esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Array System Overview  
This section describes the design of the Single Antenna (SA) element, which serves as a fundamental unit within a larger receiving array system operating at a central frequency of 412 MHz.  
Each SA adopts a hexagonal configuration, as illustrated in Figure 1, consisting of 19 circular radiating elements mounted on a metallic ground plane, following the design already presented in [28]. The choice of our array configuration followed a dimensioning procedure using sensor array analysis tools. This approach considered both the performance of individual elements and the overall array specifications. The hexagonal layout of 19 elements emerged as optimal, offering improved SLL control and preventing the coherent reinforcement of grating lobes at specific scan angles. This result is mainly due to the variation in element spacing along different directions (500 mm along the \(x\)-axis and 1000 mm along the \(y\)-axis) and the odd number of elements, which breaks the symmetry of the layout and helps disrupt regular interference patterns.  
Figure 1: The layout of the single antenna of the SA array: (**a**) front view, (**b**) back view.  
The overall footprint of the SA spans approximately 2.7 m along its maximum dimension, corresponding to about 3.7A at the operating frequency.  
The SA is designed to provide a gain of approximately 17 dBi, with an SLL of \(-\)17 dB and a Half Power Beamwidth (HPBW) of \(16^{\circ}\), in accordance with the specified requirements. Additionally, it supports the reception of both Right-Hand Circular Polarization (RHCP) and Left-Hand Circular Polarization (LHCP) signals on separate channels. For this reason, each individual element of the SA is equipped with two feeding ports, one for each polarization, and is connected to two separate dual beamforming networks located beneath the ground plane, both operating in the broadside direction. One feeding network is dedicated to the RHCP port, while the other is connected to the LHCP port.  
One of the main objectives of the SA system is to enable electronic scanning across a \(\pm 45^{\circ}\) Field of Regard (FoR). To significantly reduce the complexity and physical size of the feeding networks, electronic scanning is constrained to the elevation plane. More precisely, full coverage of the FoR is achieved through a hybrid approach: in the elevation plane, electronic scanning is combined with a fixed mechanical tilt of \(22.5^{\circ}\) relative to the horizontal axis, and in the azimuth plane, beam steering is performed mechanically by rotating the entire SA array using an external positioner. In this way, the electronic scanning range is deliberately restricted to \(\pm 22.5^{\circ}\), thereby containing SA array movements and effectively suppressing side lobe levels. Full-wave electromagnetic simulations were carried out to characterize the resulting radiation performance, allowing for a precise evaluation of the maximum achievable beam tilt and the preservation of key array metrics, such as gain, HPBW, and SLLs, throughout the specified scanning interval. The scanning mechanism is illustrated in Figure 2.
# Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry
###### Abstract  
High spatial and temporal resolution satellite remote sensing estimates are the silver bullet for monitoring of coastal marine areas globally. From 2000, when the first commercial satellite platforms appeared, offering high spatial resolution data, the mapping of coastal habitats and the extraction of bathymetric information have been possible at local scales. Since then, several platforms have offered such data, although not at high temporal resolution, making the selection of suitable images challenging, especially in areas with high cloud coverage. PlanetScope CubeSats appear to cover this gap by providing their relevant imagery. The current study is the first that examines the suitability of them for the calculation of the Satellite-derived Bathymetry. The availability of daily data allows the selection of the most qualitatively suitable images within the desired timeframe. The application of an empirical method of spaceborne bathymetry estimation provides promising results, with depth errors that fit to the requirements of the International Hydrographic Organization at the Category Zone of Confidence for the inclusion of these data in navigation maps. While this is a pilot study in a small area, more studies in areas with diverse water types are required for solid conclusions on the requirements and limitations of such approaches in coastal bathymetry estimations.  
satellite-derived bathymetry; hydrography; CubeSats; hypertemporal; zones of confidence; PlanetScope +
Footnote †: journal: Remote Sensing  
1
## 1 Introduction
Both simulation and measured results show that this scheme significantly reduces the system BER, demonstrates excellent nonlinear distortion suppression capability and enhanced system robustness, and provides an effective new approach to solving the equalization problem under complex channel conditions in terahertz communications.
## 3 Results
### Computational Efficiency Analysis  
We analyzed the computational efficiency of training and inference for each model, as presented in Table 2.  
Notable observations regarding computational efficiency:  
U-Net achieved the highest segmentation accuracy but required longer training and inference times.  
NablaNet provided an effective trade-off between processing speed and segmentation quality.  
Initial preprocessing represented a significant time investment but facilitated more efficient subsequent model training.  
TOPPI values were consistent across all three models as they shared the same pre-processing pipeline before model-specific training. The preprocessing steps represent a significant initial investment but become amortized across multiple experiments as the prepared dataset can be reused for various architectural approaches.
## 3 The Feature Selection Approach of Composite Faults
### Construction of Causal Feature Network of Composite Faults  
The expressions of statistical features have similar calculation methods and may call each other, which leads to redundancies in the feature set. Redundant features will expand the scale of the diagnostic model and slow down the diagnosis. Therefore, feature selection is critical. We have constructed the causal feature network (CFN), introducing the complex network to determine features of relative importance to fault diagnosis results.  
The primary task for constructing the composite fault CFN is to define the network's nodes and edges. As shown in Figure 3, we abstract each feature of composite fault vibration signals as nodes and the causal relationship between features as edges. The CFN of composite faults can be expressed with an adjacency matrix. \(A=a_{ij}\) is to show whether causality exists between features. If causality exists between feature \(i\) and \(j\), then \(a_{ij}=a_{ji}=1\); if no causal relationship exists, then \(a_{ij}=a_{ji}=0\).  
In defining edges, we use IAMB discovery algorithm to determine the causality between features, as shown in Figure 3. We sequentially take features of the composite fault feature set as the target variables and search for the Markov blanket of the target variables in the remaining 19-dimensional features. Then, we obtain the features that share causal relationships with the target variable features. In this case, we add edges between the target variable features and the features in its Markov blanket set.
## 4 Conclusions
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15073709/s1](https://www.mdpi.com/article/10.3390/app15073709/s1)]([https://www.mdpi.com/article/10.3390/app15073709/s1](https://www.mdpi.com/article/10.3390/app15073709/s1)). Figure S1: Parallel coordinates plot for thebiomass concentration of days 0, 2, 4 and 7; Figure S2: Parallel coordinates plot for the carbon concentration of days 0, 2, 4 and 7; Figure S3: Parallel coordinate plots for the nitrogen concentration of days 0, 2, 4 and 7; Table S1: Features from day 0 and day 2 of the bioprocess selected by each feature selection method applied for the forecasting of day 4; Table S2: Features from day 0, day 2 and day 4 of the bioprocess selected by each feature selection method applied for the forecasting of day 7. **Author Contributions:** Conceptualization: C.A.V.R., N.T.F., A.L.N.F., S.P.A. and C.A.V. Methodology: C.A.V.R., N.T.F., A.L.N.F., S.P.A. and C.A.V. Software: C.A.V., S.P.A. and A.L.N.F. Validation: C.A.V.R., N.T.F., A.L.N.F., S.P.A. and C.A.V. Formal analysis: C.A.V., S.P.A. and N.T.F. Investigation: C.A.V.R., N.T.F., A.L.N.F., S.P.A. and C.A.V. Resources: N.T.F., C.A.V.R. and A.L.N.F. Data curation: N.T.F., S.P.A. and C.A.V. Writing--original draft preparation: C.A.V. Writing--review and editing: S.P.A., A.L.N.F., N.T.F. and C.A.V.R. Visualization: C.A.V. Supervision: A.L.N.F., N.T.F. and C.A.V.R. Project administration: A.L.N.F., N.T.F. and C.A.V.R. Funding acquisition: N.T.F., A.L.N.F. and C.A.V.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Fundacao para a Ciencia e Tecnologia (FCT) through iBB, Institute for Bioengineering and Biosciences (UIDB/04565/2020 and UIDP/04565/2020), Associate Laboratory i4 HB (LA/P/0140/2020), project \"SMART\" (PTDC/EQU-EQU/3853/2020), and the doctoral grant 2024.03713.BDANA, from IT, Instituto de Telecomunicacoes, through research grant BIM/No16/2022, B-B01049, and from the Good Food Institute, GFI, through the VitaminSea project (22-CM-PT-DG-1-317). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study and the code needed to reproduce the results are openly available on GitHub at [[https://github.com/carolina-vares/MELs-forecasting](https://github.com/carolina-vares/MELs-forecasting)]([https://github.com/carolina-vares/MELs-forecasting](https://github.com/carolina-vares/MELs-forecasting)), accessed on 19 March 2025. **Conflicts of Interest:** Author [PERSON] was employed by the company Cell4 Food. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Work
ML techniques can help edge caching while predicting user requests based on user preferences, mobility, and content popularity [12; 13; 24]. The convergence of AI/ML techniques with edge networks to optimize complex caching and routing decisions leads to the emergence of Edge Intelligence (EI). Authors [15] presented a supervised ML based content edge caching technique to lower backhaul traffic in cellular networks. The authors employ ML for popularity prediction of videos where content features are taken as input. The popularity of new videos is predicted based on the similarity and popularity of old videos. A 3D convolutional neural network (CNN) with a multiple stage pooling algorithm is employed to extract spatio-temporal features of videos. The extracted features are vectorized and clustered based on cosine distances. A Support Vector Machine (SVM) algorithm classifies each video into a video category. Afterward, a supervised ML algorithm is trained to predict popularity of a video category. To the best of our knowledge, the application of EI to predict the collective opportunities of 5G multicast and edge caching has not been explored. Regression techniques were utilized in [25] to anticipate the demand of online content. The nonlinear attributes of Support Vector Regression and Gaussian Radial Basis Functions are engaged to forecast popularity patterns. Visual features, such as color, face, and scene dynamics, are selected from the videos while utilizing a DNN (ResNet-152). Temporal and social features, such as views and likes, are also utilized in the regression model. Multivariate Radial Basis Function utilizes the linear regression model for temporal popularity growth of video and nonlinear regression model to learn similarity between a set of videos. A data set of 24k videos with a daily count of temporal features over a span of one month from social platforms is used for evaluation. The regression models lead to a prediction accuracy of more than 95%. Although this work employs ML techniques, it predicts the popularity of videos from a global perspective instead of an edge network perspective. Table 1 lists the related work and highlights their contributions. The table shows that the existing solutions are one-dimensional, enabling either multicast delivery or edge caching in 5G networks. In this article, we present a three-dimensional solution to alleviate the burden from networks in the existing pandemic situation. Firstly, we propose a Virtual Network Function (VNF) based service-less architecture at the MNO core to enable video multicast for distance learning. We demonstrate its efficiency in terms of bandwidth consumption, resource block utilization, and QoE optimization on a real-world edge network dataset. Secondly, we propose to complement the VNF with EI to predict and learn the multicast data streams in the MNO core. Lastly, EI is conceptualized to resolve the issue of end-users with high SNR who can not watch the stream live by predicting edge caching opportunities.
# Analysis of Soil-Water Characteristic Curve and Microstructure of Undisturbed Loess
## 3 Results and Discussion
### SWCCs of Undisturbed Loess Samples
#### 3.1.2 SWCCs of Undisturbed Loess Samples Buried in Different Layers  
Samples NO. 2 to NO. 5 represent undisturbed loess samples buried at gradually increasing depths in different layers. The analysis of the SWCCs (Figure 4) reveals that the burial depth significantly influences the AEV, saturated volumetric water content, and slope of the transition zone. A deeper burial results in a higher dry density and a smaller  
Figure 4: SWCCs of undisturbed loess samples from different depths.  
cumulative pore ratio, limiting the water accommodation space and consequently reducing the saturated volumetric water content. The AEV, indicative of the matric suction when gas invades the maximum pore during drying, increases with deeper burial due to larger pore diameters facilitating gas entry. Interestingly, the slope of the transition zone does not exhibit consistent trends with the depth. This discrepancy is attributed to variations in the particle size composition and plasticity index of the loess at different depths [51; 59]. Thus, the slope of the transition zone is influenced not only by the burial depth (dry density) but also by the soil's particle size composition and plasticity index.  
The VG model parameters for the five sets of undisturbed loess samples at different depths are presented in Table 2. The VG model effectively captures the measured SWCC, with the deviation parameter R\({}^{2}\) exceeding 0.99 for all samples, indicating a strong fit. Figure 5a,b illustrate the variations in parameters \(\alpha\) and \(n\), respectively, in regard to the dry density. Parameter \(\alpha\) decreases with an increasing dry density, while parameter \(n\) initially decreases and then increases with the dry density. The trend observed for parameter \(\alpha\) aligns with the findings of [PERSON] et al. (2021) [61], who established a quadratic relationship between parameter \(\alpha\) and the dry density.
# Recent Advances in Spectroscopy Technology for Trace Analysis of Persistent Organic Pollutants
## 2 Applications of Spectroscopy Technology in Detection of POPs
### Fluorescence  
Fluorescence is an amazing natural phenomenon which can be observed at a longer wavelength after the excitation of fluorescent materials at a shorter wavelength. Its original explanation was provided by Sir [PERSON] around 1850 [76]. The investigations of fluorescence-based biological strategies began to develop in the 1950s, due to the availability of commercial fluorescence instrumentation [77]. So far, fluorescence spectroscopic techniques have been widely recognized as useful tools in analytical chemistry, especially due to their high inherent sensitivity [78]. Hence, fluorescence-based techniques like fluorescence sensors using nanomaterials have been suggested as a possible alternative to conventional methods [79; 80; 81].  
Sensitivity of fluorescent sensors strongly depends on the nanostructure and morphology of the sensing film. Such sensors can be used to determine trace POPs by functionalizing the chip surface with functional groups to effectively preconcentrate targeted molecules [82; 83]. For example, optosensors based on the online immobilization of BaP using Amberlite XAD-4 could detect BaP in a continuous-flow system with the limit of detection up to 3 ng\(\cdot\)L\({}^{-1}\)[84]. Among PAHs, only BaP can interact with Amberlite XAD-4 resins and shows strong fluorescence at the excitation wavelength \(\lambda\) = 392 nm. Additionally, fluorescent quantum dot (QD) sensors based on the mechanism of fluorescence resonance energy transfer (FRET) have been extensively studied and applied to detect POPs, due to their high selectivity and sensitivity and a wider applicability [85]. As optical sensors constructed by coating CdTe QDs onto TiO\({}_{2}\) nanotube (NT) arrays which possess a huge surface area (Figure 6A), the fluorescence intensity of BaP was amplified 15 times compared to that of the direct liquid fluorescent spectrometry [86]. The sensitivity obtained using this method to PAHs depended on the number of their fused aromatic rings, with the highest sensitivity to BaP and the detection limit down to 15 pmol\(\cdot\)L\({}^{-1}\). The fluorescence enhancement was contributed to the fluorescence energy transfer from CdTe QDs to BaP. Beyond these, fluorescent sensors functionalized with polysilsesquioxane film can be used to detect PCBs, based on the mechanism that their fluorescence emission intensity decreased steadily with a corresponding increase in PCBs, which was shown in Figure 6B [82]. The detection limit of PCB77 came up to \(8.03\times 10^{-7}\) mol\(\cdot\)L\({}^{-1}\). So far, fluorescence, including its combination with other techniques, has been used to detect different kinds of POPs and has seen notable achievements, detailed in Table 1. This can bring about a new research trend in environmental analysis.  
Figure 5: Schematic diagram illustration of the difference in evanescent wave interaction with adsorbed molecules between (**a**) the nanoporous gold film and (**b**) dense gold film [35].
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 1 Introduction
This model incorporates various constraints, including the physical limitations of drones, channel selection, and communication performance, among others;
* The Discrete Soft Actor-Critic (DNAC) algorithm is deployed on relay drones, training them for channel selection to adeptly navigate and mitigate dynamic interference;
* The Bayesian optimization algorithm is utilized to refine the hyperparameters of the DSAC algorithm, such as the learning rate, discount factor, and target entropy, enhancing the algorithm's capability to resist interference;
* The joint optimization problem is systematically decomposed and transformed into manageable convex subproblems, including the modulation order of relay drones, their transmission power, trajectories, and the power allocation factors for clustered drones, all aimed at iterative solutions to maximize the total communication rate. The experimental results demonstrate that the algorithm proposed in this paper effectively addresses the efficient communication challenges faced by clustered drones in environments with interference. Furthermore, the algorithm exhibits robust stability, strong convergence, and impressive task performance. The structure of this article is as follows: Section 1 introduces related work and provides an overview of the current research status. Section 2 describes the system model, detailing the tasks, channels, and interference. Section 3 presents the problem description, integrating the model to express specific research questions. Section 4 proposes a DSAC algorithm using Bayesian optimization. Section 5 presents a joint optimization algorithm for communication rates. Section 6 covers experimental simulations. Section 7 concludes the paper. Table 1 presents a description of the key parameters used throughout the paper. \begin{table}
\begin{tabular}{c c} \hline \hline
**Key Parameter** & **Description** \\ \hline \(F(k)\) & Trajectory Coordinate Set of the Drone Cluster \\ \hline \(H_{i}^{\text{m}}(k)\) & Channel Gain between Drone \(i\) in the Cluster and the Relay Drone \\ \hline \(\Lambda\) & Channel Attenuation between the Jammer and the Relay Drone \\ \hline \(D_{n}\) & Normal Communication Threshold of the Relay Drone \\ \hline \(E^{s}\) & Total Energy Consumption of the System \\ \hline \(F_{f}^{Rauv}\) & Operating Frequency Set of the Relay Drone \\ \hline \(\text{B}_{n}^{Rauv}\) & Subchannel Bandwidth \\ \hline \(p^{F}\) & Maximum Transmission Power of the Drone Cluster \\ \hline \(p^{I}\) & Maximum Transmission Power of the Jammer \\ \hline \(Y(\cdot)\) & Channel Interference Indicator Function \\ \hline \(SINR_{i,R}\) & SINR of the Signal Received by the Relay Drone from Drone \(i\) in the Cluster \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of key parameters.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 3 Materials and Methods
### Comparison of ALOHA with Other Risk Assessment Tools  
In evaluating the efficacy of ALOHA software for ammonia leak risk assessments, it is crucial to place it within the broader context of available risk assessment tools. ALOHA, primarily designed for analysing chemical releases and modelling dispersion patterns, offers unique strengths, such as its user-friendly interface and integration of emergency response guidelines.  
However, other tools, such as PHAST 6.7 (Process Hazard Analysis Software) and CAMEO 4.1 (Computer-Aided Management of Emergency Operations), provide complementary functionalities. For instance, PHAST excels in detailed quantitative risk assessments, incorporating extensive databases of chemical properties and advanced modelling capabilities, which may enhance the depth of analysis when paired with ALOHA's dispersion modelling.  
Moreover, CAMEO integrates a robust planning framework, including hazardous materials' tracking and real-time monitoring features, which could significantly augment ALOHA's existing functionalities. The incorporation of these tools into the workflow could facilitate a more holistic approach to risk management, ensuring that decision-makers have comprehensive insights for effective emergency preparedness.  
Thus, while ALOHA remains a valuable tool in the field of risk assessment, it is essential to recognise its limitations and consider integration with other advanced tools. Such a multidimensional approach would not only enhance the robustness of risk assessments but also ensure a more thorough analysis of potential hazards in real-world scenarios.