esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Flood Susceptibility Mapping  
In the current study, the frequency ratio (FR) and weights of evidence (WoEs) were used to hybridize the classes of flood conditioning factors to be used in the decision matrix in TOPSIS. Index of entropy (IoE) values of the flood condition factors were used to determine the weights of the factors to be used in the AHP method. Five distance measures as Euclidean, Manhattan, Chebyshev, Jaccard, and Soergel were implemented to determine the closeness coefficient values in TOPSIS. Closeness coefficients calculated at randomly selected points were interpolated throughout the basin using the Krigging method to generate the flood susceptibility maps. Estimations were validated using the Receiver Operating Characteristic (ROC) analysis and the Seed Cell Area Index (SCAI) values for both training and testing points.
# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
###### Abstract
These systems, capable of engaging with users through spoken, written, and visual languages, have the potential to expand access to mental health interventions, especially for those reluctant to seek help due to stigma [18; 19]. The global market for mental health apps, including chatbots, was estimated at USD 6.2 billion in 2023, with a projected growth rate of 15.2% annually from 2024 to 2030 [20]. As of 2022, mental health, meditation, and sleep trackers accounted for less than 15% of health apps, indicating substantial potential for growth in this area [21]. AI-powered chatbots have evolved from simple rule-based only systems to advanced models using natural language processing (NLP) [22; 23]. They show great potential in medical contexts, offering personalized, on-demand health promotion interventions [24; 25]. These chatbots mimic human interaction through written, oral, and visual communication, providing accessible health information and services. Over the past decade, research has assessed their feasibility and efficacy, particularly in improving mental health outcomes [26]. Systematic reviews have evaluated their effectiveness, feasibility in healthcare settings, and technical architectures in chronic conditions [26]. Recent studies focus on using AI chatbots for health behavior changes like physical activity, diet, and weight management [26]. Integrated into devices like robots, smartphones, and computers, they support behavioral outcomes such as smoking cessation and treatment adherence [26]. Additionally, AI chatbots aid in patient communication, diagnosis support, and other medical tasks, with studies discussing their benefits, limitations, and future directions [25]. Their potential uses include mental health self-care and health literacy education [14; 25; 27; 28].
# Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT
## 3 Systematic Literature Review
### Related Work on RA/DRA in MIoT  
We conducted the SLR between 2 May 2023 and 6 May 2023 for all the chosen information sources. Figure 1 shows the suggested PRISMA 2020 flow diagram for SLR, which includes searches of databases.  
Regarding the process, the initial identification resulted in 172 records, with four removed for being duplicates or unavailable. The next phase screened these records and excluded 83 because the title or abstract was unrelated to our SLR proposition. We assessed 85 reports, reading all the work and excluding 73 for various reasons related to not meeting the previously identified eligibility criteria. The process finished with selecting 12 studies, where we extracted significant risk-associated elements, frameworks, standards, attacks, and methodologies to support our discussion. Table 1 summarises the main findings from the SLR, presenting an overview of selected work on RA/DRA. Note that some literature is specific to MIoT, and some discusses IoT in broader terms.  
[PERSON] et al. (2018) [79] devised a DRA framework in the context of autonomous vehicles. This work did not make it into the SLR because it was not applied to healthcare; however, it includes important discussions and observations for proposing a dynamic framework for tackling risk. The authors comment on the requirements and challenges for developing a DRA framework in highly dynamic environments with frequent threats, vulnerabilities  
Figure 1: PRISMA 2020 identification of studies.  
and technological changes. They justified the need for such an approach because systems should quickly adapt to unstable environments from various IoT sources. As stated, the risk framework should:  
1. Deal with heterogeneous data.
2. Eliminate inconsistency and incompleteness, managing uncertainty errors and missing values, increasing data _reliability_.
3. Reduce the data scale for efficient processing.
4. Provide run-time risk analysis for effective and actionable decision making.
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 3 Method
### Feature Selection  
It has been reported that the hidden semi-Markov model (HSMM) heart sound segmentation method could correctly decompose a cardiac cycle into four parts: S1, systole, S2, and diastole periods [38]. We propose using S1 and S2 signals as the features to train the deep convolutional autoencoder. Thus, for each segment, we null all samples that correspond to the systole and diastole periods and keep other samples in a segment.
## 3 Results
### Estimation of Carbon Sequestration Capacity  
As shown in Figure 5, living AGB and BGB of _P. australis_ ranged from 1.87 to 2.93 \(\times\) 10\({}^{3}\) g m\({}^{-2}\) and 1.75 \(\times\) 10\({}^{3}\) to 2.49 \(\times\) 10\({}^{3}\) g m\({}^{-2}\), respectively. In addition, mortality biomass ranged from 0 to 203.95 g m\({}^{-2}\) and 0.07 to 0.62 g m\({}^{-2}\) for the above- and below-ground part, respectively. Therefore, daily carbon sequestration capacity for the above- and below-ground part varied in the range of \(-\)0.05 to 19.71 gC m\({}^{-2}\) and \(-\)2.58 to 6.67 gC m\({}^{-2}\), respectively. Maximum daily carbon sequestration capacity was attained on the 207 th day. Annual carbon sequestration capacity was 2040.73 gC m\({}^{-2}\) yr\({}^{-1}\), which translated to a total amount of 1766.86 tC being sequestrated annually by _P. australis_ in the HWNR.  
As shown in Figure 6, monthly whole-plant carbon sequestration capacity was found to slowly increase from January to April, which was consistent with the phenological pattern that the above-ground part of _P. australis_ started growing at the end of April (120 th day). Growth of sterile shoot in May led to a rapid increase in the AGB and monthly whole-plant carbon sequestration capacity, i.e., 509% higher than that in April. The monthly whole-plant carbon sequestration capacity reached its peak in July with the value of 528 gC m\({}^{-2}\), which was mainly contributed by the largest monthly carbon sequestration capacity of the above-ground part in July of 577.19 gC m\({}^{-2}\).  
As new peduncles and roots started to form from the end of July (205 th day), below-ground carbon sequestration capacity increased afterwards. Daily below-ground carbon sequestration capacity became positive from the 207 th day, and monthly carbon seques  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Variation (\%)** & **AGB (\%)** \\ \hline Maximum specific net daily & +50 & +56.35\% \\ photosynthesis rate & \(-\)50 & \(-\)58.01\% \\ Specific mortality rate of shoots & +50 & \(-\)17.14\% \\  & \(-\)50 & +21.46\% \\ Fraction of shoot transfer to rhizome & +50 & \(-\)12.41\% \\  & \(-\)50 & +14.73\% \\ Constant of the availability of nutrients & +50 & +8.95\% \\  & \(-\)50 & \(-\)17.38\% \\ Fraction of shoot biomass for elongation & +50 & +7.08\% \\  & \(-\)50 & \(-\)13.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensitivity of model predictions on key modeling parameters.  
Figure 6: The variation in monthly carbon sequestration capacity of _P. australis_ in the Hanshiqiao Wetland Nature Reserve.  
tration capacity of the below-ground part became higher than that of the above-ground part in September. As both the above- and below-ground part continued senesce from the middle of September (260 th day), monthly whole-plant carbon sequestration capacity kept decreasing and became negative in November and December.
## 6 Pairwise Synchronization and Localization
### Iterations  
The output of the second phase, the sound speed will be fed into the third phase which calculates the skew which is used as an input to find the location coordinates of sensors in the fourth phase, and in the fifth or last phase, we find the offset. The process of iteration will continue until the errors of skew, offset and location, decrease, and converge to a constant value.
## 3 The Proposed Method
### Improved EWT Strategy  
The method proposed in Section 3.1 improves the boundary detection mechanism of EWT, and a new method for determining the number of modes of EWT was proposed in Section 3.2. Through the improvement of the EWT boundary detection mechanism and mode number estimation method, they can be better applied to the compound weak fault diagnosis of the rolling bearings. Experiments using a variable speed acoustic signal were conducted to verify the effectiveness of the method. The reason that using the variable speed signal is that the early weak fault of the bearing is more easily exposed during the variable speed phase. The acoustic signal was used because an acoustic sensor can simultaneously monitor multiple parts of the device, and can thus overcome the limited number of sensor installations. Because the bearing speed slowly increases linearly, angular resampling was used in this study to conduct preprocessing. The flow chart of the method proposed in this paper is presented in Figure 4.  
Figure 3: The schematic diagram of the adaptive estimation of the number of modes.  
Figure 4: Flowchart of the proposed improved empirical wavelet transform (EWT) strategy.
# Design of Adaptive Kalman Consensus Filters (a-KCF)
## 1 Introduction
Extensive simulation results aim to demonstrate better effectiveness of a-KCF compared with the previous work of [PERSON][17] along with a faster convergence rate of the estimation error when the consensus gains change adaptively based on the disagreement of these filters. The remainder of this paper is organized as follows. In Section 2, we provide preliminaries on algebraic graph theory [20], which is the basis of the consensus strategy. In Section 3, we provide a retrospective view of the previous work of [PERSON] on the Kalman consensus filtering algorithm, which our analysis relies upon. In Section 4, we illustrate the main results of this paper, namely, derivation of the adaptive Kalman consensus filtering algorithm. The purpose is to adaptively adjust the consensus gain as the weight applied to the disagreement terms in order to improve the convergence of the estimation error. Simulation results are presented in Section 5, then we conclude our work in Section 6. The following notations are be used throughout this paper: \(\mathbb{R}^{n}\) and \(\mathbb{R}^{m\times n}\)denote the \(n\) dimensional square matrices and the set of all \(m\times n\) matrices, respectively. \(I_{m}\) denotes the identity matrix with dimension \(m\times m\). For a given vector or matrix, \(A^{T}\) represents the transpose of the matrix \(A\). For a given square matrix \(A\), \(tr(A)\) denotes its trace and the norm of \(A\) is defined by \(\|A\|=\sqrt{trace(A^{T}A)}\). If \(f\in\mathcal{N}(a,\sigma)\), this indicates a random variable \(f\) that complies with a Gaussian distribution with mean \(a\) and variance \(\sigma\).
# Reverse Electrodialysis: Potential Reduction in Energy and Emissions of Desalination
## Appendix A Salinity Gradient Exergy  
The exergy in RED, given by the Gibbs free energy of mixing (\(\Delta\)G\({}_{\text{mix}}\)), indicates the amount of available energy that can be recovered from a system reaching equilibrium. The specific exergy--the exergy per concentrate solution's volume [70]:  
\[\frac{\Delta\text{G}_{\text{mix}}}{\text{V}_{\text{HC}}}=\ \
u\ R\ T\left(\frac{\Phi}{1 -\Phi}C_{\text{LC}}\ln\frac{C_{\text{LC}}}{C_{\text{M}}}\ +\ C_{\text{HC}}\ln\frac{C_{\text{HC}}}{C_{\text{M}}}\right) \tag{1}\]  
\[C_{\text{M}}=\ \Phi\ C_{\text{LC}}+\ (1-\Phi)\ C_{\text{HC}} \tag{2}\]  
\[\Phi=\frac{\text{V}_{\text{LC}}}{\text{V}_{\text{HC}}+\text{V}_{\text{LC}}} \tag{3}\]  
where \(\Delta\)G\({}_{\text{mix}}\) is the change in the Gibbs free energy of mixing (J), R is the gas constant (8.314 J mol\({}^{-1}\) K\({}^{-1}\)), T is the absolute temperature (K), \(\
u\) is the number of ions each salt molecule dissociates into (2 for NaCl aqueous solutions), V is the volume (m\({}^{3}\)) and C is the concentration (mol m\({}^{-3}\)) of the concentrate(HC) and the diluate (LC) aqueous solution before mixing completely. The concentration of the mixed solution in thermodynamic equilibrium \(\mathrm{C_{M}}\) (mol m\({}^{-3}\)) is given by Equation (A2). \(\Phi\), Equation (A3), is the volumetric ratio of diluate solution's initial volume to total volume of the system.  
The exergy change between RED plant inlet and outlet streams gives the exergy recovered for conversion--i.e., the retrieved exergy, used to capture the potential GHG emissions savings RED could lend to desalination plants in the \"optimistic\" Mix+RED energy supply scenario.
# Beyond Classical AI: Detecting Fake News with Hybrid Quantum Neural Networks
## 3 Results
### Ablation Experiment: Removing the Quantum Layer  
To assess the specific contribution of the quantum layer in the HQDNN model, we performed an ablation experiment by removing the quantum component and training a purely classical variant, HQDNN_NoQuantum. In this version, the input TF-IDF vectors are processed solely through classical fully connected layers, and no quantum encoding is applied.  
The test set results of this comparison are summarized as follows, as presented Table 4.  
These results clearly indicate that the quantum layer significantly enhances the model's recall performance, with a 47.8% drop in recall observed when the layer is removed. This  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **HQDNN** & **HQDNN\_NoQuantum** \\ \hline Accuracy & 0.5652 & 0.6069 \\ Precision & 0.5782 & 0.5586 \\ Recall & 0.9440 & 0.4738 \\ F1 Score & 0.7176 & 0.5127 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model performance with test set.  
Figure 9: The confusion matrix of the MLP model on the test set.  
suggests that the quantum component helps the model capture subtle patterns related to fake news, thereby reducing the number of false negatives.  
While the classical variant achieves slightly higher accuracy and precision scores, this comes at the cost of missing a large number of fake news instances. These findings confirm that the quantum-enhanced model adopts a recall-oriented decision strategy, which may be desirable in critical applications where failing to detect fake content is riskier than flagging false positives.
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
###### Abstract  
With low-power gadgets proliferating, the development of a small, effective rectenna is crucial for wirelessly energizing devices. A simple circular patch with a partial ground plane for RF-energy harvesting at ISM (2.45 GHz) band is proposed in this work. The simulated antenna resonates at 2.45 GHz with an input impedance of 50 \(\Omega\) and a gain of 2.38 dBi. An L-section matching a circuit with a voltage doubler is proposed to provide excellent RF-to-DC transformation efficiency at low power input. The proposed rectenna is fabricated and the results show that the return loss and realized gain have good characteristics at the ISM band with 52% of RF-to-DC transformation efficiency, with an input of 0 dBm power. The projected rectenna is apt for power-up low sensor nodes in wireless sensor applications.  
RF-energy harvesting; voltage doubler; L-section network; rectifier; wireless sensor +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. 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/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 5 Drone Layout Design
### Telescopic Arms with Motors and Propellers  
The drone's frame features eight arms, each equipped with a three-position telescopic capability that allows for user-manual adjustments. These arms are joined together in pairs via two straight pieces through two joints. These two straight pieces are interconnected at a central joint. Rotor positions are located at each free joint, providing the UAV with 12 positions for motor mounting. To facilitate the motor attachment on the drone, a custom 3D-printed motor quick release mechanism has been designed, ensuring a secure and hassle-free motor attachment process. This mechanism comprises two parts. The first part is permanently affixed to the drone, while the second part is attached to each motor. Employing a straightforward plug-and-play procedure, the motor effortlessly connects and secures to the drone through a twisting motion, complemented with an additional pin for enhanced security. The procedure of assembling and disassembling the motor on the UAV is calculated to take approximately half a minute, highlighting the ease and user-friendliness of the proposed implementation (Figure 8b).  
When not fully extended, the intermediate parts of the arms offer an alternative configuration, with the central joint facing either outwards or inwards (Figure 9). In both setups, the UAV can adopt either a quadcopter or a co-axial quadcopter arrangement. For instance, when the central joint faces outward, the UAV is depicted as a co-axial quadcopter in the visualization. Similarly, users can also configure the UAV as a co-axial quadcopter with the intermediate parts facing inward.  
With the central joints facing inward, the UAV is equipped with smaller arms, providing a compact footprint ideal for navigating in relatively tight spaces. Conversely, when the joints face outward, the UAV is provided with longer arms. This increased distance significantly improves agility and flight control, particularly in windy conditions, essential for tasks requiring consistent and smooth aerial  
Figure 8: Telescopic arms with motors: (**a**) Telescopic arm mechanism; (**b**) motor plug-and-play procedure—exploded view.  
Figure 9: Telescopic arm configurations: (**a**) fully extended—october; (**b**) central joint facing outward—co-axial quadcopter (**c**) central joint facing inward—quadcopter.  
The external dimensions of each configuration that the PUL-SAR prototype can accommodate are presented below (Figure 10).
## 3 Results
### Quantitative Estimation of Soil pH and EC  
In accordance with our previous research [16], soil reflectance was highly correlated with soil pH and EC in VIS-NIR ranges of field spectra. In this study, we tested the HSI image bands ranging from 505 nm to 956 nm. Soil alkalinity in the study area was categorized into strongly alkaline (pH \(>\) 9.5), moderately alkaline (9.5 \(>\) pH \(>\) 8.5), slightly alkaline (8.5 \(>\) pH \(>\) 7.5), and non-alkaline (pH \(<\) 7.5) as described by [PERSON] [42]. Similarly, soil salinity was characterized into strongly saline (EC \(>\) 16 dS/m), moderately saline (16 dS/m \(>\) EC \(>\) 8 dS/m), slightly saline (8 dS/m \(>\) EC \(>\) 4 dS/m), and non-saline (EC \(<\) 4 dS/m) as illustrated by [PERSON] and [PERSON] [44]. By combining these two soil properties, we categorized all soil samples into seven pH-EC levels (Table 3) with the averaged values of pH, EC, CO3\({}^{2-}\), and TOC in each pH-EC level. The CO3\({}^{2-}\) contents of soil pH below 8.5 were not calculated. In addition, the observation characteristics of soil surface and geographical background in soil sample sites were also listed in Table 3.  
The spectra of soil samples in each pH-EC level are averaged into one spectrum and demonstrated in Figure 2. Generally, the spectral curves from 505 nm to 956 nm have similar shapes, and the reflectance decreases with low soil alkalinity and salinity levels. The trends are further significant in the 505-567 nm regions. For TOC, the reflectance decreases with soil TOC content increase except in the strongly alkaline and moderately saline level. For CO3\({}^{2-}\), the reflectance further significantly decreases with the decrease in soil CO3\({}^{2-}\) content. Notably, the trends are statistically significant in all levels, including the non-affected soil. So, the short wavelength bands can be used to quantitatively estimate the pH and EC in a wide range of soil salinity and alkalinity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **pH** & **EC** & **TOC** & **HCO3\({}^{-}\)** & **CO3\({}^{2-}\)** \\ \hline pH & 1 & & & & \\ EC & 0.74 & 1 & & & \\ TOC & –0.61 & –0.48 & 1 & & \\ HCO3\({}^{-}\) & 0.19 & 0.25 & –0.07 & 1 & \\ CO3\({}^{2-}\) & 0.87 & 0.85 & –0.58 & 0.16 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation matrix among pH, EC, TOC and two ions.
## Unknown experiments
### Flow Rate Prediction  
**Partially known data**  
The model performed well for flow rate predictions, closely aligning with the experimental setups, as illustrated in Figure 10. The maximum flow rates showed expected trends, such as declines with increasing filter cycles (Figure 10a) and concentrations (Figure 10b). The predictions for varying filter chamber numbers (Figure 10c,d) also matched measured values qualitatively. Quantitative errors for partially known experiments, summarized in Table 4, ranged from MSE 0.339 to 2.150, RMSE 0.582 to 1.466, and RL2N 6.2% to 16.7%, with 77% to 89% of predictions within CI90% bounds. Deviations were linked to specific anomalies. For example, Exp-3 showed initial discrepancies due to clogging, which normalized post-clogging. Exp-5 achieved the best results, closely followed by Exp-12. As seen in Figure 10b, the significant deviation of Exp-5 at the start reflects challenges in initial phase predictions.  
**Unknown experiments**  
For unknown experiments, the prediction errors, detailed in Table 5, were higher, with averages of MSE 8.229, RMSE 2.772, RL2N 15.4%, and PIB 52.25%. The worst predictions were in Exp-2-val, where initial trends deviated strongly (Figure 11a,b) due to limited training data for higher flow rates (7 \(\mathrm{dm^{3}/min}\)). In contrast, Exp-6-val performed best, with deviations confined to the end of the filtration process, caused by sensor limitations below 5 \(\mathrm{dm^{3}/min}\). Experiments at unknown concentrations (15 g/L, Exp-2-val and Exp-3-val) achieved acceptable results, with MSE 4.876 to 9.649, RMSE 2.208 to 3.106, RL2N 12.7% to 17.9%, and PIB 63% to 45%. Figure 11c illustrates the model's ability to approximate overall trends despite missing knowledge for certain configurations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{4}{c}{**Pressure**} & \multicolumn{4}{c}{**Flow Rate**} \\  & \multicolumn{2}{c}{**RL2N**} & \multicolumn{2}{c}{**RL2N-B**} & \multirow{2}{*}{**PIB [\%]**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multicolumn{2}{c}{**RL2N**} & \multirow{2}{*}{**RI2N-B**} \\  & & & & & & & & & **[\%]** & **[\%]** \\ \hline
1 & 0.041 & 0.202 & 4.6 & 0.5 & 96 & 4.955 & 2.226 & 13.4 & 5.3 & 50 \\
2 & 0.011 &  
Figure 10: Comparison of measured (M) and predicted (P) flow rate for the experiments from Table 2. (**a**) shows the reduction in the flow rate with higher cycle numbers over the pressure. (**b**) shows the reduction in the flow rate with different concentrations and similar filter cycles over the pressure. (**c,d**) shows the flow rate with different numbers of filter chambers over the pressure.