrates the excellent thermal insulation and temperature regulation effects of the phase change panel.  
Conceptualization, B.J. and C.Y.; methodology, B.J. and C.Y.; validation, B.J. and C.Y.; investigation, C.Y.; resources, B.J.; data curation, C.Y.; writing--original draft preparation, C.Y.; writing--review and editing, B.J.; visualization, C.Y.; supervision, B.J.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hainan Province (No. 123 MS006) and the National Key Research Program of China (No. 2019 YFD1101004).  
Not applicable.  
The data presented in this study are available upon request from the authors. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 4 Reconstruction Parameters Optimization of Tucker Decomposition  
When using the algorithm proposed in this paper, it is found that the core tensor dimension of the transformation matrix, the number of measuring points, and the arrangement of measuring points will affect the reconstruction accuracy. Therefore, optimizing the key parameters in the reconstruction calculation process is of great significance to improve the accuracy of the reconstruction algorithm.
## 4 Stability Evaluation
### Result Analysis
#### 4.3.5 Equivalent Strain  
Figure 16 shows the equivalent strain distribution maps of the cavity's surrounding rock under different internal pressures. The red color indicates that the equivalent strain value is higher than 3%, which suggests the existence of creep damage. From the figure, it can be seen that under low pressure, there are more red-colored areas, which are mainly located near the cavity. By contrast, the equivalent strain values of the salt cavity covered by residues are small, indicating that these areas are stable in terms of the equivalent strain index, which also reflects the inhibitory effect of the residues. Under the same pressure condition, the range of the red area at the top of the deep cavity is larger than that of the shallow cavity, while the range at the waist of the deep cavity is smaller than that of the shallow cavity. This reflects that the top of the deep cavity and the waist of the shallow cavity are the weak areas more prone to creep damage under low pressure. As the internal pressure rises, there is a gradual reduction in the red-colored areas. Notably, under high-pressure scenarios, the red areas almost vanish. This phenomenon suggests that the creep damage of the salt cavity is diminishing and its stability is being enhanced.  
Figure 16: Distribution diagrams of equivalent strain in surrounding rock under different internal pressures. (**a**) 10.5 MPa; (**b**) 12 MPa; (**c**) 25.5 MPa; (**d**) 29 MPa.
## 3 Epidemiological Model and Evolutionary Game Theory
### Epidemiological Model
#### 3.1.1 SIR Model  
The SIR model is the most basic epidemiological model. The model describes changes in three quantities over time, denoted by \(S\), \(I\), and \(R\) respectively, where \(S\) represents the number of susceptible individuals, \(I\) represents the number of infected individuals, and \(R\) represents the number of immune individuals. The SIR model is given by the ordinary differential equations in system (1). Figure 2 shows a model diagram that describes the relationship between each state.  
\[\left\{\begin{array}{l}\frac{dS}{dt}=-\beta SI\\ \frac{dI}{dt}=\beta SI-\gamma I\\ \frac{dR}{dt}=\gamma I\end{array}\right. \tag{1}\]  
Each parameter is explained as follows: \(\beta\) is the effective contact rate, also called the infection rate, \(\gamma\) is the recovery rate, and \(1/\gamma\) is the average duration of infection. The SIR model assumes \(S^{\prime}+I^{\prime}+R^{\prime}=0\), which means that the number of individuals does not change due to births or deaths. The model does not take into account the possibility that a once-infected person may become infected again.