esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Scattering Properties of Graphene Plasmonic Trimers  
As was pointed out above, the nonzero net dipole moment of the graphene plasmonic trimer system enables optical sensitivity with specific polarization. The A\({}_{1}\) modes show a nonzero net dipole moment along the vertical direction, while the B\({}_{2}\) modes demonstrate a nonzero net dipole moment along the longitudinal direction. The far field scattering extinction cross-section as a function of wavelength (from 4.1 \(\upmu\)m to 5.0 \(\upmu\)m) with different vertex angles \(\theta\) is shown in Figure 6. The scattering characteristics of graphene plasmonic trimers shown in Figure 6 are generally consistent with the plasmon energy diagrams shown in Figure 4. The evolution of the peak positions of the scattering cross-section spectra comes from the net nonzero dipole moments of the graphene trimer as a function of vertex angle. The change of net dipole moments of graphene trimers with increased vertex angle \(\theta\) gives rise to the perturbation in extinction spectra. We chose the E\({}^{-}\)-A\({}_{1}\)-\(\Sigma_{\theta}\)\({}^{+}\) and A\({}_{1}\)'-A\({}_{1}\)-\(\Pi_{\text{u}}\) mode evolution paths with vertical polarization for further exploration of transformation mechanisms in extinction spectra. With \(\theta=60^{\circ}\), the net dipole moments of the graphene trimer are relatively large in E' mode (Figure 2), where the mode is bright and the peak in extinction spectra is also obvious (Figure 6). As \(\theta\) becomes larger than \(60^{\circ}\), E' mode transforms to A\({}_{1}\) mode with a decrease of net dipole moment due to the rotation of the dipole moment in two base nanodisks, which gives rise to a reduction of the peak in extinction spectra. When \(\theta=180^{\circ}\), A\({}_{1}\) mode transforms into a dark \(\Sigma_{\theta}\)\({}^{+}\) mode with zero net dipole moment, and the peak completely vanishes in extinction spectra. On the contrary, the A\({}_{1}\)'-A\({}_{1}\)-\(\Pi_{\text{u}}\) mode evolution path shows a transition that a dark A\({}_{1}\)' mode transforms into a bright \(\Pi_{\text{u}}\) mode with an increase of net dipole moment of graphene trimers. Consequently, a new peak appears and the corresponding peaks in extinction spectra gradually become more distinguishable. Note that there are some peaks that are not related to any mode evolution paths discussed here. These might belong to the higher order modes or the hybridized modes, which will be examined in further exploration.  
Figure 5: (a) Quality factor (\(Q\) factor) as a function of the the vertex angle (\(\theta\)), (b) the real part of effective refractive index as a function of the plasmon energy.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 8 Evaluation
### Model Parameter Setting  
To compare the objectivity of the results, the hidden layer of the LNN network was set to two layers, 24 units in the first layer and 12 units in the second layer. The size of each sliding window is set to 24 and a training target minimum error of 0.0001 is chosen. The Adam optimizer with an initial learning rate of 0.005 is chosen for RLSTM neural network training. As one of the important components in the model proposed in this paper, the LNN-RLSTM has two important parameters: Initial Learn Rate and Learn Rate Drop Factor. If Initial Learn Rate is too low, the training will take a long time, but if the learning rate is too high, the training may fall into suboptimal results. Learn Rate Drop Factor is applied to the learning rate each time a certain number of epochs pass, reducing the impact of overly old historical data on future data. Experimentally, Initial Learn Rate is chosen to be 0.005 and Learn Rate Drop Factor is chosen to be 0.2, and the model accuracy is high.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**KPI** & **Average \# of Users** &
\begin{tabular}{c} **Average \# of** \\ **Activated Users** \\ \end{tabular} & **Cell PDCP Traffic** \\ \hline Total points & 3480 & 3480 & 3480 \\ Anomaly point & 156/4.48\% & 100/2.87\% & 178/4.91\% \\ Total windows & 145 & 145 & 145 \\ Abnormal windows & 2/1.38\% & 1/0.69\% & 2/1.38\% \\ Average & 16.2898 & 1.7046 & 1.7133 \(\times\) 10\({}^{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of dataset.  
Figure 12: Anomaly detection of three types of KPI data. (**a**) Anomaly detection of average # of users. (**b**) Anomaly detection of average # of activated users. (**c**) Anomaly detection of cell PDCP traffic.
# Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods
## 6 Conclusions  
This article shows the results of a multi-criteria decision-making study when using PMEDM cylindrically shaped parts. In this work, 90 CrSi tool steel was chosen as the workpiece material, and 100 nm SiC powder was mixed into the Diel MS 7000 dielectric. Moreover, five process factors, including the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage, were investigated. Additionally, the Taguchi method with the L18 (\(2^{1}+3^{4}\)) design was used to design the experiment, and three methods, including MARCOS, TOPSIS, and MAIRCA, were applied for multi-criteria decision making. Moreover, the determination of the weights for the criteria was performed using the MEREC method. The following conclusions were drawn from the research results:  
1. This is the first time that MARCOS, TOPSIS, and MAIRCA methods have been used for the MCDM of a PMEDM process when processing cylindrically shaped parts.
2. Using all three above-mentioned methods identified the same best alternative.
3. The MAIRCA and the TOPSIS methods give quite similar ratings, proving that these two methods can be used interchangeably for MCDM when using PMEDM.
4. It was noted that the optimum set of the input factors for obtaining the minimum Ra and the maximum MRS simultaneously when processing cylindrically shaped parts was C\({}_{\text{p}}\) = 0.5 (g/l), T\({}_{\text{on}}\) = 8 (\(\upmu\)s), T\({}_{\text{off}}\) = 12 (\(\upmu\)s), IP = 15 (A), and SV = 5 (V).
5. To further strengthen the reliability of the conclusions of this study, it is necessary to conduct multi-criteria decision-making studies with different weighting methods.  
All authors have discussed the original idea. [PERSON], [PERSON] and [PERSON] designed and analyzed the experiment and the MCDM problem; [PERSON] wrote this manuscript with support from [PERSON], [PERSON], [PERSON] and [PERSON]. All authors discussed the manuscript and gave critical feedback. [PERSON] supervised this work and revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Thai Nguyen University of Technology.  
The authors state no conflict of interest.  
Figure 2: Comparison graph of MARCOS, TOPSIS, and MAIRCA.
# Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models
###### Abstract  
This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection and analysis. As GNSS systems become increasingly reliant on deep learning for signal processing, the lack of model interpretability poses significant risks for safety-critical applications. We propose a novel approach combining Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells with Layer-wise Relevance Propagation (LRP) to create an explainable framework for multipath detection. Our key contributions include: (1) the development of an interpretable LSTM architecture for processing GNSS observables, including multipath variables, carrier-to-noise ratios, and satellite elevation angles; (2) the adaptation of the LRP technique for GNSS signal analysis, enabling attribution of model decisions to specific input features; and (3) the discovery of a correlation between LRP relevance scores and signal anomalies, leading to a new method for anomaly detection. Through systematic experimental validation, we demonstrate that our LSTM model achieves high prediction accuracy across all GNSS parameters while maintaining interpretability. A significant finding emerges from our controlled experiments: LRP relevance scores consistently increase during anomalous signal conditions, with growth rates varying from 7.34% to 32.48% depending on the feature type. In our validation experiments, we systematically introduced signal anomalies in specific time segments of the data sequence and observed corresponding increases in LRP scores: multipath parameters showed increases of 7.34-8.81%, carrier-to-noise ratios exhibited changes of 12.50-32.48%, and elevation angle parameters increased by 16.10%. These results demonstrate the potential of LRP-based analysis for enhancing GNSS signal quality monitoring and integrity assessment. Our approach not only improves the interpretability of deep learning models in GNSS applications but also provides a practical framework for detecting and analyzing signal anomalies, contributing to the development of more reliable and trustworthy navigation systems.  
G 2025, 25, 978 +
Footnote †: journal: Sensors  
1
Footnote 1: Department of Communications, Navigation, and Control Engineering, National Taiwan Ocean University,  
2 Peining Road, Keelung 202201, Taiwan; [EMAIL_ADDRESS] (H.-S.W.);  
[EMAIL_ADDRESS] (Z.-H.G.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 2 Progress of Traditional Aquaculture
### Conventional Technologies and Solutions  
#### 2.2.1 Control of Water Quality  
The most straightforward methods to control water quality in aquaculture include reducing the stocking density and frequent water replacement. However, in the aquaculture industry, owing to the low profitability and the high cost, the applications of these two methods are limited.  
Wastewater treatment for water reuse is a possible way to reduce water replacement frequency and control the operation cost of the aquaculture system [7; 27]. Technologies commonly applied for the aquaculture wastewater treatment include anaerobic treatment and aerobic treatment, which performed well in wastes removal and water purification. For example, [PERSON] et al. (2007) reported that removal efficiency of chemical oxygen demand (COD) of sequencing batch reactor (SBR) under aerobic condition reach 97.34% in eight days [27]. In the study of [PERSON] et al. (2008), aquaculture sludge was subjected to anaerobic treatment for methane production and up to 70% sludge-mass reduction was demonstrated [28]. However, from the perspective of nutrients recovery, these traditional treatment technologies are not highly recommended [9]. For example, organic carbon in aquaculture wastewater is converted to CO\({}_{2}\) and CH\({}_{4}\) by aerobic and anaerobic treatment. As a result, the aquaculture wastewater is treated at the expense of greenhouse gas emissions and the resources in wastewater could not be efficiently reused. An artificial wetland was considered as an environmentally friendly way to recover nutrients from aquaculture wastewater and improve water quality, however, its commercialization is hindered by the large ground occupation area and the high management cost. [PERSON] et al. (2016) reported that the maintenance of a high content of dissolved oxygen (DO) by aeration is a technically feasible way to improve the stocking density, but the high electricity consumption of the aeration  
Figure 1: Comparison of conventional aquaculture and microalgae-assisted aquaculture.  
process increases the aquaculture cost [8]. Therefore, until now, the eco-friendly and economically promising technologies for water quality control have not been widely applied in aquaculture.
## 3 Data and Methods
### Data Sets  
This study analyzed data sets from many sources. For the InSAR analysis, two Sentinel-1 data sets with range and azimuth resolutions of 2.3 m and 14 m, respectively, were acquired from the European Space Agency (ESA). The detailed information of the used Sentinel-1 images is presented in Table S2 and the spatial coverage of SLCs and the geometric relationship between the SLCs and the study area are shown in Figure S1. It is noteworthy that the combination of ascending and descending data can reduce the slope effects on the observed geometry [57]. The STRMGL1 digital elevation model with a resolution of 30 m was downloaded from the Land Processes Distributed Active Archive Center ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)) (accessed on 12 October 2023)) and used for co-registration and flat-earth phase removal in the single interferogram generation processes of SLCs. Gaofen-2 optical images with a resolution of 2 m were collected from the China Center for Resource Satellite Data and Applications ([[http://www.cresda.com/](http://www.cresda.com/)]([http://www.cresda.com/](http://www.cresda.com/)) (accessed on 12 October 2023)) to assist in the analysis of landslide spatial geometry. Unmanned aerial vehicle (UAV) images with a resolution of less than 0.2 m were used to determine the shape of landslides during the field investigation. The landslide inventory map provided by the Department of Natural Resource of Sichuan Province was used to analyze the spatial distribution characteristics.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 3 Results
### The Characteristics of Inter-Annual Variation in NDVI
From the perspective of the final change, the transfer-out area of high coverage was the largest, which was 357,704.37 km\({}^{2}\), 1.26 times the transfer-in area, accounting for 50.66% of the total transfer-out area; the highest coverage transfer-out area was 23,192.33 km\({}^{2}\), accounting for 3.28% of the total transfer-out area; the medium coverage transfer-out area was 307,709.17 km\({}^{2}\), accounting for 43.58% of the total transfer-out area and 5.36 times the transfer-in area; the low coverage transfer-out area was 13,993.99 km\({}^{2}\), accounting for 1.98% of the total transfer-out area; and the transfer-out area of lowest coverage was 3544.33 km\({}^{2}\), accounting for 0.5% of the total transfer-out area. The transfer-in area of highest coverage was the largest, which was 351,279.44 km\({}^{2}\), accounting for 49.75% of the total transfer-in area, and 15.15 times the transfer-in area; the high coverage transfer-in area was 283,127.25 km\({}^{2}\), accounting for 40.10% of the total transfer-in area; the area of medium coverage transfer-in area was 57,361 km\({}^{2}\), accounting for 8.12% of the total transfer-in area; the transfer-in area of low coverage was 12,805.25 km\({}^{2}\), accounting for 1.81% of the total transfer-in area; and the transfer-in area of lowest coverage was 1570.49 km\({}^{2}\), accounting for the total transfer-in area of 0.22%. In this period, the vegetation coverage mainly showed a trend of gradual improvement, and the vegetation coverage also gradually increased.
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
###### Abstract  
In this study, we present the Response-Based Key Encapsulation Mechanism (R-KEM), an ephemeral key encapsulation and recovery scheme tailored for cryptographic systems in high-noise, high-jamming network environments. By adopting the Challenge-Response Pair (CRP) mechanism for both key encapsulation and authentication, R-KEM eliminates the need to store secret keys on the device, favoring on-demand key generation. By maintaining only encrypted data on the device, R-KEM significantly enhances security, ensuring that in the event of an attack, no sensitive information can be compromised. Its novel error-correcting strategy efficiently corrects 20 to 23 bits of errors promptly, eliminating the need for redundant helper data and fuzzy extractors. R-KEM is ideally suited for terminal devices with constrained computational resources. Our comprehensive performance analysis underscores R-KEM's ability to recover error-free cryptographic keys in noisy networks, offering a superior alternative to conventional methods that struggle to maintain secure data transmission under such challenges. This work not only demonstrates R-KEM's efficacy but also paves the way for more resilient cryptographic systems in noise-prone environments.  
c 2025 15 2732 1  
This paper outlines the foundational principles of our method and presents a comprehensive performance analysis, demonstrating its effectiveness in key recovery under challenging conditions.
## 3 Data Sources and Treatments
### Data Treatment  
Here, the input data are treated with various methodologies in order to obtain useable and synchronized forms of all the data. The data treatment stage is performed before the WT transformation and the FFANN model training steps are executed. The following are the data treatment techniques employed in this paper [19].  
1. Both the 10-min resolution actual measurement data and the 15-min resolution weather data are changed into hourly-average data to form a complete one-hour resolution data; \[\mathrm{x_{houly}(t)}=\frac{\sum_{t=1}^{6}\mathrm{x_{10-min}(t)}}{6},\] (1) for the wind speed and power actual measurements, \[\mathrm{x_{houly}(t)}=\frac{\sum_{t=1}^{4}\mathrm{x_{15-min}(t)}}{4},\] (2) for the weather forecast information.  
Here, \(\mathrm{x_{10-min}(t)}\), \(\mathrm{x_{15-min}(t)}\), and \(\mathrm{x_{houly}(t)}\) are the 10-min resolution wind energy generation data, the 15-min resolution weather data, and the hourly resolution values, respectively.
2. The WRF weather archives are converted from the UTC to the CST (data timestamp synchronization); \[x_{CST}(t)=x_{UTC}(t+8)\] (2) Here, \(x_{CST}(t)\) and \(x_{UTC}(t)\) are hourly weather data in CST and UTC, respectively.
3. Skipped raw data are replaced by an equivalent data generated by the following expression: \[x_{i}=\mu_{1}x_{i-24}+\mu_{2}x_{i+24}\] (3) Here, \(\mathrm{x_{i}}\) denotes the value at the \(i\)th timestamp, and \(\mu_{1}\) and \(\mu_{2}\) are weight coefficients (0.5 is used in this work). \(\mathrm{x_{i-24}}\) and \(\mathrm{x_{i+24}}\) are the values at similar hours on the prior day and the following day, respectively.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 3 Results
### Influence of LAI and Soil Moisture on \(\sigma^{\circ}\) Calculated from Sentinel-1 Data.
A full range of soil moisture under fixed LAI caused the variation of \(\sigma^{\circ}\) VH up to 5 dB. The case of LAI under fixed SM caused a variation of \(\sigma^{\circ}\) VH of only 1.2 dB. It can be concluded that the influence of LAI on \(\sigma^{\circ}\) is slightly over the statistical error (0.89 dB). The influence of the two components, LAI and SM, on \(\sigma^{\circ}\) VH values when the soil moisture is low was examined. The model described by Equation (3) presents the relationship of LAI and SM for the conditions when soil moisture was lower than 40%. \[\sigma^{\circ}\,\mathrm{VH}\,=\,-11.6\,+\,0.046\times\mathrm{SM}\,+\,1.12 \times\mathrm{LAI}-0.24\times\mathrm{LAI}^{2} \tag{3}\]  
Figure 10 presents the relationship between observed and modelled \(\sigma^{\circ}\) VH (dB) for SM!40, where R\({}^{2}\) = 0.6, \(p\)! 0.05, number of observations--52, RMSE = 0.73 dB, MBE = 0.1 dB. When LAI is constant, the partial correlation for SM explaining \(\sigma^{\circ}\) VH variation gave R equal to 0.61, and R was equal to 0.48 for LAI explaining \(\sigma^{\circ}\) VH variation when SM is constant. Figure 8: Relationship between \(\sigma^{\circ}\) VH observed and predicted by applying Equation (2). Figure 10: Relationship between \(\sigma^{\circ}\) VH observed and predicted by applying Equation (3). Figure 9: 3D-area plot for Equation (2). With fixed low soil moisture values, the range of backscatter variance depended on LAI and could increase up to 3.2 dB. When LAI is on a fixed level, the variance of backscatter caused by SM gets up to 2.4 dB, (Figure 11). Contribution of LAI to backscatter VH was higher when SM values were low. High difference of \(\alpha^{\circ}\) VH between two points covered by vegetation does not mean that the soil moisture is really different. In the model (3) \(\sigma^{\circ}\) VH \(>\)\(-\)19 dB represents a high value of LAI for SM of about 30%-40%. The same values of \(\sigma^{\circ}\) VH, i.e., \(>\)\(-\)19 dB in the model (2) occurred for high SM (\(>\)70%) independently of LAI (Figures 9 and 11).
###### Abstract  
Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant _Neyraudia renaudiana_, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0-20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R\({}^{2}\)) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.  
heavy metal; soil remediation; reflectance spectroscopy; MEA-BP neural network +
Footnote †: journal: Applied Sciences  
Article