esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach for Improving XML Querying over Wireless Broadcast Channels
## 3 Proposed Work
### Architecture Diagram for Proposed XML Querying Scheme
Each wireless broadcast channel then transmits the assigned XML data partition. To handle user queries, a query routing mechanism is used. This includes a Query Router, which routes user queries to the appropriate channels, and a Query Interface, where users enter their queries. The routing logic directs queries to the appropriate channels based on the XML data partitions they contain. After the queries are processed, the system gathers the results from each channel and combines them to produce the final query result. After the query process is completed, the combined query results are returned to the user, ensuring that the query process is completed efficiently and effectively. This architecture ensures the efficient and balanced querying of XML data over wireless broadcast channels by dividing the data, assigning it to channels, broadcasting, and implementing a strong query routing mechanism.
## 4 A Look Toward Privacy in 6G Core Networks
### Implications of AI for Identity Protections in 6G Technology
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{4}{c}{**Current 6G AI Privacy Literature**} \\ \hline
**Paper Title and Citation** & **Focus** & **Description** \\ \hline \multirow{4}{*}{'Privacy-Preserving AI Framework for 6G-Enabled Consumer Electronics’ [62]} & \multirow{4}{*}{Differential privacy, federated learning} & Covers user data privacy concerns in 6G technology and presents a differential privacy and federated learning (FL) framework, offering a solution to safeguard sensitive user data during transmission. Blockchain is utilized to support the FL model in a decentralized manner, reducing vulnerabilities.  
\begin{tabular}{c c} \hline \hline \multirow{4}{*}{'AI and 6G Security: Opportunities and Challenges’ [63]} & \multirow{4}{*}{Intelligent security privacy provisions} & Discusses the use of AI to catch and deter threats in 6G networks. Mentions the use of AI as a double-edged sword, and the importance of maintaining user privacy given the huge influx of data consumed or produced by models. Underscores the importance of the “Ethics by Design” of AI systems.  
\begin{tabular}{c c} \hline \hline \multirow{4}{*}{'Personal Data Protection in AI-Native 6G Systems’ [64]} & \multirow{4}{*}{AI privacy compliance, privacy by design} & Identifies privacy risks with AI-enabled 6G systems, such as privacy breaches, data misuse, lack of transparency, and bias. Solutions to these issues include regulatory compliance, bias audits, and explainable AI-deployed systems.  
\end{table} 
Table 5: Comparison of the current literature regarding AI, user identity protections, and privacy in 6G technology.
# Chemical Compositional Changes in Over-Oxidized Fish Oils
## Discussion
### Specific Groups of Secondary Oxidation Products and Biological Activity
#### 4.4.1 Volatiles  
Volatiles are frequently measured as an indicator for oxidative quality of marine oils, although no consensus currently exists about which volatiles should be monitored, since the profile can vary depending on oil type and oxidation mechanism. With the exception of 1-penten-3-ol, the volatiles tested in the present study are conjugated aldehydes that will react with the _para_-anisidine reagent and contribute to p-AV value. The formation of unesterified secondary oxidation products originating from esterified LCPUFA, such as various oxygenated \(\alpha\),\(\beta\)-unsaturated aldehydes, has been shown to occur in thermally treated aerated edible oils [71]. Such conjugated aldehydes are bio-available upon oral intake, and their electrophilicity toward proteins and nucleic acids is linked to cytotoxic and genotoxic activity [72; 73]. The reactive aldehydes can react with sulfhydryl groups in proteins, thereby interfering with cellular metabolism and physiological processes. Under Condition A, 1-penten-3-one, (\(E\))-2-pentenal, 1-pentene-3-ol, (\(E\),\(E\))-2,4-heptadiene, and (\(E\),\(Z\))-2,6-nonadieneral were all detectable during the over-oxidation of hoki liver oil at 15 and 30 days. Hence, these volatiles will likely have been present in the oil administered to rats in the study by [PERSON] et al. In a future study, it would therefore be interesting to assess which of these substances, at the concentrations uncovered in this study and administered in correctly allometrically adjusted doses, display perinatal toxicity in pups and dams. To draw meaningful conclusions about the biological risk resulting from exposure to reactive oxidation products, such in vivo studies should also evaluate the dose in relation to endogenous antioxidant defenses and phase 2 metabolism of reactive oxidation products [74; 75].  
Furthermore, biological activity that is unrelated to electrophilic reactivity should be considered. For example, (\(E\))-2-pentenal is a strong irritant of sensory nerves, being a ligand for the TRPA1 channel [76]. Even though these volatiles are highly reactive, they do not show an indiscriminate reactivity, because steric factors are important as well; for example, the \(\alpha\),\(\beta\)-unsaturated oxo-compounds 1-penten-3-one and (\(E\))-2-pentenal inhibit glucose 6-phosphatase, but conjugated dienals do not [77]. Limitations of the present study are recognized, since a wider array of LCPUFA-derived volatiles can be formed in fish oils [78]. The major low molecular weight volatile acrolein, the dialdehyde malonaldehyde, as well as 4-hydroxy-\(\alpha\),\(\beta\)-unsaturated aldehydes such 4-hydroxy-hexenal, for which cytotoxic effects are documented, were not covered by the present study [78; 79]. From the perspective of refined anchovy oil, further research could focus on volatiles formed particularly under conditions of thermal stress in the dark.
# The Effect of Porous Media on Wave-Induced Sloshing in a Floating Tank
## 2 Methodology
### Eulerian-Lagrangian Method  
By applying [PERSON]'s second identity to the Laplace equation, the boundary integral equation (BIE) for the fluid boundary can be obtained as:  
\[c_{p}\phi_{p}=\int_{S}\left(\frac{\partial G}{\partial n}\phi-G\frac{\partial \phi}{\partial n}\right)dS \tag{11}\]  
where \(G\) is the fundamental solution with source point \(p\) and \(c_{p}\) is the resulting flux. In this paper, BIE is discretized, and the associated integrals are implemented by BEM. Therefore, Equation (11) can be rewritten in matrix form as:  
\[\textbf{A}\{\phi\}=\textbf{B}\{\phi_{n}\} \tag{12}\]  
where **A** and **B** are the kernel matrices and \(\{\phi\}\) and \(\{\phi_{n}\}\) are the vectors containing the potential and the normal velocity, respectively, at all of the boundary nodes. Equation (12) represents a mixed-type BVP with \(\phi\) given on the free surface and \(\phi_{n}\) on the wetted boundaries. The free-surface particle is tracked via a second-order Taylor series expansion, assuming a small time step, \(\Delta t\), as follows:  
\[\textbf{R}(t+\Delta t)=\textbf{R}(t)+\Delta t\frac{D\textbf{R}}{Dt}+\frac{ \Delta t^{2}}{2}\frac{D^{2}\textbf{R}}{Dt^{2}}+O\Big{(}\Delta t^{3}\Big{)} \tag{13}\]  
\[\phi(t+\Delta t)=\phi(t)+\Delta t\frac{D\phi}{Dt}+\frac{\Delta t^{2}}{2}\frac{ D^{2}\phi}{Dt^{2}}+O\Big{(}\Delta t^{3}\Big{)} \tag{14}\]  
The first-order derivative in Equation (13) is obtained by the kinematic boundary condition, while the first-order derivative in Equation (14) can be obtained by the momentum equations, i.e., Equation (7) for water waves and Equation (8) for sloshing water. The second-order derivatives of \(\textbf{R}\) and \(\phi\) can be obtained as:  
\[\frac{D^{2}R_{i}}{Dt^{2}}=\frac{\partial^{2}\phi}{\partial t\partial\textbf{x }}+\textbf{u}\
abla.\frac{\partial\phi}{\partial\textbf{x}_{i}} \tag{15}\]  
\[\frac{D^{2}\phi}{Dt^{2}}=\textbf{u}\cdot\frac{Du}{Dt}-g\frac{D\eta}{Dt} \tag{16}\]  
A new unknown variable \(\phi_{t}=\partial\phi/\partial t\) appears in the equations above. Since \(\phi_{t}\) also satisfies the Laplace equation, another mixed-type BVP involving \(\phi_{t}\) and \(\phi_{tn}\) arises and needs to be solved. The Dirichlet condition on the free surface is given by:  
\[\phi_{t}=\frac{D\phi}{Dt}-|\
abla\phi|^{2} \tag{17}\]  
The Neumann condition on the wetted boundaries is given by:  
\[\phi_{tn}=\ddot{\textbf{x}}\cdot\textbf{n}-(\textbf{u}\cdot\
abla)\phi_{n}, \tag{18}\]  
Figure 1: (**a**) Configuration of a floating body undergoing wave excitations with a water tank, installed with porous media, attached above; (**b**) degree of freedom of the floating body motion.  
while the Neumann condition on the radiation boundary is given by:  
\[\phi_{tn}=c\frac{\partial^{2}\phi}{\partial s^{2}} \tag{19}\]  
where \(\ddot{\mathbf{x}}\) is the acceleration of the node on the wetted boundary and \(s\) denotes the tangential direction of the boundary. Fortunately, the domain geometry remains unchanged at the current time step, so the matrices \(\mathbf{A}\) and \(\mathbf{B}\) in Equation (12) can be reused.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 2 Energy Constraints--A Persistent Problem
### Power Enhancement in mm-wave--A Cellular Perspective  
The utilization of directive antennas enables 5G wireless networks for simultaneous power and data transfer. The higher bandwidth usage allows mobile devices to send and receive a vast amount of data. [PERSON] et al. made a case study by utilizing different bands of mm-waves, such as 28 GHz, 38 GHz, 71-26 GHz, and 81-86 GHz for a 5G enhanced local area network [16]. The investigation to collect power from cellular networks has already started [17]. This technique has the potential to supply sufficient energy to devices under the network coverage. Likewise, RF-based energy harvesting in the cognitive cellular networks was proposed in [18]. The work considers that secondary users can harvest energy from the mobile base station (primary transmitter) channel and store it. When the  
Figure 4: Overview of our survey paper.  
cellular channel becomes idle, the secondary users can use the accumulated energy to fulfill power requirements.  
[PERSON] el al. presented numerical results to show that mm-wave frequencies possess the potential to increase the achievable rates and harvested power, compared to conventional cellular networks [19]. A fully functional mm-wave identification system at frequencies above 70 GHz is introduced for the RFID system [20]. This work discusses the size of the transponder in the receiver, while considering backscattering operation, to enable BackCom in low power devices. The concept of BackCom beamforming is introduced through continuously variable antenna loads [21]. [PERSON] et al. introduced wireless power transfer for mm-wave cellular networks and found users with high energy storage [22]. The energy storage power was examined and recharged with mm-wave base stations. Subsequently, energy harvesting performance for low power devices, energized by the mm-wave cellular networks, was analyzed to show improvement in energy coverage with narrower beams, during beam alignment between transmitter and receiver [23]. Moreover, this work also indicates that wider beams provide more energy if receivers are not aligned with any specific transmitter.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Nano-/Micro-Precipitation Method  
The nano-/micro-precipitation method, also called the solvent displacement [230] or dialysis method [231], is widely employed to produce polymeric particles as drug carriers [72]. The main difference between this method and the emulsion method is that the particle formulation process is not under high energy/shear, but is driven by spontaneous diffusion of the organic solvent into the anti-solvent, normally water. In brief, particle production can be described by the following steps: (i) choosing a water miscible solvent as the polymer/drug dissolving phase; (ii) mixing the polymer/drug phase with water and the droplets or particles are formed through the process called the \"Ouzo\" effect [232]; and (iii) removing the organic solvent by evaporation, extraction, or a combination of both [233; 234; 235; 236].  
Because the solvents selected for dissolving the polymer/drug should be easily removable and water miscible, the commonly preferred solvents are acetone, acetonitrile (ACN), dimethylformamide (DMF), dimethylsulphoxide (DMSO), and tetrahydrofuran (THF) [235]. The mixing process can also be categorized into three types: (a) pouring anti-solvent (water) into the organic solution in one-shot, (b) adding the water into the organic solution dropwise, or vice versa, and (c) slow dialysis of the organic solution with water. For producing nanoparticles, the concept of the \"Ouzo\" region (Figure 6) has been proposed [237]. Within the \"Ouzo\" region, nanoparticles are more prone to be produced rather than microparticles [238; 239], and vice versa, which is a vital strategy in fabricating particles with a desirable size range.  
Overall, the nano-/micro-precipitation method is to some extent, an alternative or modification of the classic emulsion method. Additionally, as this technique is straightforward, fast, and easy to duplicate in practice, commercially available equipment is available for large scale production, making it a preferred and recommended method [240].
## 6 Conclusions  
This systematic review combined large language model text mining with expert appraisal to map the state of predictive maintenance research for industrial equipment. A structured search retrieved and filtered articles on bearings, compressors, hydraulic pumps, conveyor belts, and cable systems. Dimensionality reduction and clustering techniques revealed the most influential methodological threads--deep learning, physics-informed hybrids, advanced signal processing, and classical machine learning models--while expert validation confirmed topic relevance and research quality.  
Four trends dominate contemporary work. First, deep neural architectures increasingly deliver early fault detection and fine-grained classification across diverse assets. Second, IoT and big data infrastructure enable high-frequency, large-scale data acquisition for real-time analytics. Third, hybrid physics-data approaches improve interpretability and resilience, especially where labelled data are scarce. Fourth, traditional statistical and ML algorithms (random forest, SVM, and \(k\)-NN) persist in small-sample or resource-constrained settings, providing lightweight baselines and interpretable benchmarks. Remaining-useful-life estimation--often via supervised or Bayesian models--has gained prominence alongside rising interest in explainability and concept-drift adaptation.
## 4 Numerical Results
### Estimation Results Based on Simulation Data
#### 4.1.3 Processing Time  
In this section, we discuss the processing time of RU-EPD and C-EPD with different input sizes from \(10^{2}\) to \(10^{5}\) points and evaluate the effect of the input sizes in terms of diameter estimation. Figure 7 shows the results of processing time on the PC with CPU Ryzen 9 5900x (3.7 GHz) and 48 GB of RAM. The processing time of RU-EPD and C-EPD was almost similar for all input sizes. The processing time increased with the input size. At the highest input size, the average processing time was about 0.54 s for both RU-EPD and C-EPD and did not exceed 1.1 s. It is suitable for post-processing systems where data are processed on a PC after inspection. For the input size \(10^{3}\) and below, the processing time did not exceed 0.03 s, which is suitable for real-time systems. On the other hand, Figure 8 shows the major- and minor-axis errors. The estimation error with both RU-EPD and C-EPD increased as the input size was reduced. However, RU-EPD still produced unbiased estimations compared to C-EPD. At \(10^{3}\), RU-EPD still kept the estimation error below 3.4 mm, which is acceptable in real-time systems.  
Figure 6: Robot pose estimation errors of C-EPD and RU-EPD across different sensor depth errors.  
Figure 7: Comparison between processing time of C-EPD and RU-EPD under varying input sizes.
## 6 Evaluation of Results
This reduction was particularly notable during months of lower ionospheric activity, reflecting the models' improved accuracy in avoiding false alarms as the solar cycle waned. The lower FAR in 2020 across all models suggests a heightened ability to discern non-events, which is crucial for reducing operational inefficiencies in real-world applications. The CSI improvements from 2015 to 2020 underscore a refinement in the models' overall predictive accuracy. The year-on-year increase in CSI during the months of January and March, despite their atmospheric variability, indicates an enhanced alignment of the models with the actual ionospheric behavior, likely attributed to improved model training and parameter optimization. The ascending POD values from 2015 to 2020, particularly for ConvGRU-L\({}_{\text{C}}\), highlight its robust detection capabilities. Improved detection rates are critical for ensuring that significant scintillation events are captured and accounted for in operational settings, particularly in navigation and communication systems reliant on ionospheric conditions. The higher TSS scores in 2020, especially notable in the ConvGRU-L\({}_{\text{C}}\) model, illustrate a sophisticated ability to differentiate between scintillation and non-scintillation conditions accurately. The progressive improvement in TSS across the years is indicative of the models' enhanced discrimination power, which is essential for reliable ionospheric forecasting. The analysis indicates that the models' performance varies not only with solar activity but also seasonally. The better performance in July across all years and models may be attributed to more stable geophysical and solar conditions, which provide a consistent environment for model predictions. Conversely, the variability observed in March could be linked to equinox-related ionospheric disturbances, which are notoriously difficult to model due to their dynamic nature. The year 2015 showed more pronounced ionospheric activity due to the solar maximum, leading to higher instances of scintillation that challenge model accuracy. In contrast, the quieter solar conditions in 2020 allowed for more stable model outputs and lower error rates, reinforcing the impact of solar activity levels on prediction success. This expanded analysis not only reinforces the need for adaptive modeling strategies across different solar phases but also highlights the importance of considering seasonal variations in ionospheric prediction models.
## 5 Case Study
### Second Scenario  
The goal in this second scenario is the same as seen in the first scenario, but the problem to tackle is more complex due to the higher number of samples (12,390) and labels (14 balanced) in the classification problem. Adding samples and classes makes visualizations more difficult for domain experts to follow and makes it more difficult for experts to apply their previous knowledge.  
The domain experts, by using the same process as in the previous scenario, built a decision tree to compare it with the decision tree automatically generated by scikit-learn. The resulting trees are available in Figure 15 in low resolution due to format restrictions. For more details, check [[https://github.com/mohedano-munoz/decision_tree_builder/tree/main/decision_trees_movements](https://github.com/mohedano-munoz/decision_tree_builder/tree/main/decision_trees_movements)]([https://github.com/mohedano-munoz/decision_tree_builder/tree/main/decision_trees_movements](https://github.com/mohedano-munoz/decision_tree_builder/tree/main/decision_trees_movements)) (accessed on 8 November 2024) to access the complete trees in high resolution. The higher complexity of the automatic tree can be observed in the figure due to the different sizes of the trees and different numbers of nodes. In this case, the performance of the automatic tree is superior, as shown in Figure 16, although only slightly.  
The difference in complexity between these trees is evident; the interactively constructed tree has 217 decision nodes, while the tree generated by scikit-learn has 504 nodes. In the initial stages of constructing the guided tree, the emphasis is placed on variables associated with the lower body's position, such as the knees, and from these nodes, the tree is further developed. A key distinction is its emphasis on the hip, with the variable 'R_Hip_1' being decisive in 20 of nodes in the guided tree. This variable appears both in the early stages of the tree and in the nodes close to the leaf nodes (see Figure 17). Shoulder rotation is also significant, with 'L_Shoulder_3' and 'R_Shoulder_3' contributing to 35 decision nodes. Furthermore, hand movements play a crucial role in defining motion, as indicated by the variables used in the tree. A detailed count of the most frequently appearing variables in the decision nodes for classifying movements for both trees, for comparison purposes, is provided in Table 2.  
Figure 16: Performance comparison between the tree built by scikit-learn and manually, with 14 classes in the dataset. Each class starts balanced, with 885 samples, and training and testing sets are created with a 70:30 ratio. The results of the manual tree are slightly worse in this case, but the result is a simpler decision tree wherein the domain experts could apply their knowledge.  
Figure 15: Resulting decision trees for the whole dataset with 14 classes. (**a**) Guided Decision tree. (**b**) Scikit-learn decision tree.
# Exploring the Bioethanol Production Potential of _Miscanthus_ cultivars
## 3 Results and Discussion
### Miscanthus Cultivar Hydrolysis and Fermentation  
The determination of cellulose content revealed that all _Miscanthus_ samples contained promising amounts of cellulose for bioethanol production. Dilute acid pre-treatment of various _Miscanthus_ cultivars revealed that xylose (3.9-24 g/L) was the predominant sugar, with low glucose present (<3.4 g/L) (Figure 2A). After enzymatic saccharification, glucose was obtained as the main sugar as the cellulose component in the pre-treated biomass was degraded. The hydrolysate derived from _Miscanthus_ cultivar B (BE04) showed the highest glucose content with a good cellulose hydrolysis yield of 75.8%. Overall, saccharification released 63.7-80.2% theoretical glucose (Figure 2B) from various _Miscanthus_ cultivars.  
A hydrolysate prepared using _Miscanthus_ cultivar B (sample BE04) was selected for bioethanol fermentation assessment using _S. cerevisiae_ NCYC2592. Data showed that a relatively long lag phase (\(\sim\)24 h) occurred when compared with this yeast fermenting an analogous glucose load and yeast media (<6 h) [17]. However, when the cell started to grow, the available glucose was consumed rapidly (Figure 2C). By the end of the fermentation, 13.58 \(\pm\) 1.11 g/L of ethanol was produced from 35.12 \(\pm\) 0.46 g/L of glucose. Overall, a bioethanol fermentation yield of 0.387 g/g sugar was observed. The fermentation yield (75.6% of theoretical yield) is higher than that reported by [PERSON] et al. (2009; 70%) [23], but lower than that reported by [PERSON] et al. (2011; 83.9%) [24]. For efficient bioethanol production, a higher ethanol yield, e.g., over 90% and a higher ethanol titre, e.g., over 80 g/L, should be achieved.