esa-sceva/satcom-chunk-collection huggingface dataset:
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## 1 Introduction  
### Background  
Over the past decade, the field of augmented reality (AR) has been established as one of the most promising areas of computer graphics. During this time, myriad groundbreaking applications have been actualized, increasing the significance of AR in daily life [1]. Such developments have not remained outside the academic realm; rather, they have had a significant influence on this sector through the creation of new disseminating tools, knowledge, and learning structures [2]. Refs. [3,4] noted that novel lines of actions and ideas that have been implemented are linked to various forms of established technologies, such as electronic learning (e-learning), ubiquitous learning (u-learning), mobile learning (m-learning), and game-based learning. Ref. [5] proposed that such technological innovations in academic setting allow various fields of knowledge to collaborate to generate different academic issues and complementary methods, content, and learning objectives.  
Indeed, [6] postulated that AR embeddedness in the field of education generates positive learning and teaching outcomes. Ref. [7] proposed that the use of AR in education provides portable, low-cost, stress-free, and promising solutions for use in a variety of academic settings. However, the challengesassociated with its implementation have also been reported by academic researchers [8; 9]. Ref. [8] identified the challenges of usability and inadequate experience with the involved technologies, and [9] noted several issues related to resistance from teachers, information overload, and usability. Although advantages and disadvantages of AR have been identified in the literature, no systematic reviews of this topic in the current literature have been conducted, which has created a knowledge gap, necessitating further exploration in this area.
## 4 Machine Learning Methods  
### NER Task Formulation  
We consider the NER problem of detecting pharmaceutically relevant entities as a multi-label classification of tokens--words and punctuation marks--in sentences. For each token, the output is a set of tags that comprises a tag in the BIO format for each attribute of each entity type (DrugName, DrugBrand and so on): the \"B\" tag indicates the first word of the mention of the particular attribute, the \"I\" tag is used for subsequent words within the mention and the \"O\" tag means that the word is outside of an entity mention. A token can be inside multiple mentions, allowing for intersecting mentions of different attributes.  
We evaluate two methods for entity recognition on our corpus. The first (Model A) is based on a bidirectional long short term memory (BiLSTM) neural network topology with different features representing an input text: dictionaries, part of speech tags and several methods of word-level representations, including FastText [45], ELMo [26], BERT, words character long short term memory (LSTM) coding, etc. At its output, Model A produces one of the three tags--B, I or O--indicating the input token's belonging to a particular entity attribute. For each attribute of each entity type, an independent instance of the model is trained. The second (Model B) is a multi-label model which predicts all tags of a token using a single instance of the neural network. It combines the pre-trained multilingual language model XLM-RoBERTa [46] and the LSTM neural network with several of the most efficient features. Details of the implementation of both methods and the features they use for input encoding are presented below.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 1 Introduction  
In recent years, the effects of global warming that are mainly caused by the increase of carbon emissions are becoming more and more tangible. Studies have demonstrated that this phenomenon is directly related to humans' activities and their inefficient use of fossil fuel energy and natural resources, thus it is urgent to promote energy efficiency at homes, commercial sectors, and industries [1]. An effective method is to allow users to monitor the device-level energy consumption information for a long time. Indeed, statistics show that residential and commercial buildings account for 40% of total energy consumption [2], and studies have estimated that via energy feedback and specific appliance upgrade programs up to 20% of this consumption could be avoided [3].  
Non-intrusive load monitoring (NILM) [4] or load disaggregation is an attractive method that decomposes the mixed energy signal into several individual signals of electrical appliances using only the measurements at the entrance of the electric power. It can provide detailed consumption information both to the energy provider and to the consumers. Compared with the conventional intrusive load monitoring technique that requires installation of one sensor for each appliance, NILM is advantageous without sub-metering or additional hardware, eliminating cost and installation complexity. Moreover, NILM can support the revamping of equipment, demand response [5], and enable the prediction of the power demand, the decision making for policy makers, and the prevention of overloading or blackouts over the energy network via appliance load shifting [6].
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 5 Theoretical and Computational Mechanical Frameworks for Magneto-Active Polymers
### Constitutive Modelling of Polymeric Matrix  
The mechanical response of soft polymers is highly nonlinear, dependent on strain rate and temperature, presents viscous relaxation mechanisms, Mullins' effects and, in the case of hydrogels, relaxation times associated with solvent diffusion. The modelling of elastomers is generally addressed by hyperelastic constitutive laws that are based on an energy potential, \(\psi\). This potential depends, a priori, on the deformation gradient tensor \(\mathbf{F}\), the material temperature \(\theta\), and internal variables (i.e., viscous deformation gradient \(\mathbf{F}^{\mathbf{v}}\) for viscoelasticity). This potential corresponds to the Helmholtz free energy and, following thermodynamics principles, can be consistently derived to obtain the final constitutive equations by means of stress tensors.  
Some of the most common energy potentials used for the polymeric matrix are the neo-Hookean and eight-chain models, which in their incompressibility form read, respectively, as:  
\[\psi(\mathbf{F})=\frac{\mathrm{G}}{2}(\mathrm{I}_{1}-3) \tag{1}\]  
\[\psi(\mathbf{F})=\mathrm{G}\sum_{k=1}^{K}\frac{\mathrm{C}_{k}}{\mathrm{N}^{k-1 }}\left(\mathrm{k}_{1}^{k}-3^{k}\right) \tag{2}\]  
where \(\mathrm{G}\) is the shear modulus, \(\mathrm{I}_{1}=\mathrm{tr}\left(\mathbf{F}^{T}\mathbf{T}\right)\), \(\mathrm{N}\) is the number of Kuhn segments and \(\mathrm{C}_{k}\) are constants related to the Pade approximation of the inverse Langevin function.  
These models can be extended to account for viscous responses by defining the potentials as a function of an elastic deformation gradient tensor computed as:  
\[\mathbf{F^{e}}=\mathbf{F}\mathbf{F^{-v}} \tag{3}\]  
where the viscous component \(\mathbf{F^{v}}\) represents the viscous part of the deformation gradient and is defined by a viscous flow rule. Some examples of viscous-hyperelastic models can be found in the literature [153; 154; 155]. Other dependences may include hardening-softening terms associated with temperature or continuum damage terms associated with Mullins' effects [156]. Moreover, when the polymeric matrix is composed of hydrogel, a volumetric mechanical response must be added as a consequence of solvent diffusion processes [157; 158; 159].
## 6 Challenges and Future Research Directions
### Future Research Directions
* Non-Orthogonal Multiple Access can enhance capacity by allowing multiple users to share the same frequency resource, increasing efficiency in underwater communication. * Generalized Frequency Division Multiplexing provides flexible subcarrier arrangements that mitigate interference, improving performance in dynamic underwater environments. * **Transfer Learning, Domain Adaptation, and Cross-Platform Resilience:** Future research should include structured robustness evaluation frameworks to test model behavior against channel perturbations and unpredictable underwater dynamics. Techniques such as transfer learning and domain adaptation offer promising solutions to improve cross-platform generalization. TL can help reuse pretrained representations in new environments with limited data, while DA adapts models across datasets with different distributions. Benchmarking models across different modem hardware and frequency bands will also be essential for scaling up reliable, AI-enabled underwater acoustic systems. * **Compact Models and Real-Time Strategies:** To support edge and federated UWA computing, future work must address model size and execution speed. Compression techniques like pruning, quantization, and distillation help reduce power consumption and memory load [63]. These are vital for low-resource devices in remote underwater environments. For latency-sensitive tasks, streamlined feature sets and efficient computational graphs can minimize delays. FPGA-based processing and AI chips offer real-time capability while conserving energy. Clear complexity-performance trade-offs should guide model selection for embedded use. In federated setups, lightweight models allow distributed training and updates without overwhelming edge devices. Together, these strategies make learning-based systems feasible for scalable and responsive underwater networks. * **Learning-on-the-Fly and Intelligent Resource Allocation:** Future UWA systems should explore real-time learning paradigms--including online, incremental, and meta-learning techniques--to enable continuous model refinement during deployment. Moreover, intelligent resource allocation remains underexplored: RL and game-theoretic frameworks offer potential for optimizing spectrum usage, power control, and adaptive modulation/coding under varying bandwidth-distance constraints. Integrating these strategies with channel estimation and feedback loops can lead to joint optimization pipelines that maximize throughput and resilience in complex marine networks. These future research directions highlight the need for efficient, scalable, and adaptive models to enhance underwater acoustic communication systems. By addressing these challenges, AI-driven methodologies will pave the way for more reliable, energy-efficient, and intelligent UWA communication networks, supporting critical applications such as deep-sea exploration, naval security, and marine research.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Security and Privacy Challenges  
Ensuring the security and privacy of V2V communication remains a critical challenge. Future research should focus on developing robust encryption, authentication, and access control mechanisms to protect sensitive data transmitted between vehicles. Additionally, addressing privacy concerns related to the collection and sharing of vehicle telemetry data is essential for fostering user trust and adoption of V2V communication technologies.
###### Abstract  
_Background_: Stabilization of luxated, avulsed, and root-fractured teeth is a part of the dental trauma treatment. The aim of the study was to assess the power chain and its comparison with two commonly used splinting materials. _Methods_: The evaluated splints were a wire-composite splint (WCS) with a diameter of 0.5 mm, a titanium trauma splint (TTS), and a power chain-composite splint (PCS). The splints were all fixed to the labial surfaces of permanent maxillary incisors in 10 human volunteers. The following parameters were measured: tooth mobility with Periotest before and after the splint application and removal, Aproximal Plaque Index (API), Sulcus Bleeding Index (SBI), and time needed to apply and to remove the splint. Each volunteer was also given a visual analogue scale (VAS) questionnaire to assess his/her subjective attitudes to splint materials. _Results_: The PCS was the most flexible material as it had the lowest horizontal Periotest values. For the two other methods, horizontal splint effects were similar and were also low. The PCS was the fastest to apply and remove and was also the most aesthetic splint. _Conclusion_: In this experimental study among volunteers, all three tested materials were found suitable for splinting the PCS has some advantages worth it to be further evaluated clinically in trauma patients.  
t 2020; 10, 8398; doi:10.3390/app10238398
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Network Model
#### 4.4.1 Physical Delays (\(f(P)\))  
In our calculation of the source-destination path \(P\), we define the time taken to cross a link by the first packet header byte as burst time(\(B_{t}\)) and time taken for one packet to cross a switch as switching time (\(H_{t}\)) over a path containing \(W\) switches. Switching techniques are essential considerations, as they determine how internal switches connect the input to output and message transfer time. We adopt Virtual Cut-Through [50], which is highlighted by Duato et al. [51] as the most used switching technique. Hence, our calculations use the following:  
\[f(P)=B_{t}+\frac{Pk_{s}}{Lk_{b}}+W\bigg{(}B_{t}+(H_{t}+\frac{Pk_{b}}{Lk_{b}} \bigg{)} \tag{3}\]  
where \(Pk_{s}\) denotes the packet full size in bits, \(Pk_{h}\) denotes the packet header size in bits, and \(Lk_{b}\) denotes the link bandwidth in bit/sec.
## 3 Results
### Effect of Laser Scanning Speed on Material Removal Rate
In order to investigate the effect of scanning speed on material removal rate, the data in Figure 15 were nonlinearly fitted using the GaussAmp function of Gauss type, where the independent variable is the scanning speed and the dependent variable is the material removal rate. The corresponding fitting equations are shown in Equation (10) when the laser power is 200 mW and in Equation (11) when the laser power is 100 mW. \[\mathrm{y}=247,082.764+81,225.199\times\mathrm{e}^{(-0.5\times\frac{1+0.071} {0.333})^{2}} \tag{10}\]  
Figure 15: Material removal rates under different scanning speeds. \[\mathrm{y}=80,761.213+93,213.034\times\mathrm{e}^{(-0.5\times\frac{\mathrm{x}-0.971 }{0.392})^{2}} \tag{11}\]  
From the fitting curve in Figure 15, it can be seen that when the energy \(-\)flux density of the focused spot and the repetition frequency is certain, with the increase in scanning speed, the volume of material removal per unit of time increases and then decreases, and the material removal rate is maximum when the scanning speed is around \(1\) mm/s.
## 3 Results and Discussion
### Discussion  
#### 3.4.1 The Influence Mechanism of Freeze-Thaw Cycle on Grouting Reinforcement Body  
In this study, the impact of freeze-thaw cycles on the physical and mechanical properties of grouting reinforcement body was investigated through a comprehensive series of experimental tests. Extensive freeze-thaw testing has demonstrated that the mass and wave velocity of grouting reinforcement exhibit significant declines as the number of freeze-thaw cycles increases [38; 39]. Correspondingly, the mass loss rate and wave velocity loss rate progressively rise. The macroscopic mechanical properties of grouting reinforcement body also deteriorate gradually, with notable reductions in peak strength, elastic modulus, and shear strength parameters [40; 41]. The peak strength and elastic modulus are found to have a negative exponential relationship with the number of freeze-thaw cycles, which is consistent with the experimental results obtained in this study. This phenomenon can be primarily attributed to the freezing of pore water within the grouting reinforcement body as ambient temperatures decrease. The expansion of frozen water generates frost heave forces that act upon the rock and cement particles. These forces exert a destructive influence on particles with weaker cementation strength, leading to localized damage within the grouting reinforcement body. As temperatures rise, the water within the grouting reinforcement body thaws, resulting in the release of freezing stress and the migration of water. With increasing freeze-thaw cycles, these localized damage zones gradually coalesce into microcracks. The continuous propagation of these microcracks leads to a progressive reduction in the strength and stiffness of the grouting reinforcement body, ultimately causing particle fracture and spalling.  
During the freeze-thaw cycle, the freezing and thawing of water in the pores lead to the expansion of the original pores and fissures and the formation of new fissures. The evolution law of internal pores in grouting reinforcement body can be obtained by nuclear magnetic resonance T\({}_{2}\) spectrum [42]. In this study, with the increase of the number of freeze-thaw cycles, the T\({}_{2}\) spectrum curve of the sample continued to grow upward and the NMR signal intensities of the three peaks continued to increase. With the increase of the number of freeze-thaw cycles, the three peaks all showed a rightward trend, which was manifested in the pore characteristics of micropores increasing and transitioning to mesopores, mesopores increasing and transitioning to macropores, and macropores increasing continuously. The change of the pore structure directly leads to the decrease of the macroscopic mechanical properties of the grouting reinforcement body.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 2 Background and Overview of WSNs
### LEACH-Based Routing Protocols in WSNs
#### 2.3.2 Overview of Several Existing Surveys  
Many surveys [30; 31; 32; 33] have been conducted to investigate the LEACH routing protocol and its variants for WSNs. In [30], [PERSON] et al. conducted a review that discussed the successors of the LEACH routing protocol. The authors classified protocols by using the category of data transmission. More-over, the paper presented more than 60 LEACH variants and discussed the advantages and drawbacks of each routing protocol. The authors compared the protocols based on nine different parameters. However, the limitation of [30] is that the used classification was based only on single-hop and multi-hop communication without considering any other categories.  
In [31], [PERSON] and [PERSON] provided a short overview of the LEACH protocol and its improvements. The protocols were compared in terms of node mobility, location awareness of nodes, and method of data transmission between CHs and the BS. The limitation of [31] is that the authors discussed routing protocols based on LEACH without using any classification. In [32], [PERSON] et al. focused on the descendants of the LEACH protocol that overcome the non-uniform distribution of CHs in LEACH. This work was extended to investigate other hierarchical routing protocols and compare them with the LEACH protocol depending on the energy consumption. The limitation in [32] is that the authors only discussed six variants of the LEACH protocol without any classification. They merely presented the variants as solutions of the non-uniform distribution of CH in LEACH.  
In [33], [PERSON] conducted a survey of various LEACH-based protocols. The author selected several LEACH-based protocols and conducted a structural analysis of them. In addition, the survey in [33] compared routing protocols on the basis of many comparison metrics, such as mobility, scalability, hop limit, energy efficiency, and use of localization information. Meanwhile, the survey [33] discussed only sixteen protocols without any classification; it merely presented the protocols briefly. A comparison of existing surveys is shown in Table 3.  
In this survey, a clear and comprehensive classification of LEACH variants is proposed with a focus on the criteria that are used to select a CH and the criteria that are used to form clusters in LEACH-based protocols. This survey is completely different from the above mentioned surveys that focused either on a certain classification of LEACH-based protocols or on the general design matters and performance improvements of LEACH-based routing protocols.
## 5 Results and Discussion
### Results with the Structural Dissimilarity Measure (DSSIM)
Our method produces a \(\lambda\) value for each point of the image adapted to the situation, as shown in the examples in Table 6 with respect to the DSSIM evaluation criterion. Finally, we compared the p(x)-Laplacian with our control operator to other smoothing models in the literature, still based on the same principle of degradation of all images in the datasets. Our proposal gives better results, although for the KODAK image database, we still have roughly equivalent results in terms of the PSNR evaluation criterion. The contribution of an adaptive controller of the \(\lambda\) parameter of the p(x)-Laplacian operator has not yet been studied in the literature. Our contribution, which aims to control its smoothing strength according to the locations of an image, shows an improvement in the results obtained using this operator. In a final study, we used two noise generation methods from the literature, again based on the image databases Berkeley, KODAK, and USC-SIPI. These methods consist of creating random additive noise to the images based on a probability law for the distribution, such as the Gaussian distribution or the Poisson distribution. The results of applying these methods and then correcting them using the methods presented in this work are shown in Tables 9 and 10. Overall, we can see that the proposed method ranks second only to the Tchumperle method. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average Time in Seconds**} \\ \hline  & **Berkeley Databases** & **KODAK Databases** & **USC-SIPI Databases** \\ \hline
10\% & 601.80 & 854.96 & 505.35 \\
20\% & 464.79 & 966.35 & 423.26 \\
30\% & 517.82 & 928.12 & 601.09 \\
40\% & 471.24 & 911.41 & 505.01 \\
50\% & 538.31 & 741.18 & 367.47 \\
60\% & 540.24 & 794.21 & 428.84 \\
70\% & 527.51 & 657.88 & 520.70 \\
80\% & 468.22 & 866.27 & 553.07 \\
90\% & 437.38 & 758.04 & 603.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Presentation of the average execution times of the adaptive p(x)-Laplacian model on the image databases used in the experiment.