esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The performance was predicted and its usefulness was explored in aiding suitable site selection among four sites. [PERSON] et al. [14] provided a dynamic model of a PTSPP without power block simulation using Wolfram's Mathematica 7 programme. The model deals only with the thermal oil behaviour within the SF and TSS. The results were compared with experimental data for a 50 MW\({}_{\text{el}}\) of PTSPP in Spain during summer. [PERSON] et al. [15] dynamically simulated the optical performance of the collector platform. The floating stability of the existing platform design was experimentally verified, and the performance influence to go offshore was small. [PERSON] et al. [16] dynamically implemented a model PTSPP by DYMOLA. A transient behaviour was evaluated for the start-up case based on thermal oil behaviour. This model was checked with the Epsilon model since there were no experimental data to investigate the model. [PERSON] et al. [17] built a dynamic parabolic trough collector model and validated it depending on the photothermal conversion process for a parabolic trough technology. Together with the verified model with pumps, the thermal oil/water heat exchangers and other existing components, a solar field was simulated. The review research reveals that most modelling studies found in the literature are focused on the work of specific parts of the PTSPP, such as the solar field or the thermal storage system or the power block during certain periods (clear periods). In addition, they do not address the operation of the control units and the operational strategy used in these power plants. In this work, a detailed dynamic model of the PTSPP (Andasol II) with all control circuits is developed employing (APROS) software. The dynamic behaviour of the investigated power plant is assessed during a strongly cloudy day. The solar data is computed depending on satellite measurements and databases from ground weather stations. Specifically, the novelty of this study can be summarised as follows:  
1. A detailed dynamic model of a PTSPP, including the SF, the TSS, and the PB, has been extended based on our previous studies presented. In addition, the dynamic behaviour of the thermal oil path of the power plant during heavily cloudy days in summer is discussed. The steam behaviour in the high pressure (HP)/low pressure (LP) turbine stages is also investigated. Such dynamic simulation modelling cannot be found in the literature. 2. This model can also determine the best operating strategy of the power plant, taking into account the DNI variations during the day, compared to the decisions made by operators in the absence of a dynamic modelling approach. 3. For model validation, the simulation results for the most important properties of the solar field, storage system, and power block are compared with measurements. The comparative work is of great interest to designers of PTSPP and scientific researchers.
## 5 Discussions and Conclusions
From these findings, two actionable interventions arise: (1) encouraging early engagement with multimedia resources to support lower-performing students and (2) providing targeted assistance with simulations and workshops as students progress through the course. These strategies could help improve learning outcomes and support students in achieving better performance as they advance. Clustering students based on top features resulted in distinct groups with identifiable engagement and performance patterns. The consistent results from [PERSON]'s post hoc tests validate the robustness of our clustering approach in capturing meaningful differences in student performance, and highlight the value of feature importance analysis in learning analytics. Overall, our study demonstrates the effectiveness of machine learning techniques in predicting student performance in non-linear Moodle courses. The insights gained from our analysis, including using FATP and CWA thresholds, offer a framework for future research and for designing targeted intervention strategies that enhance student engagement and academic outcomes. The recurring patterns identified across various \(\text{CWA}_{U}\) thresholds provide educators with a structured approach to guiding students toward improved performance. These results also address several gaps identified in prior research. By introducing FATP based on students' progress rather than course time or assignment availability, we provide a practical solution for performance prediction in non-linear, self-paced courses, an issue overlooked in existing PTP approaches. Moreover, our integration of feature importance analysis with clustering and model retraining responds to calls in the literature for more robust validation of selected features. Finally, our work demonstrates the synergy between these areas by combining performance prediction and pattern analysis within a unified framework. It extends current learning analytics methodologies to better support adaptive interventions. Despite these contributions, our study has limitations. We focused on a single self-paced course, which may affect generalizability, and the sample size of the final dataset may reduce stability.
## 6 Conclusions  
In this work, we studied the potential of Sentinel-1 remote sensing data for rice height and dry biomass estimations. We proposed to use three classical machine learning approaches to predict rice parameters from Sentinel-1 data, which were applied in the Camargue region.  
We demonstrated that good regression performance could be yielded with Sentinel-1 data even with the classical approaches (MLR, SVR, and SVR). We found that non-parametric methods (SVR and RF) had better performance over the parametric MLR method for estimating rice parameters. Between the SVR and RF models, the RF method obtains slightly better results than the SVR. Correlation coefficient \(R^{2}\) and RMSE were 0.90 and 18% (162 g\(\cdot\)m\({}^{-2}\)) for rice dry biomass, 0.92 and 16% (7.9 cm) for rice height, which indicated a high accuracy of Sentinel-1 SAR retrieval. In other words, we demonstrated that Sentinel-1 remote sensing data could be an alternative and reliable approach to monitor regional rice height and estimate dry biomass, compared with direct field measurements. Future work on the Camargue region could be focused on exploiting Sentinel-1 data to improve crop models to better estimate rice production yields in order tobe able to propose strategies for sustainable agricultural development.  
In the context of the Copernicus program, there are two missions: radar Sentinel-1 and optical Sentinel-2. In future research, the data combination from the two satellites will be a necessity as it will be possible to carry out large-scale missions. Radar and optical data can be complementary to each other because they offer different perspectives on the Earth's surface providing different information content according to their specificity [7; 8; 58]. Both types of data can also be merged to provide information from multiple sources and to provide improved results for decision making [59].  
E.N. and D.H.T.M. developed the main idea that led to this paper. E.N., D.N.H.T. and I.E.M. analyzed SAR data for rice parameters estimations and their descriptions. E.N. collected the reference data and was the main author of the paper. INRA has provide the main contacts with farmers on the Camargue area. All authors read and approved the final manuscript.  
This research received no external funding.  
This work was supported by Centre National d'Etudes Spatiales. We thank the government of Burundi for funding the PhD research of [PERSON]. The authors were grateful to [PERSON] and [PERSON] for the authorisation of accessing their rice parcels.  
The authors declare no conflict of interest.
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 2 Modeling of Distributed Power System
### The Structure of a Typical Distributed Power System  
As shown in Figure 1, a distributed power system is composed of a distributed generation system, a hybrid energy storage system, and the loads. Distributed generation mainly includes two parts: the distributed renewable energy generation and the fossil energy generation. As two typical types of renewable energy, wind power and PV are extensively installed in distributed power systems because of their environmental friendliness and easy maintenance characteristics. However, both of their outputs can be greatly influenced by the weather, which may cause network power fluctuation and bring harm to the safety operation of the power grid [22; 23; 24]. Therefore, in distributed power systems, a fossil energy generation system is usually equipped to alleviate renewable energy system output power fluctuation. In the following sections, the diesel generator will be used as an example to show the mathematical output power and carbon emissions models of a fossil energy generator equipped in the proposed power system.  
A distributed energy storage system, as the most important part of distributed power systems, is commonly used to shave peak demand, improve the renewable energy penetration rate, and smooth the renewable generation output power curve. However, as mentioned in the introduction, when different optimization time scales are selected, the meteorology (fluctuation characteristics) and the strong uncertainty of distributed power systems can cause different optimal results of energy storage installation capacity and operation strategy. Previous studies have preferred to take local meteorology characteristics as the key factor and select a longer time scale (for example: hour-based optimization) to configure the capacity of an energy storage system and optimize the operation of the energy storage system, which ignores the strong uncertainty (minute-based optimization) of the distributed generation [25; 26]. Therefore, by taking the uncertainty and meteorology characteristics of the distributed power system into account, this paper introduces a super-capacitor and battery hybrid energy storage system to optimize the energy storage installation capacity and operation strategy.  
As the end of distributed power system, the loads section is also an important part of power systems. There are many ways to classify the loads, such as AC and DC loads, uncontrollable and controllable loads, etc. In this paper, in order to optimize the energy storage system configuration and provide feasible operation strategies, this paper only monitors the active power of the distributed power system and weakens the load classification.
# Analysis of Salt Mixture Contamination on Insulators via Laser-Induced Breakdown Spectroscopy
###### Abstract  
The composition of contamination deposited on transmission line insulators can affect their surface flashover voltage. Currently, there is no rapid on-line method to detect this contamination composition in power grids. In this paper, we applied laser-induced breakdown spectroscopy (LIBS) to analyze contamination on insulator surfaces. Usually, Na and Ca salts are found in contamination along with various sulfate, carbonate, and chloride compounds. As an element's detection method, LIBS can only measure a certain element content, for example, Ca. The mixture of various compounds with the same cations can influence the LIBS signal. The influence of mixing ratios on the calibration curves and relative spectral intensity was studied via LIBS. Na\({}_{2}\)CO\({}_{3}\), NaHCO\({}_{3}\), CaSO\({}_{4}\), and CaCO\({}_{3}\) samples containing different proportions of Na and Ca were prepared. The linear correlation coefficients (R2) for the Na and Ca calibration curves generated using various mixing ratios were analyzed. The results showed that the mixture ratio did not dramatically affect the linear calibration curves for mixtures containing the same cations. This finding may significantly reduce the difficulty of applying LIBS analysis for complex contamination on insulators. The laser energy density had effects on the spectral characteristics of the measured elements. The partial least-square regression (PLSR) model can improve the accuracy of Na and Ca prediction.  
laser-induced breakdown spectroscopy; contamination; insulators; salt; calibration curves 2020 10 2617 2020 10.3390/app1072617
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Cell-Laden Commercial Skin Templates
#### 3.1.1 Placental Membranes  
The placental membrane contains epithelial cells, neonatal fibroblasts, and mesenchymal stem cells (MSCs), promoting effectively wound healing [28]. MSCs produce factors that stimulate migration and proliferation of the main cell types in the wound healing process. MSCs also release the hepatocyte growth factor (HGF) and vascular endothelial growth factor (VEGF) to stimulate vascular network formation and provide anti-scarring properties, respectively [29]. Different studies have illustrated that MSCs improve tissue repair and skin regeneration and play a critical role in the major wound healing phases [30]. Additionally, MSCs can differentiate into multiple cell types and produce pro-regenerative cytokines. Therefore, MSCs-based skin substitutes provide an alternative option to conventional treatments for skin repair.  
Grafix (Osiris Therapeutics Inc., Columbia, MD, USA) represents another commercially available placental-based cryopreserved allograft. It is used in different acute and chronic wounds such as diabetic foot ulcers, epidermolysis bullosa, burns, and surgical incisions and dehiscence [30; 31]. Grafix employs the native constitutes of placental tissue,  
Figure 1: A schematic classification and summary of different commercial and bioengineered skin substitutes for large and hard-to-heal wounds.  
producing a 3D extracellular matrix (ECM) that includes various living cells including epithelial cells, fibroblasts, and MSCs [30]. Importantly, included fibroblasts also actively secrete ECM proteins along with growth factors, enriching the environment for epidermis formation and wound contraction [31]. Different clinical studies demonstrated that the treatment of difficult-to-heal chronic wounds with Grafix led to high wound closure rates, suggesting that Grafix represent a promising treatment option for wound therapy [30; 31; 32; 33].
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Model Training
Figure 4d shows that ResNet and EfficientNet both converge to an approximate value of 0.4 while MobileNet converges to an approximate value of 0.6 for the total loss. The increased value of total loss for MobileNet can be attributed to the regularization loss which converges to a relatively large value in comparison to the localization loss and the classification loss for MobileNet. Thus, the significantly higher value of regularization loss for MobileNet skews the value of total loss, despite the model's significantly lower values for classification loss and localization loss.
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 4 Suppression of Harmonic Current in AMB with Redundant Structures  
The idea of repetitive control (RC) is derived from the internal model principle of control theory. In essence, repetitive control uses the periodic compensation of errors to realize the tracking of the desired signal and suppression of disturbance signals. According  
Figure 5: Closed-loop equivalent control schematic diagram of the entire system.  
Figure 6: Equivalent control schematic diagram in the \(x\)-direction.  
to the characteristics of the disturbance signals occurring in the magnetic bearing-rotor system, repetitive controllers can effectively suppress harmonic currents. Furthermore, the design of the repetitive controller can be independent of the design of the original system controller. For a system that does not include a repetitive controller, there is no need to make any changes to the original system parameters; one can simply insert the repetitive controller into the original system and ensure that the new system meets the stability conditions.
## 2 Design and Simulation
Figure 3b-d show the FDTD simulation results of the MDM optical power divisions at TE\({}_{0}\), TE\({}_{1}\), and TE\({}_{2}\) modes, respectively, launched from the left-hand side and outputted at the right-hand side. Without loss of generality, we use the TE\({}_{0}\) mode as an example. If the TE  
Figure 2: (**a**) Schematic of the SOI based ADC MDM Mux/Demux. FDTD simulation result of (**b**) TE\({}_{0}\) to TE\({}_{0}\), (**c**) TE\({}_{0}\) to TE\({}_{1}\) and then back to TE\({}_{0}\), (**d**) TE\({}_{0}\) to TE\({}_{2}\) and then back to TE\({}_{0}\). mode needs to be power divided, it will be mode up-converted to the TE\({}_{1}\) mode before power division, as shown in Figure 3b. Then, two equal power TE\({}_{0}\) modes can be produced at the output of the Y-bench. Similarly, the TE\({}_{1}\) and TE\({}_{2}\) are mode up-converted to the TE\({}_{3}\) and the TE\({}_{5}\) mode through the ADCs. Then, the Y-bench would split TE\({}_{3}\) and TE\({}_{5}\) modes to two 50% power TE\({}_{1}\) and TE\({}_{2}\) modes, as shown in Figure 3c,d. In the experiment, since we cannot measure the output TE\({}_{1}\) and TE\({}_{2}\) modes of the MDM power divider directly, they will be further converted to TE\({}_{0}\) modes via ADCs, as illustrated in Figure 3a. As a result, there are six outputs from the MDM optical power divider. Figure 3e shows the photograph of the MDM optical power divider, showing the magnified section of the ADCs for mode conversions, and the six TE\({}_{0}\) outputs at the right part of the device.
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion  
### Battery Life  
PSM and eDRx were introduced in NB-IoT Release 12 and 13 to lengthen the NB-IoT devices' battery life. Moreover, the most recent updates require the UE to be able to transmit during RRC-idle mode which will reduce the required ON time for data transmission. However, devices experiencing bad channel conditions due to hard-to-reach areas will require to perform several retransmissions per session, which will drain the device's energy and hence shortens the battery life. Similarly, devices that require a relatively large number of reporting sessions per day will consume more energy, which makes energy management a concern. As seen in Section 3, most of the proposed algorithms are power hungry because most of the power is consumed during transmission and reception. Therefore, energy harvesting alternatives such as solar, biogas, vibrations, etc. that will lengthen the NB-IoT device battery life should be introduced to complement or replace frequent battery charging.
## 4 Methods Used for Fault Diagnosis
### DC Fault Detection Based on Voltage Prediction  
This fault protection strategy was developed based on the use of local measurements and the characteristics of the system parameter. This method does not require any kind of communication system, and works based on the deviation of the actual and predicted voltage. The deviation value depends on the location and type of the short circuit. In fault situations, a big gap between the actual and expected parameters will happen. Thus, a protection system design framework and a mathematical model was created for fast fault detection and isolation. Here, the faults are detected and isolated by using voltage data which is provided at the line side of the protective devices. Additionally, the Fault Current Limiter (FCL) method is also implemented for temporary fault circumstances. Although the fault-current limiting devices can efficiently limit a huge DC current and comparatively provide a low-cost protection system, it causes a longer detection time and interruption of the fault current [28].
## 4 Proposed 3D-UWSN Design
### Data Redundancy Elimination  
Data redundancy might occur when inconsistent data duplicates are retransmitted from the sensor nodes; therefore, the existence of duplicate or unnecessary data should be resolved because it can be costly in terms of energy and time consumption. During data gathering by the CH in each cluster, the amount of data can sometimes be large due to the redundant data combined from different sensing nodes in the neighborhood. Thus, the data gathered need to be processed before being transmitted in order to detect and remove redundancy, which can impact the communication traffic and energy consumption of the network in a negative way. We propose an algorithm to measure similarity between the data collected (relative to specific event monitoring) so that an aggregator sensor sends a minimum amount of information and eliminates the duplicate data in a way that means the the latter can deduce the source information of sensing neighbor nodes.  
Before transmitting the data, redundancy is minimized by evaluating the similarity between the sensor measurements. The Hassanat distance measure is mathematically formulated as [48]:  
\[HasD\big{(}s_{i},s_{j}\big{)}=\sum_{1}^{k}D\big{(}s_{i},s_{j}\big{)} \tag{10}\]  
\[D\big{(}s_{i},s_{j}\big{)}=\left\{\begin{aligned} & 1-\frac{1+min\big{(}s_{i},s_{j} \big{)}}{1+max\big{(}s_{i},s_{j}\big{)}},\quad\quad min\big{(}s_{i},s_{j}\big{)} \geq 0\\ & 1-\frac{1+min\big{(}s_{i},s_{j}\big{)}+\big{|}min\big{(}s_{i},s_{ j}\big{)}\big{|}}{1+max\big{(}s_{i},s_{j}\big{)}+\big{|}min\big{(}s_{i},s_{j} \big{)}\big{|}},\quad min\big{(}s_{i},s_{j}\big{)}<0\end{aligned}\right. \tag{11}\]  
These distance values are in the range \([0,\,1]\); a larger value denotes that the data from the two sensors are similar, and, hence, one set of data is eliminated. In this way, the data redundancy is reduced, which also minimizes the amount of gathered data, which, in turn, reduces the energy consumption in data transmission. The CH delivers the collected data only after eliminating the redundancy.
## 2. Mature Technologies for Water and Wastewater Remediation
### Ozonation
#### 2.2.2 Ozonation Process  
A high-voltage electric discharge is applied to a stream of oxygen or air. The energy from the discharge splits the oxygen molecule into two oxygen atoms [20]. These unstable atoms then combine with other oxygen molecules to form ozone gas (O\({}_{3}\)). Ozone is a powerful oxidiser that decomposes in water and reacts with metals, organics, inorganics and pathogens to form insoluble compounds that can be filtered out. An example of this interaction is displayed in Equation (3), where manganese is oxidised to form a precipitate [21]:  
\[\text{Mn}^{2+}+\text{O}_{3}+\text{H}_{2}\text{O}\rightarrow\text{MnO}_{2}+ \text{O}_{2}+2\text{H}^{+} \tag{3}\]