esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Main Result  
We used the indicators mentioned earlier to provide the results, and Table 2 reports the results of our model on the FB15K dataset, as well as the comparison results with some baseline models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Entities** & **Relations** & **Train** & **Valid** & **Test** \\ \hline FB15K & 14,951 & 1345 & 483,142 & 50,000 & 59,071 \\ FB15K-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of FB15K and FB15K-237 datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MR** & **MRR** & **Hits@10** & **Hits@3** & **Hits@1** \\ \hline TransE & - & 0.463 & 0.749 & 0.578 & 0.297 \\ ComplEx & - & 0.692 & 0.884 & 0.759 & 0.599 \\ DistMult & 42 & 0.798 & 0.893 & - & - \\ ConvE & 51 & 0.657 & 0.831 & 0.723 & 0.558 \\ RotatE & 40 & 0.797 & 0.884 & 0.830 & 0.746 \\ RGCN & - & 0.696 & 0.842 & 0.760 & 0.601 \\ KTAT & **38** & **0.808** & **0.897** & **0.839** & **0.766** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of link prediction on FB15k datasets.  
In Table 2, all baseline model data except for RGCN are from [PERSON] et al. [29], and RGCN data are from [PERSON] et al. [18], where the data that perform best in this evaluation indicator are highlighted in bold. According to the data presented in the table, we can see that our knowledge representation model shows stronger performance on the FB15K dataset when conducting link prediction experiments. Specifically, our model outperforms the baseline models in all indicators, and there are slight improvements in the MR, MRR, and Hit@N metrics compared to the second-ranked model. This result demonstrates the effectiveness of our model, and shows the higher robustness in a complex network structure. In addition, it demonstrates that attention mechanism and entity type information could improve the accuracy of predictions of the relevance between entities.  
The data sources of the baseline model in Table 3 are the same as those in Table 2 due to the removal of inverse relationships in the FB15K-237 model, and the performance of the knowledge representation model in link prediction tasks is more challenging. From the experimental results in Table 3, it can be found that our model outperforms the baseline model in all indicators except for MR in the FB15K-237 dataset, and its performance in MR indicators is only second to the RotatE model. Specifically, the KTAT model showed a 47.3% improvement in MRR metrics compared to the second-ranked model. Knowledge representation methods based on neural networks typically have better representation capabilities in graph structured data. The experiments show that fusing the graph attention mechanism and node type information can improve the performance of the model, which has certain advantages compared to baseline models.
## 9 Conclusions  
In this paper, we have introduced the Mobile Health (mHealth) Predictive Outbreak COVID-19 (mPOC) system, which utilizes a cutting-edge wearable device that can be utilized by individuals from all walks of life. The centerpiece of this system is a smartphone-driven physiological watch called Bio-Watch, which captures and tracks users' physiological signals using both bottom-up (individuals first) and top-down--from public health and the Outbreak Tracking Center (OTC)--approaches.  
By merging the top-down and bottom-up approaches, we believe we have found a way to effectively address fast evolving future pandemics and provide fast and efficient support to affected individuals. The algorithm described in this paper, which operates through three recursive stages, calculates the user's likelihood of being infected based on two formulas: the Predictive Exposure Index (PEI) and the Deterioration Risk Index (DRI). Visual and auditory warnings are then issued to inform users of their risk of infection.  
We have also discussed the feasibility of this approach, aiming to revolutionize pandemic outbreak management by utilizing existing systems and sensors with minor upgrades to devices and communication protocols. The mPOC system makes optimal use of Device-to-Device (D2D) communication, which is supported by current 4G/5G networks. From a performance perspective, we simulated the behavior of the mPOC system using the LabView Community Edition. The results demonstrate mean accuracy values in correctly informing mobile users of risks, ranging from 92.8% for movements between Low-Risk Locations (LRL), 87.08% for movements from LRL to High-Risk Locations (HRL), 84.84% for movements within HRL, and 84.12% for movements from HRL to LRL. The decline in accuracies for movements within HRL can be attributed to the increased utilization of the algorithm to capture, transmit, and interpret parameters in high-risk environments.  
The distinctive feature of the proposed method is its focus on the well-being of affected individuals and the ability to monitor them with accuracies close to the gold standard (>90%), which was previously unattainable. Our mPOC system has the potential to revolutionize pandemic management by providing timely and accurate information to individuals as well as public health agencies, thereby enabling early detection and intervention.  
Further research and development in this field will allow us to refine the mPOC system and explore additional functionalities, such as contact tracing. By continually improving and advancing wearable IoMT technology, we can enhance the efficiency and effectiveness of outbreak prediction and monitoring, ultimately saving lives and mitigating the impact of future pandemics.  
This research received no external funding.  
**Data Availability Statement:** Not applicable.
## 4 Experiment and Analysis
### Experiment Setup and Results
#### 4.2.2 Verification of GCN Layers  
In addition, this paper also conducts a comparative experiment on the number of GCN layers as a way to verify whether stacking GCN layers improves the relation extraction model in this paper. The experimental results are as follows:  
The experimental results in Table 4 show that the number of GCN layers is improved from 1 to 2, and there is no improvement beyond 2 layers, but the effect will decrease. 2-layer GCN can aggregate feature information of most nodes in the network to the target node. If the number of layers is deeper, the objects of aggregated features of each node will be heavily duplicated, resulting in a weak differentiation ability of the trained embedding, which is not good for the later classification loss function. Therefore, the graph convolution module in this paper uses 2-layer GCN.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 2 Microbial Polymers
### Pullulan  
Pullulan is a fungal-based microbial biopolymer that belongs to the family of exopolysaccharides. It is extracted with fermentation approaches by utilizing the fungal strain _Aureobasidium pullulans_. Pullulan is comprised of replicating units of maltotriose adjoined jointly by \(\alpha\)-(1,6) glycosidic joinings. Maltotriose is an oligosaccharide, which is further composed of three glucose units adjoined by (1,4) glycosidic joinings [171]. Elevated fermentation-broth viscosity, melanin coloring, and pullulanolysis during fermentation are the main issues encountered during the manufacturing of pullulan [172]. Other than this, the high cost of feed is a major challenge involved in producing pullulan. To overcome this, inexpensive lignocellulosic substances can be used as carbon sources for microbial fermentation [173]. Pullulan can endure high heat loads, has a versatile range of viscosities and solubilities, and is generally regarded as safe by monitoring authorities. It embraces strong adhesive attributes and has the capacity to develop non-odorous, semi-opaque, and oxygen-proof films [125; 174]. Pullulan exhibits applications in the bioremediation of industrial waste streams [29], biosorption of heavy metals [67; 175; 176], harmful dyes [177; 178; 179], antibiotics [180; 181], and other pollutants [182] in water due to its robust adhesive attributes. The use of synthetic polymers for food packaging has increased over time due to their mechanical and thermal attributes, but the decomposition of these polymers leads to the production of harmful gases [183]. One of the major uses of pullulan is to make antimicrobial films to preserve food [184; 185; 186; 187; 188; 189].
###### Abstract  
The article presents novel hardware solutions for new intelligent sensors that can be used in wireless sensor networks (WSN). A substantial reduction of the amount of data sent by the sensor to the base station in the WSN may extend the possible sensor working time. Miniature integrated artificial neural networks (ANN) applied directly in the sensor can take over the analysis of data collected from the environment, thus reducing amount of data sent over the RF communication block. A prototype specialized chip with components of the ANN was designed in the CMOS 130 nm technology. An adaptation mechanism and a programmable multi-phase clock generator--components of the ANN--are described in more detail. Both simulation and measurement results of selected blocks are presented to demonstrate the correctness of the design.  
wireless sensor networks; intelligent sensors; artificial neural networks; ASIC; CMOS technology; low power solutions; parallel data processing +
Footnote †: journal: Sensors
# A Novel Energy-Saving Route Planning Algorithm for Marine Vehicles
## 2 Formulation of the Energy-Saving Problem  
### Optimization Objective  
Route planning technology is one of the core issues in the field of marine vehicles. It aims to find an optimal or suboptimal route from the initial state to the target state in the marine environment, and the optimal route here takes the distance as the evaluating indicator. Route planning mainly includes two parts: one is to build the environment model, and the other is to search for the optimal route.  
Energy-saving of ships is among the key factors of route planning. Energy consumption is particularly crucial in the route planning of marine vehicles. Especially, when the route is on a large-scale marine environment, the problem of energy consumption becomes prominent. The main purpose of this paper is to study the optimal route planning of the ship's energy consumption, taking accounting into ocean current. Therefore, the energy consumption model of the ship in the working environment is its crucial component.  
The environment model is an abstract description of the marine space that the ship is in. Building the environment model is a process that describes the realistic environment that the ship is in as a proper abstract model by utilizing a set of rules, which is performed before the ship implements the route planning algorithm. Route search is a process that searches for an eligible and feasible route from the environment model. By building the ocean current model, this paper utilized the improved ant colony algorithm to find the route that has the lowest energy consumption of ocean current.
# Detail-Preserving Shape Unfolding
## 2 Related Work
### Shape Unfolding without Detail Preservation  
[PERSON] and [PERSON] [11] took the pairwise geodesic distance as input to generate the coordinates of all vertices that preserved the specified distances. Two strategies were presented in their paper. One was to calculate the centralized squared geodesic distance matrix and then eigen-decomposition was applied to obtain the resulting shape. The other was to produce the result in a least squares sense by using scaling by majorizing a convex function(SMACOF) to minimise the stress [12]. Unlike the least squares MDS method, which matched the resulting Euclidean distances to the exact geodesic distances, [PERSON] et al. [26] only matched the ordering of distances. However, these algorithms are not applicable in practice for meshes with hundreds of thousands as the computation of geodesic distance is timing-consuming.  
The fast MDS method [14] and accelerated MDS method [27] were proposed to improve the calculation efficiency of geodesic distance from different views. The former projected the geodesic distance to Euclidean space one dimension at a time and the latter accurately approximated the pairwise geodesic distance maps through farthest point sam pling [30]. However, these methods still need to calculate the pairwise geodesic distance. In addition, the results generated by these methods have significant geometric distortions in Euclidean space, which affect the performances of context-based shape retrieval and shape matching. [PERSON] [31] used graph Laplacian matrix, which encoded local geometric and topological properties of a mesh, to generate canonical form of 3D shapes. [PERSON] et al. [32] extend the idea of heat kernel signature to robust isometry-invariant volumetric descriptors for shape retrieval. Similar to the classical MDS method [11], eigenvalues and eigenvectors of the Laplacian matrix in these methods were used to obtain the canonical form, which were called as Global Point Signatures. However, they still suffer from serious geometric distortions. In contrast, our approach applies the local rigid energy which effectively avoids this issue.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Scheme Construction
#### 4.2.2 Data Encryption  
After system setup, smart meters and the concentrator establish a path key, which is unique for each aggregation path. Each smart meter then delivers encrypted metering data to the concentrator following the aggregation path. A new path key is established whenever the aggregation path is changed (e.g., in case of the change of a smart grid network topology).
# Strontium Ferromolybdate-Based Magnetic Tunnel Junctions
MTJs with an area of 30 \(\times\) 30 \(\mu\)m\({}^{2}\) were fabricated by means of SFMO thin films deposited on Ba\({}_{0.4}\)Sr\({}_{0.6}\)TiO\({}_{3}\)-buffered STO substrates [5]. Native SrMoO\({}_{4}\) oxide formed by surface oxidation was used as a tunneling barrier, and a 50 nm thick Co film shunted by Au served as a counter-electrode. Junction delineation was carried out using standard UV lithography and ion milling. The structure possessed a TMR of 10% at 4.2 K. MTJs consisting of two SFMO electrodes separated by an STO barrier were described in [6,7]. Trilayer SFMO/STO/SFMO MTJ devices of 40 \(\times\) 40 \(\mu\)m\({}^{2}\) area were created on an STO buffered Si (100) wafer. The large TMR of \(\sim\)7% of the devices obtained at RT was ascribed to spin-dependent tunneling through the sufficiently thin (\(\sim\)2 nm) STO barrier. A comprehensive review of manganite-based magnetic tunnel structures [8] addressed the application of combined external factors (transport current, magnetic and electric fields, microwave and optical radiation) as well as comparing current-perpendicular-to-plane and current-in-plane geometries. However, La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\) thin films undergo a transition to the ferromagnetic state already at about 300 K. The discovery of the coherent tunneling effect at the Fe(100)/MgO(100) boundary, enabling theoretically MR values up to 1000% [9], shifted the research activities in recent years to MTJs employing magnetic electrodes of Fe-containing alloys. In this work, we consider (i) selection of an appropriate tunnel barrier material, (ii) role of the interface roughness and its quantification, (iii) influence of the magnetic dead layer (DL) at the surface/interface, (iv) appropriate models of the TMR, and (v) microstructural promotion of the LFMR. We demonstrate that (i) barrier materials with effective electronegativity lower than that of SFMO are beneficial, (ii) diminution of the magnetic offset field (the latter caused by magnetic coupling due to interfacial roughness) requires a wavy rather than solely a smooth surface, (iii) the DL has a thickness in the order of 10 nm, (iv) the TMR deteriorates due to spin-independent tunneling and magnetically disordered interface layers, and (v) antiphase boundaries along the growth direction promote negative LFMR by reducing charge carrier scattering in the absence of the field.
###### Abstract  
The evolution from 4G to 5G--and eventually to the forthcoming 6G networks--has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastructures. However, the unique characteristics of these networks--extensive connectivity, device heterogeneity, and architectural flexibility--impose significant security challenges. This paper introduces a comprehensive framework for enhancing the security of current and emerging wireless networks by integrating state-of-the-art machine learning (ML) techniques into intrusion detection and prevention systems. It also thoroughly explores the key aspects of wireless network security, including architectural vulnerabilities in both 5G and future 6G networks, novel ML algorithms tailored to address evolving threats, privacy-preserving mechanisms, and regulatory compliance with the EU AI Act. Finally, a Wireless Intrusion Detection Algorithm (WIDA) is proposed, demonstrating promising results in improving wireless network security.  
5G and 6G networks; ML algorithms; EU AI Act; wireless network protection Article
## 3 Results and Discussion
### Scaling under Freezing and Thawing
#### 3.3.2 Influence of the Air Content of Concrete  
The results plotted in Figure 17 reveal a markedly positive effect of the increase of the air content on the performance of concrete subjected to freeze/thaw cycles in the presence of salts. The total accumulated scaled material for Mix 3 (with targeted air content of 4.5%) after 112 freeze/thaw cycles was 1.075 kg/m\({}^{2}\), which is considered not acceptable according to SS 13 72 44 [9]. Mix 6, however, with targeted air content of 6% (and average air content measured according to the pressure method of 5.6%) presented good frost resistance after the end of the test, with an average scaled material of 0.370 kg/m\({}^{2}\).  
According to these results, an increase in the air content from 4.5% to 5.6% changes the salt-scaling resistance of concrete with 50% of GGBS replacement from not acceptable to good. The positive effect of air entrainment in the improvement of the salt-frost resistance of concrete is widely described in the literature. Air entraining agents are used to entrain small-sized, closely-spaced air bubbles in concrete that will accommodate the water expelled from the large capillary pores during freezing, therefore reducing the hydraulic and osmotic pressures caused by freeze/thaw cycles, and, consequently, the chance of damage [12]. In fact, the analysis of the other air pore parameters measured with the AVA shows a clear relation between the increase of air content and the decrease in the spacing factor. The spacing factor changes from a mean value of 0.215 mm for Mix 3 to 0.120 mm for Mix 5. The pore specific surface is also higher for Mix 5 (22.7 mm\({}^{-1}\)) than for Mix 3 (19.8 mm\({}^{-1}\)), which reveals an  
Figure 16: Mean values of the accumulated mass of scaled material per area of freezing surface after 28, 56 and 112 cycles for concrete with different amounts of GGBS replacement.  
Figure 17: Mean values of the accumulated mass of scaled material per area of freezing surface after 28, 56 and 112 cycles for concrete with 50% GGBS and different air content.  
average pore size lower for Mix 5 than for Mix 3. It should be noted, however, that the values obtained with the AVA for the total air content of these mixes were markedly higher than those obtained according to SS-EN 12350-7, as presented in 3.1.2. It would be important to determine the actual air content in the hardened concrete mixes, in order to assess if the actual air contents are indeed much higher than the 5.50% and 5.60% values obtained according to the pressure method, or, instead, if it is actually possible to obtain a similar air void structure and adequate salt-frost scaling resistance with an air content between 5% and 6%.  
These results show, nevertheless, that it is possible to produce concrete with GGBS replacement levels up to 50% of the cement content that presents good salt-frost resistance, as long as proper air entrainment is provided.
# Can Biochar Improve the Sustainability of Animal Production?
## 1 Introduction
However, there still remains research gaps in the areas of utilizing biochar modifications to target certain compounds/pollutants and biochar interactions with microbial communities. Figure 2 outlines the scope that this paper will address for biochar inclusion into animal production systems. The objective of this review is to describe \begin{table}
\begin{tabular}{c c c} \hline
**Gaseous Species** & **Sources** & **Negative Impacts** \\ \hline Methane (CH4) & Enteric fermentation, manure management & Global warming, 25x CO2e (GWP100) 1  \\ Carbon dioxide (CO2) & Animal feed, on farm energy & Global warming, 1x CO2e \\ \hline Nitro oxide (N2O) & Applied and deposited manure, manure management & Global warming, 300x CO2e 1  \\  & Applied and deposited manure, manure management & & \\  & Applied and deposited manure, manure management & & \\  & Applied and deposited manure, manure management & & \\ \hline Hydrogen sulfide (H2S) & Manure storage (lagoons, pits, ponds) & Contributes to acid rain; odor; human health & \\  & & hazard at high concentration & \\ \hline Ammonia (NH3) & Synthetic fertilizers, manure, manure land application & Contributes to PM2g formation; soil & \\  & & nitrification and acidification; algal blooms; & \\  & & human respiratory health impacts & \\ \hline Volatile Organic Compounds (VOCs) & & Potential health impacts (eye, nose, and throat & \\ (e.g., alcohols, ketones, esters) & Confined animal housing, manure & irritation); at high concentrations may cause & \\  & & lung irritation, damage to the liver, kidney, or & central nervous system \\ \hline Fine particulate matter (PM2.5) & Confined animal housing, manure or fertilizer land & Microscopic solids or liquid droplets that cause & \\  & application, bedding & human respiratory health impacts upon & \\  & & inhalation; contributes to haze and smog; & \\  & & contributes to acidic deposition in soil and water \\ \hline Odor (e.g., sulfides, volatile fatty acids, & Manure, manure land application, wastewater & Quality of life impacts on & \\ phenol, etc.) & & downward communities \\ \hline \end{tabular}
\end{table} 
Table 1: Primary airborne emissions of concern in animal production [7; 8; 9; 10; 11; 12].