esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Precision Combined Tidal Forecasting Model
## 2 Related Concepts
### PSO Algorithm  
The particle swarm optimization (PSO) algorithm [18] is a swarm intelligence optimization algorithm proposed by [PERSON] and [PERSON] in 1995. Like the fish swarm algorithm and the ant colony algorithm, PSO optimizes the solution via group intelligence generated by the mutual cooperation of particles and information sharing. PSO also has a memory function for dynamically tracking the current search situation and adjusting the search strategy in real time [19]. Because of its simplicity, high efficiency, and lack of many parameter adjustments, PSO has been widely applied in fuzzy system control, parameter optimization of machine learning algorithms, and function optimization.  
The PSO algorithm is fully described below.  
Step 1: Initialize a group of particles in \(D\)-dimensional space. Each particle represents a set of potential optimal solutions to the optimization problem and is characterized by its position, velocity, and fitness. The position and velocity vectors are represented as \(X_{i}=(X_{i1},X_{i2},X_{i3},\ldots,X_{iD})\) and \(V_{i}=(V_{i1},V_{i2},V_{i3},\ldots,V_{iD})\), respectively.  
Figure 1: Architecture of a support vector machine (SVM).  
Step 2: As the particles move in space, update their individual positions by tracking the individual extremum \(P_{\text{best}}\) and the group extremum \(G_{\text{best}}\). \(P_{\text{best}}\) and \(G_{\text{best}}\) represent the local and global fitness of the optimal particle positions, respectively.  
Step 3: Calculate the fitness after updating the particle position. The \(P_{\text{best}}\) and \(G_{\text{best}}\) positions are updated by comparing the fitness of the new particle with those of the individual extremum and group extremum computed in Step 2. In each iteration, the particle adjusts its velocity vector based on the inertia vector \(P_{i}=(P_{i1},P_{i2},P_{i3},\dots,P_{iD})\), the optimal empirical vector \(P_{g}=(P_{g1},P_{g2},P_{g3},\dots,P_{gD})\), and its own experience. It then adjusts its position vector. The specific update formula is  
\[V_{id}^{k+1}=\omega V_{id}^{k}+c_{1}r_{1}(P_{id}^{k}-X_{id}^{k})+c_{2}r_{2}(P_ {gd}^{k}-X_{id}^{k}) \tag{5}\]  
\[X_{id}^{k+1}=X_{id}^{k}+V_{id}^{k+1} \tag{6}\]  
where \(c_{1}\) and \(c_{2}\) are the learning factors, and \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1 (with \(i=1,2,\dots,n\); \(d=1,2,\dots,D\)).
## Materials and Methods
### Reactivity with Acids
On the whole, all complexes have higher P.A. values than free ligands, with only complex **5a**, close to the free ligand. In contrast, the _cisoid_ conformations of protonated free ligands (D, E) show a remarkable additional stability likely due to the above-mentioned N--H-N interaction. It is worth noting that protonation of free bpy\({}^{\text{6 OMe}}\) affords isomer E as the most stable species, resulting in protonation of the nitrogen of the unsubstituted pyridine ring. It results, on the whole, that the methoxy substituent seems to have a subtle effect on the donor properties of the adjacent nitrogen atom, which effects the overall behavior of bpy\({}^{\text{6 OMe}}\) both as a free ligand and in its complexes. Extrapolation of these data suggests that while free bpy\({}^{\text{6 OMe}}\) is more basic than bpy, its rollover complexes are less basic; as a consequence, experimentally, its rollover complexes do not show signs of nitrogen protonation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Neutral/Protonated** & **Ancillary Ligands** & **Bpy\({}^{\text{6 ome}}\)** & **Bpy\({}^{\text{6 et}}\)** & **Bpy** \\ \hline Free ligands & bpy\({}^{\text{R}}\) - bpy\({}^{\text{6H+}}\)(a) B \(\rightarrow\) C & & 952.22 & 968.30 & 948.51 \\  & bpy\({}^{\text{R}}\) - bpy\({}^{\text{R}}\)H+ (b) B \(\rightarrow\) D (E) & & 985.05 (E) (c) & 1002.76 & 982.72 \\  & 1–3 & Me/DMSO & 970.85 & 1005.23 & 990.15 \\ complexes & 4–5 & Cl/DMSO & 954.78 & 988.48 & 972.45 \\  & 6–9 & Me/PPh\({}_{3}\) & 992.85 & 1027.63 & 1014.17 \\  & 7–10 & Cl/PPh\({}_{3}\) & 972.22 & 1006.37 & 992.51 \\ \hline \hline \end{tabular} (a) _transoid_ coplanar configuration for protonated bipyridines; (b) _cisoid_ coplanar configuration for protonated bipyridines. (c) in this case, the most stable species (E) is protonated on the unsubstituted pyridine-nitrogen. The P.A. value for species D is 983.96 KJ/mol.  
\end{table} 
Table 4: DFT calculated proton affinity data (P.A., KJ/mol) for uncoordinated ligands and related complexes. On the whole, this study shows, as expected, that higher P.A. values are found with better donor ligands (compounds **6b**/**9b**, P.A. = 1027.6 KJ/mol; ancillary ligands: PPh\({}_{3}\), Me; substituent: 6-ethyl), whereas the lower values are found in connection with worst donors (compounds **4a**/**5a** P.A. = 954.8 KJ/mol KJ/mol; ancillary ligands: DMSO, Cl; substituent: 6-methoxy).
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 3 Experimental Setup
### Hardware and Software  
We used the software-defined radio device (USRP RIO by National Instrument ([[http://www.ni.com/en-us.html](http://www.ni.com/en-us.html)]([http://www.ni.com/en-us.html](http://www.ni.com/en-us.html))), 1 in Figure 2) to apply IEEE 802.11 physical layer specifications into WAIC bands (4.2-4.4 GHz).To measure wireless channel in WAIC bands, we ran our software, which was newly modified from IEEE 802.11 application framework (commercially provided by National Instrument) based on a visual programming language called LabVIEW (2 in Figure 2). Although we used commercial software, open-source codes based on GNU radio are also available in the case of an experiment under limited budget ([[http://github.com/bastibl/gr-ieee802-11](http://github.com/bastibl/gr-ieee802-11)]([http://github.com/bastibl/gr-ieee802-11](http://github.com/bastibl/gr-ieee802-11))). We used two USRPs: one for transmitter only with transmit antenna and the other for receiver only with receive antenna.
# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 5 Simulation and Result Analysis
### Network Traffic Data Analysis and Preprocessing  
According to the OPNET simulation, we can obtain the traffic data of the Space-Integrated Ground Information Intelligent Network with sudden bursts, periodicity, and stability, so the ordinary ground network traffic prediction method does not apply to the Space-Integrated Ground Information Intelligent Network. We need to segment and preprocess the data to facilitate the GTCN algorithm.  
First, we analyze the autocorrelation of the network traffic sequence to determine the segment of the traffic sequence. The reference of calculation is shown in Equation (3) and the autocorrelation of the intelligent network traffic in the Space-Integrated Ground Information Intelligent Network is shown in Figure 7.  
From Figure 7, we can observe that in the network traffic sequence, each sampling point has a strong correlation with the first two sampling points. A weak correlation with a large value is shown between the third sampling point in front of each sampling point and the 40 th sampling point in front of each sampling point. While the segmenting period is too long and should not be too short, these weak correlation sequ  
Figure 7: Autocorrelation Value of the Network Traffic.  
can be included in the basis of the segment. Therefore, in this experiment, a length of 40 samples is taken as the segmentation length, with 1200 sampling points in each segment. Data preprocessing supports the neural network model to process the data quicker by normalization and standardization of data, and to reduce the learning pressure of the neural network, thus improving the speed and efficiency of learning features of neurons.
## 1 Introduction
### Previous Studies--Pockmark Field Activity
#### 1.2.2 In-Situ CH\({}_{4}\) Measurements  
Between 2002 and 2003, monitoring of the PGPF activity was attempted by operating a methane sensor (METS(r),Franatech GMBH, Germany) above 14 pockmarks. Emphasis was given to the activity of one crater complex, called \"P4\", which showed high methane concentrations in the water near the seafloor during monitoring. Dissolved CH\({}_{4}\) amounts ranging from 3 to 707 nmol/L were recorded on a yearly basis, without showing any seasonal patterns but displaying rather a spasmodic supply rate of CH\({}_{4}\) to the water column [41]. However, after a strong earthquake (4.7 R, 30 July 2003, 60 km east of the field), significantly higher dissolved methane concentration levels (420 to 1476 nmol/L) were measured near the seabed of pockmark 'P4'.
# A Molecularly Imprinted Polymer Based SPR Sensor for 2-Furaldehyde Determination in Oil Matrices
## 3 Results
### SPR Spectra of POF-MIP Sensors  
In order to investigate the effect of the thickness of the MIP layer, two kinds of sensors were considered: the first one was prepared on the platform SPR-POF-A5 bare by depositing the prepolymeric mixture at 1000 rpm and the second one was prepared by depositing two layers of MIP (SPR-POF-A1 MIP2 and SPR-POF-A2 MIP2), each one at a spinning rate of 500 rpm. They should be covered by a larger amount of MIP than SPR-POF-A5 MIP1.  
As an example, the spectra in the water of the bare platforms of SPR-POF-A5 bare and SPR-POF-A2 bare are shown in Figure 4. In both cases, the spectra of the corresponding MIP sensor in air and water, normalized to the spectrum of the bare sensor in air, present several dips, which are sharper and better defined for SPR-POF-A5 MIP1. Additionally, the difference between the spectrum in air and that in water is higher in the case of SPR-POF-A5 MIP1, which confirms that a lower amount of MIP is deposited.  
Figure 2: SPR spectra in water of the bare optical platforms, normalized to the spectra in air.  
Figure 4: Spectra in air and water normalized on the spectrum of the corresponding bare sensor in air of the sensors: (**a**) SPR-POF-A5 MIP1; (**b**) SPR-POF-A2 MIP2.  
Figure 3: Sensitivity curves of SPR-POF-A4 bare and SPR-POF-A5 bare in water–glycerin solutions at different RIs.  
The effect of the different thicknesses can be more easily appreciated by comparing the same spectra but normalized to the SPR-POF-MIP in air, as reported in Figure 5. In both cases, the main resonance dip is shifted to higher wavelengths with respect to that obtained at the bare sensor, demonstrating that the MIP layer is present and that it modifies the RI of the dielectric layer. Its RI must be higher than that of water. As proposed in a previous work [14], the resonance wavelength shift with respect to that of the same platform not derivatized with MIP in pure water depends on the amount of MIP deposited, being different for SPR-POF-A5 MIP1 and SPR-POF-A1 MIP2.  
By increasing the number of MIP layers, the resonance wavelength increases, and a shift to higher wavelengths (red shift) is observed (from 660 nm to 720 nm). This could be due either to a direct sensing of the RI of the fluid overlaying the MIP or to some solvation effects. As a matter of fact, the polymer considered here contains carboxylic groups from the functional monomer MAA, which can easily interact with water.  
Figure 5: Spectra in water of the bare and MIP derivatized platforms normalized to the corresponding platform in air: (**a**) spectra of SPR-POF-A5 bare and SPR-POF-A5 MIP1 in water; (**b**) spectra of SPR-POF-A2 bare and SPR-POF-A2 MIP2.
## 4 Results
### Fault Detection Results Analysis  
To assess the effectiveness of the proposed multimodal decision fusion diagnostic method for rolling bearing fault detection, various tests were conducted. The output results are presented in a confusion matrix, which includes different classification performance metrics, such as accuracy, recall, and F1 score. The confusion matrix displays each class with a corresponding row and column. Each entry in the confusion matrix indicates the number of test samples, where the columns represent the true classes, and the rows represent the predicted classes.  
Initially, the performance of each modality model was analyzed on the Ottawa and Pade datasets, and a comparison was made with the proposed multimodal decision fusion method.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 3 Materials and Methods: A Gamified AI-Driven System
### System Component  
The proposed system includes therapeutic micro-games, adaptive daily surveys, environmental data collection via location services, and AI-driven analytics. Figure 3 shows the main application interface, allowing users to select games while weather data is contextualized in real time.  
Figure 1: Physical view of the system architecture showing mobile front-end, AWS back-end integration, and AI analytics components.  
Figure 2: Sample of stored user data including mood rating, location, and gameplay interaction timestamps.  
Key components of the system are as follows:  
* **Therapeutic micro-games:** The system incorporates lightweight micro-games designed to assess and support cognitive and emotional functioning. These games target attention span, decision-making speed, working memory, and error sensitivity, domains that are frequently affected by depression and stress-related disorders. Prior research has demonstrated that game-based tasks can capture fine-grained behavioral markers and serve as engaging interventions to support emotion regulation and cognitive flexibility [33; 34]. Short, interactive games reduce friction and increase adherence, making them suitable for frequent self-monitoring and low-intensity therapeutic engagement. Figure 3 illustrates an example game interface.
* **Adaptive daily surveys:** The platform employs short daily self-report surveys grounded in EMA principles, focusing on core themes such as mood, stress levels, energy, sleep quality, social interaction, and cognitive clarity. Survey questions are context-aware and adapt dynamically based on user history and behavioral data, ensuring relevance while reducing repetition and survey fatigue. This adaptive approach enables high-frequency yet low-burden mental health monitoring, enhancing the granularity and accuracy of symptom tracking [35].  
Figure 3: Main application interface showing game selection and real-time weather contextualization, and the game page (Sudoku).  
* **Environmental context awareness:** Real-time environmental data, including temperature, humidity, and light exposure, are collected to provide contextual layers for emotional assessment. This data is correlated with user-reported mood states to explore environmental influences on mental well-being.
* **AI-driven analytics:** The platform leverages machine learning models to integrate data from micro-game performance, adaptive surveys, and environmental sensors. These models first identify hidden behavioral patterns and correlations, such as changes in cognitive speed, mood variability, and environmental influences, indicative of emerging depressive trends. By learning from historical data, the system can predict potential mental health risks and deliver real-time, personalized feedback aligned with each user's evolving profile.
anticorrosive coating [16]. Alginates are also excellent candidates for forming composite hydrogels for their easy gelling properties, resulting from the cooperative binding of divalent and trivalent cations to their structure.  
Previous works showed good interaction between conducting polymers and alginate [9], with increased conductivity [17], allowing their use in highly sensitive amperometric glucose biosensors [18; 19], and for endocrine-disrupting compound extraction from water samples [20], where polypyrrole not only improved the adsorption of the pollutants, but also the stability and dispersibility in aqueous media.  
In this work, two simple synthetic approaches were presented to prepare alginate-polypyrrole-based hydrogels. In the first approach, the conducting polymer was prepared by the chemical synthesis of pyrrole in an alginate matrix, followed by gelation with CaCl\({}_{2}\). In the second approach, the alginate hydrogel was directly prepared by gelation with CaCl\({}_{2}\), and then the pyrrole was chemically polymerized around the pre-formed alginate hydrogel beads. In the first case, polymer particles were dispersed all over the hydrogel matrix, while in the second case, the polypyrrole was only present around the hydrogel matrix.  
Both hydrogels were used for the extraction of contaminants of emerging concern (CECs), which are a class of compounds with diverse origins that have not been deeply studied in the past, and in many countries are still not fully monitored, despite presenting adverse effects both in the environment and in human health [21; 22; 23]. CECs include many different molecules with various physical chemical properties. Thus, in this work, three contaminants with different characteristics were used to test the hydrogels as extraction phases: progesterone, atrazine, and caffeine. These molecules were selected not only because they are classes of CECs but also because they have different polarities, solubilities, and pKa, working as model molecules for the extraction studies.  
The SPE (solid phase extraction) technique was applied, since it is a popular technique for sample preparation and removal of matrix interferents. It allows rapid, selective, and reproducible sample preparation, providing the isolation, extraction, and concentration of the target analytes [24; 25].
## 4 Evaluation
### Results and Discussion
#### 4.2.7 Limitations of the Proposed Approach  
Our strategy has three main limitations. First, to support producer mobility we use the location-based forwarding strategy to fill the gap resulting from the lack of the information on the producer's new location after handoff. However, location-based forwarding cannot guarantee 100% delivery in mobile and ad hoc networks due to the local minimum issue. In a connected graph, a local minimum occurs when a node, denoted as x, is closer to the destination y than any of its neighbors, even though y is not directly connected to x. In this situation, the node x faces difficulty in deciding the next hop, resulting in a failure in the forwarding process. Second, the effectiveness of location prediction depends on the accuracy of the prediction algorithm and the quality of the input data. If the predictions are not accurate, it may result in inefficient forwarding decisions, leading to increased latency and packet loss. Third, the limitation arising from the overhead generated by the synchronization of producer location coordinates among Anchors during frequent handoffs of producers poses a significant challenge. As producers move in the network with high speed, the necessity to synchronize data introduces a notable overhead, impacting the system's performance and efficiency. The continuous synchronization process consumes communication and processing resources, leading to increased network congestion, latency, and potential resource contention among Anchors.  
Figure 13: (**a**) Average delay vs. mobile producer speed, (**b**) average path stretch vs. mobile producer speed.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
###### Abstract  
An increasing global aridification due to climate change has made the health monitoring of vegetation indispensable to maintaining the food supply chain. Cost-effective and smart irrigation systems are required not only to ensure the efficient distribution of water, but also to track the moisture of plant leaves, which is an important marker of the overall health of the plant. This paper presents a novel electromagnetic method to monitor the water content (WC) and characterisation in plant leaves using the absorption spectra of water molecules in the terahertz (THz) frequency for four consecutive days. We extracted the material properties of leaves of eight types of pot herbs from the scattering parameters, measured using a material characterisation kit in the frequency range of 0.75 to 1.1 THz. From the computed permittivity, it is deduced that the leaf specimens increasingly become transparent to the THz waves as they dry out with the passage of days. Moreover, the loss in weight and thickness of leaves were observed due to the natural evaporation of leaf moisture cells and change occurred in the morphology of fresh and water-stressed leaves. It is also illustrated that loss observed in WC on day 1 was in the range of 5% to 22%, and increased from 83.12% to 99.33% on day 4. Furthermore, we observed an exponential decaying trend in the peaks of the real part of the permittivity from day 1 to 4, which was reminiscent of the trend observed in the weight of all leaves. Thus, results in paper demonstrated that timely detection of water stress in leaves can help to take proactive action in relation to plants health monitoring, and for precision agriculture applications, which is of high importance to improve the overall productivity.  
vegetation health monitoring; leaf water content; terahertz; sensing; plants health Article
## 5 Conclusions  
This study confirmed that an IMU-based wearable sensor can provide a feasible and cost-effective alternative to marker-based MoCap systems for quantitatively analyzing the trunk range of motion. Among the movements tested, rotational tasks showed the highest accuracy and agreement, whereas flexion, extension, and lateral bending were more affected by variability and underestimation, likely due to sensor relative displacement and soft tissue artifacts.  
Despite these limitations, including a small sample size and the exclusive inclusion of healthy participants, this study offers a proper preliminary validation of the clinical potential of the proposed approach. Future research should focus on clinical populations, where compensatory strategies and postural alterations may impact measurement accuracy. Refining calibration procedures and improving sensor fixation methods will also be essential to increase reliability in real-world and outpatient settings.  
Conceptualization, F.D.F. and V.C.; methodology, V.C. and N.F.L.; formal analysis, F.D.F., S.C. and V.C.; investigation, F.D.F. and S.C.; resources, V.C.; data curation, F.D.F., S.C. and V.C.; writing--original draft preparation, F.D.F., S.C., M.P. ([PERSON]), M.P. ([PERSON]) and M.G.; writing--review and editing, F.D.F., V.C., M.G. and N.F.L.; supervision, V.C. and M.P.  
([PERSON]), M.G. and N.F.L. All authors have read and agreed to the published version of the manuscript.  
The study was supported by the MUSA--Multilayered Urban Sustainability Action--project funded by the European Union's NextGenerationEU under the National Recovery and Resilience Plan (NRRP) Mission 4, Component 2, Investment Line 1.5: Strengthening of research structures and creation of R&D \"innovation ecosystems\" of \"territorial leaders in R&D\".  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of POLITECHICO OF MILAN (protocol 22/2021, 14 June 2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data collected and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors would like to thank [PERSON] for her valuable help in data acquisition and processing.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: