esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 6 Conclusions  
With the final goal of filling the gap of ICT technologies between rural and urban areas, this paper proposes a video monitoring platform for wide rural areas not covered by Internet access. In order to be flexible and dynamically adaptive, the platform is designed as a FANET of drones leveraging on the recent SDN/NFV paradigm. This constitutes the backbone of the platform, where a variable number of video transmitter drones and fixed video transmitters can be connected automatically to provide clients with video flows of some requested portions of the monitored rural area.  
First, the paper introduces the platform architecture and the service chains to realize the video delivery service. Then, an analytical model is defined to evaluate the computational load of the platform nodes, and it is applied to a case study for numerical analysis.  
As a future work, we plan to apply the proposed model to define some orchestration and resource allocation policies that the platform orchestrator can apply to manage the entire platform, also in presence of variability in terms of number of drones in the wireless mesh network, their mutual position, and the access to the backbone of the video transmitter drones.  
This work has been partially supported by the INPUT (In-Network Programmability for next-generation personal cloUd service supportT) project funded by the European Commission under the Horizon 2020 Programme (Call H2020-ICT-2014-1, Grant no. 644672).  
[PERSON] has mainly contributed to the definition of the video monitoring platform and has developed the software to derive numerical results. [PERSON] has conceived the scenario and defined the service chains and the analytical model. Both the Authors have designed the experiments and contributed to write the paper.  
The authors declare no conflict of interest.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 2 Compressive Sensing  
Conventional sensors are based on the sampling theorem of Shannon-Nyquist which is based on the following principle: the minimum sampling frequency of a signal that not distorts its underlying information, should be the double of its highest frequency component. However, this theorem which imposes an unnecessary high sampling rate is becoming outdated for applications that require a large amount of data. Thus, the Compressive Sensing paradigm seeks to decrease the rate of the Shannon-Nyquist principle and meets the expectations of the Massive data-intensive applications. To keep it simple, for our application case, a CS camera takes a number of measurements coded from the scene much smaller than the number of reconstructed pixels. In fact, CS is an approach that facilitates the efficient acquisition of the sparse signals where detection and compression are performed at the same time.
## 4 Framework for Reliability Analysis Incorporating Noisy Response Prediction  
### Response Prediction with Noise  
Constructing the predictor for Kriging models applicable to structures with noisy variance entails introducing the following framework. It is assumed that the observed response incorporates noise with a known variance, denoted as \(\varepsilon\), adhering to a zero-mean Gaussian distribution \(\sim\mathcal{N}(0,\Sigma^{*})\), where \(\Sigma^{*}\) is the noise covariance matrix, and can be expressed as:  
\[\mathcal{Y}=\Gamma^{\text{\emph{trig}}}(\mathbf{x})+\varepsilon \tag{18}\]  
Substituting Equation (18) into Equation (7), the formulation of the response prediction model can be expressed as:  
\[\begin{split}\left\{\begin{matrix}\boldsymbol{\mathcal{Y}}( \mathbf{x}_{*})\\ \boldsymbol{\mathcal{Y}}_{D}\end{matrix}\right\}\sim N_{n+1}\biggl{(}\left\{ \begin{matrix}\mathbf{f}^{T}(\mathbf{x}_{*})\mathbf{m}\\ \boldsymbol{\mathcal{F}}\mathbf{m}\end{matrix}\right\}\cdot\left\{\begin{matrix} \sigma^{2}&\sigma^{2}\mathbf{r}^{T}(\mathbf{x}_{*})\\ \sigma^{2}\mathbf{r}(\mathbf{x}_{*})&\sigma^{2}\boldsymbol{\kappa}+\Sigma^{*} \end{matrix}\right\}\biggr{)}\end{split} \tag{19}\]  
Based on Equation (19), the conditional mean and variance of Kriging predictors are then derived:  
\[\mu_{\widetilde{Y}}(\mathbf{x}_{*})=\mathbf{f}(\mathbf{x}_{*})^{T}\widetilde {\mathbf{m}}+(\sigma^{2}\mathbf{r}(\mathbf{x}_{*}))^{T}(\sigma^{2}\boldsymbol {\kappa}+\Sigma^{*})^{-1}(\boldsymbol{\mathcal{Y}}_{D}-\boldsymbol{\mathcal{F} }\widetilde{\mathbf{m}}) \tag{20}\]  
\[\sigma_{\widetilde{Y}}^{2}(\mathbf{x}_{*})=\sigma^{2}-(\sigma^{2}\mathbf{r}( \mathbf{x}_{*}))^{T}(\sigma^{2}\boldsymbol{\kappa}+\Sigma^{*})^{-1}(\sigma^{ 2}\mathbf{r}(\mathbf{x}_{*}))+\widetilde{\mathbf{v}}(\mathbf{x}_{*})^{T} \Bigl{(}\boldsymbol{\mathcal{F}}^{T}(\sigma^{2}\boldsymbol{\kappa}+\Sigma^{*} )^{-1}\boldsymbol{\mathcal{F}}\Bigr{)}^{-1}\widetilde{\mathbf{v}}(\mathbf{x}_{ *}) \tag{21}\]  
where  
\[\widetilde{\mathbf{v}}(\mathbf{x}_{*})=\boldsymbol{\mathcal{F}}^{T}(\sigma^{2} \boldsymbol{\kappa}+\Sigma^{*})^{-1}\sigma^{2}\mathbf{r}(\mathbf{x}_{*})- \mathbf{f}(\mathbf{x}_{*}) \tag{22}\]
# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
## 2 Materials and Methods  
The analysis method proposed focuses on a photovoltaic system constituted by a 230 W solar panel and a Zeta converter connected to a DC microgrid (48 V). The DC-DC converter must guarantee the energy transfer from the source to the grid, and several techniques can be used to achieve this goal, such as the Maximum Power Point Tracking (MPPT) control [26; 27]. The MPPT algorithm's purpose is to control the converter duty-cycle (D) to ensure an optimal operating point is achieved on the PV source. If no condition is required on the output current and voltage, classic MPPT aims at setting the source voltage as close as possible to the maximum power voltage. Since this voltage changes according to the environmental conditions, either a tracking algorithm (e.g., the Perturb & Observe) or a model-based algorithm (also based on machine-learning methods) should be used. In this paper, the MPPT algorithm is not simulated because it does not represent a fundamental aspect of the prognostic analysis. The main idea the monitoring procedure is based on is to fix the duty-cycle for the short time interval necessary to extract the voltage and current measurements. This avoids putting the converter out of service and allows the definition of its state of health without interrupting the energy transfer. Therefore, during the prognostic analysis, the duty-cycle of the converter is not varied to reach the maximum power point, thus limiting the variability of the measurements and facilitating the localization of malfunctions. Once the prognosis is finished, the MPPT algorithm can vary the duty-cycle again. Since the measurement procedure requires only a few periods at the converter switching frequency, the prognostic analysis does not significantly affect the energy production.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 6 Conclusions  
In this paper, we reviewed the IEEE 802.11p standard and described the problems of this standard in time-varying channels. IEEE 802.11p cannot track rapidly changing channel variations because of a lack of pilots for channel estimation. To overcome the channel estimation problems arising from this limitation, various channel estimation techniques that do not need to change the standard have been proposed. However, none of the current channel estimation schemes provide satisfactory performance over the entire range of SNR levels. Therefore, we have proposed an adaptive channel estimation scheme that selectively uses one of two channel estimation schemes with good performance in their corresponding SNR region to obtain outstanding performance over the entire SNR range. The performance for two channel models has been analyzed, and the simulation results demonstrate that the proposed scheme can selectively use two channel estimation techniques regardless of the environment. Nevertheless, the PER of the proposed scheme does not decrease below \(10^{-1}\) in 16 QAM; thus, we plan to continue our research to increase the performance of high-order modulation.  
**Author Contributions:** Conceptualization, J.-Y.C. and C.M.; Methodology, C.M.; Software, J.-Y.C.; Validation, H.-S.J., C.M. and J.-G.Y.; Formal Analysis, J.-Y.C., H.-S.J., C.M., and J.-G.Y.; Investigation, J.-Y.C.; Resources, H.-S.J.; Data Curation, H.-S.J., J.-Y.C. and J.-G.Y.; Writing--Original Draft Preparation, J.-Y.C. and C.M.; Writing--Review and Editing, C.M., and J.-G.Y.; Visualization, H.-S.J.; Supervision, C.M. and J.-G.Y.; Project Administration, C.M. and J.-G.Y.; Funding Acquisition, C.M.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was supported by a grant(19 CTAP-C151968-01) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Towards a Real-Time Description of the Ionosphere:
## 1 Introduction
Corresponding results are represented in the form of grids of values as a function of the local time (LT) and month of the year, for three different levels of solar activity, for the different ionosonde locations. Moreover, the spatial variation of the calculated statistical values has been investigated through the COSMIC dataset. Comprehensive statistical results are provided for the entire ionosonde and COSMIC datasets as distribution of residuals, density plots, and residuals deviation ratio values, allowing us to draw a complete picture of IRI and IRTAM performances in the description of the F2-layer peak characteristics. As far as we know, it is the first time that IRI and IRTAM are cross-validated on the basis of such a large dataset covering very different conditions and locations. Moreover, the use of \(f\)oF2 and \(hm\)F2 datasets from different measurement techniques, such as ionosonde and radio occultation, represents an added value in the validation process because it allows us to validate IRTAM against independent data (i.e., COSMIC RO data) and evaluate how much IRTAM is tied to the assimilated data from ionosondes. A brief description of both IRI and IRTAM models will be provided in Section 2. An overview of the two different datasets used for validation and some information about the runs of IRI and IRTAM models are given in Section 3. The statistics metrics, the binning procedures, and the graphical representation of the results are the subject of Section 4. The validation results for \(f\)oF2 are described in Sections 5 and 6, while those for \(hm\)F2 are described in Sections 7 and 8; the validation shown in Sections 5 and 7 is based on ionosonde data, while that shown in Sections 6 and 8 is based on COSMIC RO data. Final analyses and considerations are the subject of Section 9, while the conclusive remarks are outlined in Section 10.
###### Abstract  
In rivers, the distribution and reactivity of heavy metals (HMs) are affected by their binding affinity with sediment dissolved organic matter (DOM) and particulate organic matter (POM). The HM-OM binding affinity affected by the interaction between DOM and POM is not well studied. This study investigated the Ni binding affinity to size-fractioned overlaying water DOM and alkaline extracted sediment POM solution (AEOM). The DOM/AEOM filtrates (\(<\)0.45 \(\upmu\)m) were sequentially separated into five nominal molecular weight (MW) solutions. The AEOM optical indicators had lower autochthonous, higher terrestrial sources, and lower aromaticity than the DOM. The Ni mass (72.3 \(\pm\) 6.4%) was primarily distributed in the low molecular weight DOM (\(<\)1 kDa), whereas the Ni (93.5 \(\pm\) 0.4%) and organic carbon (OC) mass (85.3 \(\pm\) 1.0%) were predominantly distributed in the high molecular weight AEOM. The Ni and DOM binding affinity, ([Ni]/[DOC])\({}_{\text{DOM}}\) ratio ranging from 0.76 to 27.32 \(\upmu\)mol/g-C, was significantly higher than the ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios, which ranged from 0.64 to 2.64 \(\upmu\)mol/g-C. The ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratio correlated significantly with the selected optical indicators (r = 0.87-0.92, \(p<0.001\)), but the ([Ni]/[DOC])\({}_{\text{DOM}}\) ratio correlated weakly with the optical indicators (r = 0.13-0.40, \(p>0.05\)). In the present study, the Ni binding affinity with size-fractionated DOM/AEOM agrees with the hypothesis of the DOM and POM exchange conceptual model in sediment. The POM underwent a hydrolysis/oxidation process; hence, AEOM had a high molecular weight and stable chemical composition and structure. The Ni mainly attached to the high molecular weight AEOM and the ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios had a strong correlation with the AEOM optical indicators. In contrast, DOM had a high ([Ni]/[DOC])\({}_{\text{DOM}}\) ratio in low molecular weight DOM.  
**Keywords:** nickel; dissolved organic matter; particulate organic matter; heavy metal binding affinity; optical indicators
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 3 Results and Discussion
### Straight-Line Trajectory Design Comparison  
In the first comparison, we evaluate the impact of using Dubins-like trajectories proposed in this paper versus straight-line paths between waypoints. For each method, travel time is calculated based on the distance travelled and is used to guide the forward DP algorithm in selecting the optimal waypoints and their sequence. The straight-line strategy connects waypoints using Euclidean paths, assuming instantaneous heading changes. In contrast, our approach accounts for the AUV's turning constraints, generating feasible trajectories that comply with its kinematic limitations.  
As shown in Figure 6 and Table 2, the proposed method completes the mission 0.7% faster and preserves 0.3% more VoI, increasing the final VoI ratio from 65.75% to 65.95%. This improvement demonstrates that even small reductions in travel time can yield measurable gains due to the exponential decay of VoI. Moreover, the proposed trajectory avoids mismatches between planned and actual motion, enhancing overall mission efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(i\) & **Initial VoI \(\boldsymbol{\mathcal{V}}_{int_{i}}\)** & **Residual Vol \(\boldsymbol{V}_{r_{i}}\)** & **Preserved Vol Ratio \(\frac{\boldsymbol{V}_{r_{i}}}{\boldsymbol{V}_{int_{i}}}\)** \\ \hline
1 & 0.8336 & 0.5507 & 66.1\% \\
2 & 0.9626 & 0.6175 & 64.1\% \\
3 & 0.7272 & 0.4921 & 67.7\% \\
4 & 0.6432 & 0.4437 & 69\% \\
5 & 0.9518 & 0.6121 & 64.3\% \\ Total & 4.1184 & 2.7161 & 65.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Final total residual VoI and corresponding preservation ratio.  
Figure 6: Comparison of the proposed (green solid line) and the straight-line (red dash-dot line) path designs: (**a**) 3D trajectories and USV positions; (**b**) 2D x–y projection.
## 4 Discussion
### Benthic-Pelagic Coupling: Nutrient Release from Sediment and Effects on the Plankton
This nutrient regime in the warm treatments therefore has similarities with the Baltic Sea nutrient regime after the spring bloom, where N and P are depleted and the sedimentation of organic material leads to increased microbial activity, leading to anoxia, which in turn results in an increased P release by the anoxic sediment [35]. While our microcosms did not turn anoxic, filamentous nitrogen-fixing cyanobacteria _Dolichosperm_ sp. increased from relatively low numbers at t6 to the most abundant species in the warm treatments at t13. As P is the primary limiting nutrient for N-fixation, the increase in phosphorus concentrations, through PO\({}_{4}\) release from the sediment, triggered a rapid increase in _Dolichosperm_ sp. abundances [36] and in the number of nitrogen-fixing heterocysts present. While cyanobacteria blooms are generally considered to be harmful, recent studies indicate that the N fixed and released as NH\({}_{4}\) by cyanobacteria might actually be beneficial for other primary producers (i.e., [35]). Stable isotope analysis and nanoscale secondary ion mass spectrometry (nanoSIMS) have shown that N derived from cyanobacterial N-fixation can be taken up by diatoms in the form of NH\({}_{4}\). Furthermore, recent studies indicate that cyanobacteria can be a direct food source for meso- and microzooplankton. Stable isotope analyses show that cyanobacteria are a major food source of copepods species such as _Acartia_ spp. [37]. This could explain the positive relationship of _Acartia_ with increasing chlorophyll a concentrations, as this increase was most likely driven by _Dolichosperm_ filaments and therefore represents an increase in food items. The increase in rotifers was most likely due to an increase in abundance of a potential food species, _Melosira_ sp., in ambient temperature treatments, or could be explained by predator-prey release. Copepods are known to not only graze on phytoplankton but also prey on other zooplankton species including rotifers [38]. Due to an increase in PO\({}_{4}\) and a consecutive cyanobacteria increase in the warm treatments, _Acartia_ increased in the warm treatments, while abundances in the ambient treatments stayed relatively low, which in turn resulted in lower predation pressure of rotifers in the ambient treatments. The overwhelming effect of temperature-induced PO\({}_{4}\) release, and the resulting changes in the phytoplankton and pelagic primary production, was probably responsible for the significant correlations observed at t6 between the benthic primary production and PO\({}_{4}\), chlorophyll a, and some phytoplankton species. It is unlikely that these correlations are a cause-effect relationship; rather, they are probably reflecting the common increase in rates of ecological processes in the warm treatments at that time point (Figure 10).
# EEG Emotion Classification Based on Graph Convolutional Network
## 2 Literature Review
### Deep Learning Approach
To improve the performance on both the spatial and temporal level, hybrid networks are used that are composed of different types of networks. From the perspective of signal decomposition, Ref. [28] proposes a model that derives the source signal by stack autoencoder (SAE). Next, the sequenced features are fed into the LSTM network to learn the contextual correlation. Ref. [29] proposes a model that first captures spatial features by convolution layers at each timestamp and feeds them into the LSTM layer. The novelty in this work is that it adopts an attention mechanism in both stages to capture which channel or which timestamp contributes more in the process of emotion recognition. Ref. [30] employs a combination of GNN and LSTM, where GNN is responsible for learning static graph-domain features and LSTM extracts effective information from the channel-level relationships in a short range of time. Recently, the study of spatial-temporal graph learning has also been employed in EEG emotion classification. Ref. [31] integrates the spatial graph convolutional network with an attention-enhanced bi-directional LSTM module. This type of model better combines the temporal information to learn the features. In addition to the aforementioned approach, some other novel methods have emerged in the field of EEG-based emotion classification. The methods provide different directions for advancing the deep learning techniques on EEG emotion classification. One of these methods, Ref. [32], focuses on a real-time method, which employs online learning techniques, including adaptive random forest, streaming random patches and logistic regression. Ref. [33] utilizes a capsule network to extract hierarchical features from the EEG signal, where each emotional capsule associates with an individual task. To enhance the power of multi-task learning, it uses the dynamic routing algorithm to achieve information exchange between primary capsules and emotional capsules. Recently, reinforcement learning has gained attention in EEG emotion classification as well. An example is [34], which is a reinforcement learning-based method that combines the idea of Papez circuit theory and uses EEG signals from the frontal lobe to simulate brain mechanisms. The key contribution in this approach is the utilization of a double dueling deep Q network, which enhances the decision-making process with more informed choices. These various methods have significantly advanced the field of deep learning for EEG emotion classification.
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 3 Results and Discussion
### Texture-Friction Relationship Analysis
These measurements were recorded on measurement sections MS 9 and MS 10, where the surface layers have low levels of both microtexture and macrotexture, resulting in low friction values. Therefore, these measurements are considered accurate, not as outliers. In contrast, sections MS 6, MS 7, MS 12, and MS 13 exhibit similar grain shapes with consistent \(S_{pc,MAC}\) values but different friction coefficients due to varying microtexture levels, impacting the overall correlation strength. Despite this, \(S_{pc,MAC}\) was included in the friction prediction model, as grain shape undoubtedly affects the wheel-road interaction in terms of friction. Comparative analysis of values obtained from texture and friction measurements led to the following preliminary conclusions:  
* The relationship between \(S_{mZ,MIC}\) and \(\mu\) confirmed the undeniable impact of surface microtexture on skid resistance;
* The influence of macrotexture on the friction was best demonstrated using the parameter arithmetic mean peak curvature (\(S_{pc,MAC}\)), as evidenced by a moderate-strong relationship with friction coefficient \(\mu\). A more pronounced effect of this parameter was observed when combined with the microtexture parameter \(S_{mZ,MIC}\) in further analysis;
* Under the given test conditions, characteristics related to the surface features' density, as well as some volume characteristics, did not exhibit as strong correlations as anticipated;
* It was confirmed that high values of individual texture characteristics, both on primary and micro and macrotexture surfaces, do not necessarily represent high friction coefficient values;
* It has been demonstrated that predicting the friction coefficient based solely on individual texture parameters from non-contact measurements is insufficient. Building upon these preliminary conclusions, the subsequent phase of the research examined the impact of combining surface texture parameters. Figure 11: Correlation between the arithmetic mean peak curvature \(S_{pc,MAC}\) and friction coefficient \(\mu\).