esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Variables Graph States Shaped as Complex Networks: Optimization and Manipulation
## 2 Results
### Background: Cluster States and Complex Networks
In particular, we can optimize given properties of the nullifiers via an analytical protocol [33], with the aim, for example, of reducing their variances, hence improving the quality of the cluster. In this work, we investigate, for the first time, clusters whose graphical representation \(\{\mathcal{V},\mathcal{E}\}\) corresponds to three different models of complex networks [1]: the Barabasi-Albert [34], the Erdos-Renyi [35], and the Watts-Strogatz [36]. These models have been developed in graph theory in order to reproduce the behaviors of complex systems. The Erdos-Renyi has been the first model considered for complex networks. It is based on random graph: given a fixed number of nodes \(n\), two nodes have the probability \(p_{ER}\) to be connected by an edge. The model generates graphs with approximately \(p_{ER}n(n-1)/2\) edges which are distributed randomly. Most nodes have a comparable node degree \(k\), defined as the number of edges of the given node. Later, by looking at the degree distribution of many real-world networks, it has been clear that complex networks go beyond the random graph model, as many networks (like Internet or the WWW) are characterized by a power-law distribution of the degree. Thus, new models, such as the Barabasi-Albert, have been developed in order to reproduce the structure of these networks, also known as scale-free networks. Barabasi-Albert networks grow from a small number of nodes according to preferential attachment, i.e., the fact that new nodes attach preferentially to nodes that already have a high degree (high number of links). In the growth process, the parameter \(m_{BA}\) specifies the number of links coming with the new node. Hubs, i.e., heavily linked nodes, arise spontaneously in this model. Watts-Strogatz networks lie between regular and random graphs and exhibit small-world properties typical of social networks. They are built from a regular network by rewiring its edges, to increase randomness. From a ring lattice with \(n\) nodes and \(k\) edges per node, which connect it to the closest neighbors, each edge is randomly rewired with probability \(p_{WS}\). The rewiring parameter \(p_{WS}\) spans from 0, the regular graph, to 1, which corresponds to a totally random graph, as shown in Figure 1. Even if we are going to study graphs with a small number of nodes compared to the ones for which these models have been developed, we can assess a different behavior for the corresponding graph states in the three classes. In the following, we will see how, starting from a given set of modes with finite squeezing values, we can optimize the quality of the clusters corresponding to these complex networks models and how the result of the optimization depends on the parameters of the analyzed model.
# A Personalized Recommendation Algorithm Based on the User's Implicit Feedback in E-Commerce
## 1 Introduction  
With the rapid development of the Internet and the emergence of big data, information and data have exploded in size, and it is more difficult for people to obtain accurate and efficient information in time. Therefore, recommendation systems [1; 2] have received more and more attention, including content-based recommendation algorithms [3; 4], collaborative filtering algorithms [5; 6; 7], and hybrid approaches [8; 9]. A good recommendation algorithm can better understand the user's purchase intention and can improve the user's viscosity for the e-commerce platform, thereby increasing the user's purchase rate. Among these algorithms, the collaborative filtering algorithm is widely used in various recommendation systems, such as Amazon, Netflix, and others. The reason is that it has no special requirements on the recommended items and can achieve significant recommendation results.  
The nearest neighbors approach (KNN) and matrix factorization (MF) are major collaborative filtering techniques. Most of them are designed with explicit feedback data such as rating data, which needs the recommendation systems to guide the users in terms of score. However, users are often reluctant to spend time reviewing items in the field of e-commerce, which causes the rating data to be difficult to obtain and sparse.  
Some recommendation algorithms are based on user's behavior logs and can solve the difficulty of data acquisition. However, the user's behavior logs lack negative feedback--we cannot be directly aware of user's preferences, which makes it more difficult to find the nearest neighbors accurately. In order to solve this problem, we fully mined the consumer's implicit feedback, such as the purchase behavior, comparison behavior, and item-sequences. The TF-IDF (Term Frequency-Inverse DocumentFrequency) algorithm and the word-embedding technology of the natural language processing (NLP) field were improved and applied to the collaborative filtering algorithm. The contributions of our work are as follows:  
* Refine the user's behavior, build item-pairs, and consider not only the user's purchase behavior but also the comparison behavior, and apply TF-IDF to collaborative filtering (CF) to calculate the user's similarity more reasonably.
* Extract the user's browse sequence for items from the user's behavior log, use word-embedding technology to map items to low-latitude vector spaces, and calculate the similarity of the items.
* Construct a secondary reordering model to generate the recommendation results for the users.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 4 Performance Evaluation
### Analysis of MCS Selection Algorithms  
Let us now consider the influence of the MCS selection algorithm and its parameters (i.e., target BLERs). The main observation is that for the two TXs cases, different target BLERs for the first TX and the second TX may significantly reduce channel resource consumption. Specifically, the usage of target BLERs \(p_{1}=0.1,p_{2}=10^{-4}\) reduces DL resource consumption by up to 40% compared to the case with \(p_{1}=p_{2}=10^{-2.5}\) because for the first TX, providing BLER of the order of \(10^{-2.5}\) requires much more resources than 0.1. The resources for the second TX are consumed only when the first TX fails with the probability \(p_{1}\). This effect significantly differs from the one observed for delay-tolerant broadband traffic for which all TXs are carried out with the same target BLERs and MCSs. To further elaborate on that observation, we consider the downlink resource consumption of only the first TX \(\theta_{1}(p_{1})\) and total consumption of the first TX and second TX (if any) \(\theta_{2 TX}(p_{1})\). As we select \(p_{1}\cdot p_{2}=PLR^{QoS}\), downlink resource consumption for second TX can be estimated as \(\theta_{1}\Big{(}\frac{PLR^{QoS}}{p_{1}}\Big{)}\), and \(\theta_{2 TX}(p_{1})\) is:  
\[\theta_{2 TX}(p_{1})=\theta_{1}(p_{1})+p_{1}\cdot\theta_{1}\Big{(}\frac{PLR^{ QoS}}{p_{1}}\Big{)},\]  
Figure 7 shows \(\theta_{1}(p_{1})\) and \(\theta_{2 TX}(p_{1})\) for the NLOS and LOS scenarios. In the NLOS scenario, \(\theta_{1}\) decreases monotonically with \(p_{1}\), and the minimum of \(\theta_{2 TX}\) is achieved at relatively high \(p_{1}=0.25\). In contrast, in the LOS scenario, \(\theta_{1}\) achieves a plateau after \(p_{1}=10^{-3}\), since even the highest possible MCS satisfies target BLER \(10^{-3}\). Because of this, the minimum of \(\theta_{2 TX}\) in the LOS scenario is achieved at \(p_{1}=10^{-3}\). Note that the target BLER \(p_{1}=0.1\), which is widely used by default for broadband traffic, provides downlink resource consumption close to that of the minimum (the difference does not exceed 10%). Thus, the selection of \(p_{1}\sim 0.1\) recommended for broadband traffic provides sub-optimal results for both considered scenarios. However, in contrast to broadband traffic, the target BLER for the second TX should be selected to guarantee high reliability. Further, we consider the MCS selection scheme with two target BLERs \(p_{1}=0.1\) and \(p_{2}=10^{-4}\).
###### Abstract  
The main objective of this work was the construction of a numerical model using Advanced Process Simulation Software to represent the dynamic behaviour of a thermal storage system (TSS). The storage model was validated by comparing the results with the measured data of the storage process of the Andasol 2 solar power plant. Subsequently, a system analysis and system optimisation were carried out, and the stand-alone concept of the thermal storage system is presented. Stand-alone refers to an isolated use of the storage system without a solar power plant. During power peaks, this storage medium is heated with excess electrical power and later returned to the electrical grid through a steam cycle. Then, the system was optimised by modelling four models based on the type of storage medium and the temperature difference of the storage system. The four models, Andasol 2, SSalt max, Hitec, and Carbonate, were evaluated and compared in terms of the improvement in capacity and efficiency that can be achieved. The comparison shows that the preferred storage medium is carbonate salt due to the increases in both efficiency and capacity. The greatest increase in efficiency in terms of power generation can also be achieved with the Carbonate model (18.2%), whereas the amount of increase was 9.5% and 7.4% for each of SSalt max and Hitec, respectively. The goal of this analysis and system optimisation of a thermal salt storage system is to stabilise and relieve the local power grid.  
dynamic simulation; stand-alone system; molten salt; thermal storage system; combined cycle plants
# Children's Functional Clothing: Design Challenges and Opportunities
## 4 Functional Textiles
### Functional Textiles and Clothing for Children's Development and Interaction
#### 4.2.2 Colour-Changing Textiles  
Colour-changing materials experience modifications in electron density or molecular arrangement induced by external stimuli, resulting in a shift in colour, with the material returning to its initial state when the stimulus reduces or disappears [16; 28]. These materials can be categorised as photochromic, thermochromic, electrochromic, solvatochromic, halochromic, tribochromic, and piezochromic. Each category responds to specific stimuli, namely, UV light, temperature, applied fields or currents, among others [16; 23], and offers potential applications in monitoring, safety, and comfort [28].  
In a study conducted by [PERSON] and [PERSON] [50], thermochromic pigment applied to fabric demonstrated potential as a non-intrusive wearable system for early fever detection in young children, monitoring diverse skin temperatures. In addition to the monitoring function, chronic textiles prove to be interesting tools for learning and stimulation, especially for children, transforming how they interact with clothing [28; 51]. However, some chromic products may lack colour fastness, leading to a gradual loss of functionality over time. A study conducted by [PERSON] et al. [51] addresses this concern and highlights the potential of exploring colour interactivity by creating changeable colour palettes and patterns, incorporating pigments with variable levels of reversibility and permanence. This insight underscores creativity in the design process and contributes to the increase of chronic products life cycle. Beyond technical aspects, the use of colour-changing materials in children's clothing can introduce interactive and educational functionalities, fostering curiosity and contributing to cognitive development.  
In addition to the functionalities previously described, numerous other properties are being explored, such as flame retardancy, anti-odour capabilities, insect-repellence, reflective properties, and more. Fabrics with these properties aim to provide improved functionalities by adapting to diverse environmental conditions and consumer activities [23].
# Structural Reliability Analysis Using Stochastic Finite Element Method Based on Krylov Subspace
###### Abstract  
The stochastic finite element method is an important tool for structural reliability analysis. In order to improve the calculation efficiency, a stochastic finite element method based on the Krylov subspace is proposed for the static reliability analysis of structures. The first step of the proposed method is to preprocess the static response equation considering randomness to reduce the condition number of the coefficient matrix. The second step of the proposed method is to construct a Krylov subspace based on the preprocessed static response equation. Then, the static displacement of random sampling is expressed as a linear combination of subspace basis vectors to achieve the purpose of a fast solution. Finally, statistics and failure probability are calculated according to the static response obtained from thousands of random samples. Three numerical examples are given to compare the proposed method with the stochastic finite element method based on the Neumann series. The results show that the stochastic finite element method based on the Krylov subspace is more accurate and efficient than the stochastic finite element method based on the Neumann series.  
stochastic finite element; Krylov subspace; static displacement; Neumann series; failure probability +
Footnote †: journal: _Algorithms_  
0
# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
###### Abstract  
Aim: This study aimed to compare the accuracy and reproducibility of the EinScan H2 (SHINING 3D, Hangzhou, China) with the Vectra M3 3D Imaging System (Canfield Scientific, Parsippany, NJ, USA) and with direct anthropometric measurements. Methods: Twenty subjects were scanned with two different facial scanning systems. Linear measurements were compared with direct anthropometric measurements taken on the patient's face, while area analysis (forehead, tip of the nose, chin, right and left cheek) was performed by overlaying scans after best-fit alignment using the Geomagic Control X v2020.1.1 program. Statistical analysis was conducted using IBM SPSS v28 software, except for the power analysis, which was conducted using R v4.2.1. Results: The intraclass correlation coefficient (ICC) showed high repeatability for both the linear and point measurements, with all values exceeding 0.90. The paired-sample Wilcoxon test revealed no significant differences (\(p>0.05\)) between the linear measurements, indicating consistency across the three measurement methods. Point analysis using the single-sample Wilcoxon test also showed no significant differences (\(p>0.05\)) in the median values. The differences between the two scanning instruments for cephalometric points ranged from 0.5 mm to \(-0.5\) mm, except for the subnasal point (0.73 mm). Over 70% of the surface areas analyzed showed overlapping within the highly reproducible range (0.5 mm to \(-0.5\) mm). Conclusion: Both scanning systems investigated in this study proved to be accurate and reliable for capturing 3D images of the patient's face.  
facial scanner; digital orthodontics; 3D analysis 2020
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 2 Overall System Design  
The complete data processing flow chart of low-orbit satellite signals on the satellite and on the ground is shown in Figure 1. The core steps of the processing flow on the satellite include two frequency conversions, that is, the encoded I and Q channel data process directly spread spectrum (spread spectrum code rate 10.23 MHz, code length 1023) and spectrum shifting (QPSK modulation) to the radio frequency band of 2\(\sim\)3 GHz for transmission. The processing flow of the receiving terminal on the ground is the reverse process of the processing flow on the satellite [1; 3].  
Figure 1: Complete data processing flow chart of low-orbit satellite signals on the satellite and on the ground.  
Among them, the processing flow of the receiving terminal on the ground is shown in Figure 2. The receiving terminal achieves signal reception and processing in complex electromagnetic interference environments through signal processing such as radio frequency processing, ADC, narrowband anti-interference, digital beamforming, filter, extraction and truncation, and information processing such as capture, tracking, bit synchronization, frame synchronization, data demodulation, message decoding, and positioning operations.  
According to the independence of functions of each part in the above-mentioned low-orbit satellite signal processing flow on the satellite and on the ground, the system can be divided into four blocks: antenna array, radio frequency unit, baseband processing board and application processing terminal [1; 6], as shown in Figure 3. Among them, the antenna array filters and amplifies the signal and then transmits it to the radio frequency local oscillator board; the radio frequency unit is responsible for moving the radio frequency signal received by the antenna array to an intermediate frequency nearby, which facilitates the subsequent baseband board to perform beamforming, acquisition and tracking process; the baseband processing board is mainly responsible for the IF signal sent from the radio frequency module to process AD conversion, beamforming, acquisition, tracking, demodulation and decoding, etc., then the processed baseband signal is sent to the application processing terminal; the data processing terminal is mainly responsible for interacting with the receiver and presents the results to users.  
Figure 3: Schematic diagram of sub-system relationships.  
Figure 2: Processing flow chart of ground receiving terminal.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 3 The Key Management Scheme for Cluster-Based WSNs  
In this section, a security key management scheme for wireless sensor networks based on public-key cryptography is presented. To avoid long-term attacks through which attackers can analyze the encrypted traffic over the network for a long period of time, a key update approach is specifically designed.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 3 Application of Surface Modification of HNTs
### As the Potential Consolidants  
Material cultural heritages are the legacy of human history. There have historical value and cultural heritage for mankind. Cultural relics are involve various fields, such as history, art, and science. However, it is difficult to protect them, due to issues arising from items such as ancient books and waterlogged archaeological woods, which may be rendered unusable due to highly sensitive and responsive environmental factors. Most of them exist in special environment, such as anoxic, low temperatures, and humid. The materials become fragile and loss mechanical resistance because of the extreme deteriorating environment. It is necessary to consolidate the thermal and mechanical properties to protect them. The HNTs are expected to the meaningful and promising protective agents for material cultural heritages by the way of improved mechanical properties.  
[PERSON] et al. [62] modified the HNTs with Rosin by chemical treatment. The results proved that the HNTs endowed better mechanical properties and thermal stability. The thermal and mechanical properties of Rosin were sufficiently improved by the mount of HNTs. This conferred to the HNTs/Rosin nanocomposites were innovative protocol for consolidating waterlogged archaeological woods. In addition, [PERSON] et al. [63] used the nanocomposite to enhance the thermal and mechanical properties between HNTs and beeswar by direct blending. The experiments indicated that the HNTs were homogeneously dispersed and significantly reduced the thermal degradation of Rosin. Except for the consolidation of waterlogged archaeological wood, HNTs were used to compounded the Ca(OH)\({}_{2}\) and then placed end-stoppers to preserve the paper [64]. They have proved that the HNTs/Ca(OH)\({}_{2}\) nanocomposites could improve the mechanical performance and balance the pH alteration with the addition of nanotubes. In view of the above mentioned research results, there have great application prospects for HNTs to consolidate waterlogged archaeological woods.
## 2 Study Area and Data
### Data
#### 2.2.3 Co\({}_{2}\) Concentration and Terrain Attributes Data  
For GPP prediction, this study used global monthly gridded atmospheric CO\({}_{2}\) concentration data, which are crucial in evaluating the impact of CO\({}_{2}\) fertilization on terrestrial ecosystems [41]. The dataset provides CO\({}_{2}\) concentration values at a 1\({}^{\circ}\) spatial resolution, covering both historical (1982-2013) and future (2021-2100) periods across four SSP scenarios [42]. It captures spatial, seasonal, and annual variations in CO\({}_{2}\) levels, offering a detailed representation of the atmospheric CO\({}_{2}\) distribution--critical for accurate GPP forecasts. Additionally, the Global Multi-Resolution Terrain Elevation Data 2010, initially at a 30 arc-second resolution, were resampled to 0.05\({}^{\circ}\) using bilinear interpolation. Five terrain attributes--elevation, slope, aspect, latitude, and longitude--were incorporated into the CNN model for simulation and projection [43].
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 5 Discussion
### Predictive Model Performance: ARIMA and MLR  
#### 5.1.1 ARIMA Model Insights  
The ARIMA model effectively captured seasonal demand trends, providing Company A with actionable insights for managing inventory during high-demand periods. Seasonal analysis revealed pronounced demand spikes at specific times, supporting ARIMA's potential as a proactive inventory management tool for aligning stock levels with forecasted demand cycles. This aligns with findings by [PERSON] et al. [6] on the importance of seasonality in cold chain logistics.  
Despite its strong performance in identifying broad trends, ARIMA's predictive accuracy was impacted by irregular ordering patterns and data gaps. The observed Mean Absolute Error (MAE) of 4.5% and Root Mean Squared Error (RMSE) of 6.2% indicate moderate deviations, particularly for products with inconsistent sales intervals. Such limitations underscore the importance of comprehensive and balanced datasets, as incomplete data can  
Figure 12: Multiple Linear Regression: Predicted vs. Actuals.  
skew time-series forecasting accuracy, especially for seasonal products [14; 15]. Addressing imbalances in product distributions, as highlighted by [PERSON] et al. [20], could enhance ARIMA's reliability by ensuring a representative sample across product categories.