esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge AI for Real-Time Anomaly Detection in Smart Homes
###### Abstract  
The increasing adoption of smart home technologies has intensified the demand for real-time anomaly detection to improve security, energy efficiency, and device reliability. Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. This paper presents an Edge AI-based anomaly detection framework that combines Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) models to identify anomalies in IoT sensor data. The system is evaluated on both synthetic and real-world smart home datasets, including temperature, motion, and energy consumption signals. Experimental results show that LSTM-AE achieves higher detection accuracy (up to 93.6%) and recall but requires more computational resources. In contrast, IF offers faster inference and lower power consumption, making it suitable for constrained environments. A hybrid architecture integrating both models is proposed to balance accuracy and efficiency, achieving sub-50 ms inference latency on embedded platforms such as Raspberry Pi and NVIDIA Jetson Nano. Optimization strategies such as quantization reduced LSTM-AE inference time by 76% and power consumption by 35%. Adaptive learning mechanisms, including federated learning, are also explored to minimize cloud dependency and enhance data privacy. These findings demonstrate the feasibility of deploying real-time, privacy-preserving, and energy-efficient anomaly detection directly on edge devices. The proposed framework can be extended to other domains such as smart buildings and industrial IoT. Future work will investigate self-supervised learning, transformer-based detection, and deployment in real-world operational settings.  
c Article Edge AI for Real-Time Anomaly Detection in Smart Homes  
1  
Footnote 1: [[https://github.com/google/](https://github.com/google/)]([https://github.com/google/](https://github.com/google/))  
In smart home ecosystems, real-time anomaly detection is crucial for identifying irregular patterns that may indicate security breaches, device malfunctions, or unsafe conditions. Implementing anomaly detection at the edge enhances responsiveness and preserves user privacy by minimizing data transmission to external servers. Recent advancements in Edge AI have demonstrated its potential in various applications, including predictive maintenance, energy management, and security monitoring within smart homes [3; 4].
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 2 Antenna System Design
### Antenna Elements  
Inverted F antenna radiating elements are designed using the bottom substrate as a ground plane. The dual-band characteristic is obtained by adding a parasitic element placed in the inner part of the structure and connected to the ground. Several configurations of the driven and parasitic elements were investigated to find out the best topology to maximize frequency bandwidth and gain. Parametric simulations show that the driven element must be placed on the edge of the structure, with the driven element placed at 8 mm in the center of the structure. The parasitic element shorting pin is aligned with the feeding element shorting pin. The width and length of the driven and parasitic elements are carefully optimized to maximize the performance in both bands.  
As shown in Figure 1a, the driven elements are placed on the edge of the ground plane with an optimized length to provide resonance at 868 MHz. The parasitic element is placed inside the structure, and the length is shorter and optimized to operate at 920 MHz. The driven element input impedance is tuned to 50 \(\Omega\) by adjusting the distance between the feed and the shorting pins.  
Four 8 mm diameter holes are inserted into the top substrate to enable the screwing of the antenna structure to the 3U frame. The M3 metallic screws are also used for grounding purposes on the CubeSat metallic structure. The 3D and top view layouts are shown in Figure 1b.  
Surface currents at 870 MHz and 920 MHz are presented in Figure 2. At 870 MHz, surface currents are strong on the driven elements with low intensity on the parasitic elements. At 920 MHz, the driven elements excite the parasitic element and strong current intensity is visible on both branches.  
Figure 1: (**a**) Top view of antenna elements; (**b**) 3D view of the proposed antenna structure.
# Exploring Effective Bio-Cover Materials for Mitigating Methane Emission at a Tropical Landfill
## 2 Methodology  
### Bio-Cover Unit Set-Up and Operation  
Three bio-cover units (1.50 m diameter \(\times\) 1.00 m height) with the schematic shown in Figure 1 were installed at a municipal landfill in Thailand. Each unit contained different packing materials, i.e., sandy loam, compost, or stabilized wastes, of 0.60 m depth providing a material volume of 1.0 m\({}^{3}\). The sandy loam was prepared by mixing with clay soil to obtain a final composition of 55.7% sand, 25.9% of silt, and 18.4% clay. Commercial compost prepared from animal manure and yard wastes was used whereas stabilized waste (>10 years old) was obtained from a closure waste disposal area of the studied landfill. Their chemical characteristics are shown in Table 1.  
Landfill gas extracted from the closed landfill area was supplied at a constant flow rate of 576 L/d, equivalent to an average methane flux of 16 mol/m\({}^{3}\) of media volume/d. The gas pump was operated with battery cells contained in a gas-tight box. The air flow rate was adjusted using a valve, regularly checked and calibrated with a drum-type gas meter (Ritter, TG05/3) to ensure a constant flow rate to the bio-cover. The gas residence time in the bio-cover varied from 0.9-1.3 days depending on its porosity. The gas was supplied through a gas distribution pipe located at the bottom of each bio-cover unit. During the operation, precipitation at the studied site was recorded using an automatic rain gauge station. The temperature and moisture content of bio-cover materials were regularly monitored using portable meters (ECHO, EC-5) and the moisture reduction through evaporation during non-raining periods was determined as about 3% per day. To maintain the optimum moisture content (wet weight basis) in those materials, i.e., 10-20% in sandy loam and stabilized waste and 50-55% in compost found in our previous studies [15,20,21], 20 L of landfill leachate taken from the leachate storage pond with the characteristics shown in Table 2 was sprayed onto bio-cover materials once every two days.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Sandy Loam (SL)** & **Compost (C)** & **Stabilized Wastes (SW)** \\ \hline Bulk density (kg/m\({}^{3}\)) & 1178 & 595 & 189 \\ Porosity (\%) & 55.5 & 71.4 & 75.5 \\ pH & 7.27 & 7.12 & 6.8 \\ EC (μs/cm) & 1766 & 1206 & 285 \\ TOC (\%) & 0.6 & 30 & 71.1 \\ NH\({}_{4}\)+-N (mg/kg) & 1.38 & 4.74 & 0.6 \\ NO\({}_{3}\)–N (mg/kg) & 16.4 & 5.5 & 57.9 \\ TN (g/kg) & 1.08 & 0.18 & 0.15 \\ TP (g/kg) & 0.43 & 1.08 & 0.61 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and chemical characteristics of bio-cover materials.  
Figure 1: Schematic of bio-cover unit.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 2 Database
### Traditional Method  
The process of the traditional method to determine the high-pass cut-off frequency of strong-motion records is shown in Figure 1, which consists of four crucial steps, as follows:  
Figure 1: The process of obtaining the high-pass cut-off frequency (f\({}_{\text{HP}}\) is the high-pass cut-off frequency).  
Step 1: The baseline correction of raw records. The method used here is to remove the first-order function fitted by the record.  
Step 2: The selection of the appropriate high-pass cut-off frequency for filtering to remove the low-frequency noise, starting from 0.01 Hz and increasing the high-pass cut-off frequency with every step of 0.01 Hz.  
Step 3: The integration of filtered acceleration time series into velocity and displacement time series.  
Step 4: The artificial identification of displacement time series curves that are qualified; if they are not, skip to step 2; if they are qualified, output the cut-off frequency.
# A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
## 5 Conclusions  
Targeting the problem that only a few components from VMD contain useful information for blockage identification, and an information gain-based selection technique is proposed. It is not only effective in selecting the feature components containing substantial blockage information but also plays a crucial role in the deep mining of the information. Sound pressure level conversion is performed on the acoustic pressure signals collected from the standard pipe and the pipe installed with a lateral connection to make a comparison. It is found that the sound pressure level is capable of reflecting the local characteristics of pipeline conditions through the mixture of information while enhancing the discrimination between various operating conditions. The RMS entropy is proven to be responsive to blockage changes from the noise-containing acoustic signals, which can thus be used as input to the classifiers for achieving effective identification.  
Given the complexity of pipeline topology and detection environment, as well as the diversity of the pipe defects, further exploration and research are needed, which should cover the following aspects: the sound propagation within pipelines with multiple defects should be explored to discover the pattern thus that a condition prediction model for varying defects can be developed. Advanced active acoustical detection technology requires further study. Such research should involve the development of sensitive acoustic sensors with long-distance availability, selection of valid excitation signal type, wavelength, and frequency, the study of the distribution of sound fields, investigation of the sound attenuation corresponding to various new materials used in civil infrastructures, as well as more advanced data processing techniques.  
Conceptualization, X.Z. and Z.F.; methodology, X.Z.; software, W.D.; validation, X.Z. and Z.F.; formal analysis, X.Z. and W.D.; investigation, X.Z.; resources, J.W.; data curation, Z.F.; writing--original draft preparation, X.Z. and Z.F.; writing--review and editing, J.W.; visualization, X.Z. and W.D.; supervision, J.W.; project administration, Z.F. and J.W.; funding acquisition, Z.F. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under Grant No. 61563024, 51765022 and 61663017.  
Not applicable.  
The data included in this study are all owned by the research group and will not be transmitted.  
The author would like to thank the financial support provided by the National Nature Science Foundation of China. Thanks to my mentor and team for this research.  
The authors declare no conflict of interest.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 2 PSO Algorithm
### Algorithm Implementation  
Step 1: Based on the characteristics of the optimization problem, the algorithm randomly initializes several parameters, including the particle swarm size \(N\), the search dimension \(J\), the upper and lower bounds of the search, the maximum number of iterations \(t_{max}\), the maximum velocity \(v_{max}\), the minimum velocity \(v_{min}\), the inertia weight \(\omega\), and the learning factors (\(c_{1}\) and \(c_{2}\)).  
Step 2: The boundaries are assessed based on the initialized positions and velocities of individual particles, followed by the calculation of fitness values. The adaptation values are then sorted, and the velocity and position of the particles are updated in accordance with Equations (3) and (4).  
Step 3: Based on the updated velocity and position, the optimal position and optimal fitness of both individual and group particles are revised. A determination is made to ascertain whether the specified condition is met; if so, the process advances to Step 4; if not, it returns to Step 3.  
Step 4: Output the optimal solution of the problem.
## 3 Results and Analysis
### Analysis of Model Results
Due to the existence of the scale effect, the large-scale forestry management effect was obviously better [11]. When the carbon sink was less than 5,327,211.8707 tons, FMI had a significant positive impact on the carbon sink (\(p<0.01\)), and the elasticity coefficient was 0.00953. That is, for every 1% increase in FMI, the carbon sink value of the region increased by 0.00953%. When the carbon sink was higher than 5,327,211.8707 tons, FMI had a significant positive impact on the carbon sink (\(p<0.01\)), and its elasticity coefficient was 0.02175. That is, for every 1% increase in FMI, the carbon sink value of the region increased by 0.02175%. The higher the value of the elasticity coefficient of FMI was, the greater the contribution rate of FMI to the growth of the carbon sink, and the higher the investment efficiency. The latter's FMI efficiency was 128.23% higher than that of the former. According to the threshold regression results, Figure 7 was drawn, in which the pink and blue areas are areas with low and high FMI efficiency, respectively. Among the control variables (Models 1, 2, 3, and 4), the GDP has an inverted U-shaped impact on the carbon sink. That is, as the level of forestry output value in each forest region increased, the carbon sink showed a trend of \"rising first, then falling\", following the basic path of the environmental Kuznets curve. In Model (4), the number of employees had a significant positive impact on the carbon sink (\(p<0.01\)), and the coefficient value was 0.03713. Precipitation had a significant positive impact on the carbon sink (\(p<0.01\)), with a coefficient value of 0.22435. That is, for every 1% increase in precipitation, the carbon sink increased by 0.22435%. The results met the tree growth principles and theoretical expectations, thus verifying the logic of the model and the reliability of the results. 4. The FMI shows a law of diminishing marginal benefits for increasing carbon sinks in each forest region, as shown in Figure 8. With the increase in FMI, the effect of increasing the carbon sink showed a downward sloping curve. The result passed the significance test, and the confidence interval of the coefficient value at each point did not include 0. Since this model was the result of nonparametric estimation, the estimation result of FMI on the carbon sink parameters in the individual fixed-effects model should be an oblique upward curve, and the coefficient value should be positive. When the investment amount in the forest region exceeded CNY 100 million, the growth rate of the carbon sink remained at a low level. However, the marginal benefit curve did not intersect or approach 0, indicating that FMI has not yet reached the optimal investment scale, and additional investment should be reasonable.
## 6 Discussion  
This study systematically investigates the hydrodynamic characteristics of a pump-jet-propelled submarine during yaw and sway motions through numerical simulations and experimental validation. The results reveal that in yaw motion, the angle of drift and swirl pump speed significantly influence the lateral force, lateral velocity, and lateral moment of the submarine. As the drift angle increases, both the lateral force and lateral velocity of the submarine noticeably improve, especially under high-speed conditions where the lateral moment growth rate is faster. During sway motion, increasing the vector spray angle significantly enhances the submarine's turning maneuverability, with further enhancement achieved by increasing the speed. Mathematical models describing the variation in submarine speed coefficient and angular velocity coefficient with swirl pump speed are established through least squares fitting, providing a theoretical basis for optimizing the submarine's maneuverability and dynamic performance.  
However, there are some limitations to this study. Firstly, although the adaptive mesh refinement technique used in numerical simulations can effectively model the submarine's motion, handling complex flow fields may introduce computational errors due to grid updates and deformations, particularly under high speed and large drift angle conditions where nonlinear and turbulent flow effects become more pronounced and might affect result accuracy. Secondly, while experimental verification through mooring and simulated cruising experiments validated the numerical results, differences exist between the experi  
Figure 15: Correlation curve between hydrodynamic coefficient and angular velocity ((**A**): relationship between \(Y\) and \(r\); (**B**): the relationship between \(N\) and \(r\)).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(n\) & \(Y_{r}\) & \(N_{r}\) \\ \hline
1500 rpm & \(-1.56\times 10^{-3}\) & \(2.74\times 10^{-4}\) \\
2000 rpm & \(-7.41\times 10^{-3}\) & \(5.13\times 10^{-4}\) \\
2500 rpm & \(-7.79\times 10^{-3}\) & \(4.71\times 10^{-4}\) \\
3000 rpm & \(-4.74\times 10^{-3}\) & \(2.96\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Angular velocity coefficients of underwater vehicles at different speeds.  
mental conditions and actual marine environments, notably overlooking external factors such as waves and currents, which could impact the submarine's practical maneuvering performance. Additionally, the study primarily focuses on the horizontal plane's dynamic characteristics, neglecting vertical movements like pitch and heave. Future research can further explore multi-dimensional motion coupling effects.  
Despite these limitations, the study's outcomes provide crucial theoretical support for the design and optimization of pump-jet-propelled submarines. Future research can integrate more complex marine environmental conditions, consider the coupling effects of multi-degree-of-freedom motion, and enhance model accuracy and applicability through more refined experimental validations.
## 5 Simulation Results
### Simulations of Reflections  
The FDTD method allows one to combine different materials in a single computational domain. Therefore, we consider a simulation of the wave propagating through a vacuum and impinging on a material described by the TF constitutive relations, with the parameter \(\alpha=0.99\). The results, in the form of electric-field distributions in the space for varying time, are presented in Figure 5.  
Figure 4: Comparison between results of FDTD with varying spatial-step size and frequency-domain simulations (\(\alpha=0.98\)). Distance from source: (**a**) \(10\,\mathrm{\SIUnitSymbolMicro m}\). (**b**) \(30\,\mathrm{\SIUnitSymbolMicro m}\). (**c**) \(60\,\mathrm{\SIUnitSymbolMicro m}\). (**d**) \(90\,\mathrm{\SIUnitSymbolMicro m}\).  
The incident wave is almost completely transmitted into the FO material, with only a small reflection, as can be seen in Figure 5b,c. The ratio of the amplitude of the reflected wave to the amplitude of the incident wave is equal to 0.0091. The transmitted pulse is further attenuated while propagating through the FO medium. The incident Gaussian-modulated sinusoidal pulse is, however, also slightly distorted before arriving upon the boundary, as can be seen in Figure 5a. This is undesired behavior, as the wave should not experience any significant deformation or attenuation while travelling through a vacuum. This purely numerical distortion is a result of the Courant coefficient for a vacuum being significantly lower than 1, i.e., \(\Delta t\) is different than the magic time-step size, which provides accurate simulations in one-dimensional IO FDTD [9]. Such a value of the Courant coefficient is, however, necessary in order to ensure the stability of simulation in the FO medium.
## 5 Conclusions  
Novel technologies and strategies for wastewater treatment have been developed, tested and applied mostly throughout EU northwest countries. This is the case of enhanced CWs, which can be considered as one low-cost and efficient technology able to provide adequate removal of organics and nutrients. Considering the latter, CWs with artificial aeration were tested to intensify chemical oxygen demand (COD) removal. The results evidenced that COD removal efficiencies were higher when intermittent and continuous aeration were used in comparison than when no aeration was considered. However, the decrease of nitrogen and nitrate concentrations was not significant due to unfavourable conditions for the denitrification process (scarce anoxic conditions and available sources of organic carbon). General judgments derived from this preliminary study could presume that through the implementation of intermittent aeration in CWs, strict discharge limits imposed to CWs treating animal manure can be met. Nonetheless, further enhancement of the experimental set-up and methodologies to refine the selection of the most optimal aeration rate is needed prior to implementing this technology in the field. To conclude, this study supports the implementation of aerated CWs in areas with limited land availability as these lead to enhanced recalcitrant COD removal, reduced treatment area requirements, and lower implementation costs.  
Conceptualization and resources were overseen by [PERSON] ([PERSON]) and [PERSON]. The methodology, software, validation, formal analysis investigation, writing--original draft preparation, review and editing were followed and performed by [PERSON], [PERSON] ([PERSON]), [PERSON], [PERSON], [PERSON] ([PERSON]) supervised, reviewed and edited the present work. [PERSON]. ([PERSON]) and [PERSON] ([PERSON]) oversaw the project administration and funding acquisition.  
This work was supported by the Interreg North West Europe programme 2014-2020 co-funded by the European Regional Development Fund under the project ALG-AD [NWE 520]. It was also conducted in the frame of the National Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT)--Ecuador. Open Call 2012  
We would like to thank the support of [PERSON] and [PERSON] on sample collection.  
**Conflicts of Interest:** \"The authors declare no conflict of interest.\" \"The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results\".  
Figure A1: Diagnostic plots for the linear mixed effect model with **(a)** COD, **(b)** NH4\({}^{+}\)-N, **(c)** NO3\({}^{-}\)-N and **(d)** TN as response variable and time and treatment as fixed effects. The compound symmetry covariance structure was used to account for variations in compound concentration across time for each treatment