esa-sceva/satcom-chunk-collection huggingface dataset:
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 1 Introduction
We will also show the results of the application of the proposed methodology during the November 2010 event in the Bradano River mouth to identify the best fit between hydrodynamic model flood predictions and (multitemporal) SAR- and DEM-derived hazard maps (Section 4.1). In Section 4.2, we present the validation of this type of calibrated hydraulic model in the same area (but for the December 2013 event) to show the efficiency of SAR data in flood hazard assessment. Finally, Section 5 presents the overall conclusions of this work.
# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 5 Conclusions  
This work proposed a method for automatically generating a virtual cohort of 3D cerebral arteries. The method allows one to reconstruct cerebral vessels with good accuracy and in a short time, allowing for the future generation of large cohorts of virtual patients for the in silico simulation of stroke treatments.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app131810074/s1](https://www.mdpi.com/article/10.3390/app131810074/s1)]([https://www.mdpi.com/article/10.3390/app131810074/s1](https://www.mdpi.com/article/10.3390/app131810074/s1)), INSIST Investigators, MR CLEAN Registry Investigators, and MR CLEAN Trial Investigators.  
Conceptualization, S.B., G.L., J.F.R.M. and F.M.; methodology, S.B. and G.L.; software, S.B. and G.L.; validation, S.B., A.R. and C.A.L.; investigation, S.B. and G.L.; data curation, P.K. and N.A.T.; writing--original draft preparation, S.B., G.L., A.R. and C.A.L.; writing--review and editing, J.F.R.M., G.D., M.N., P.K., N.A.T., H.A.M., C.B.L.M.M. and F.M.; supervision, G.D., M.N., H.A.M., C.B.L.M.M. and F.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 777072 and from the MIUR FISR-FISR2019_03221 CECOMES. F.M. is partially supported by the MUSA--Multilayered Urban Sustainability Action--project, funded by the European Union--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\", set up of \"territorial leaders in R&D\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Acknowledgments:** We would like to thank the INSIST, MR CLEAN Registry, and MR CLEAN trial investigators for their contributions. A list of the investigators from all three consortia is presented in the Supplementary Material. We would like to thank Parametric Design s.r.l for its contribution to the CAD reconstruction within the PON REACT-EU project.  
**Conflicts of Interest:** The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: H.A.M. reports being a co-founder and shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical image analysis. C.B.L.M.M. received funds from the European Commission (related to this project, paid to institution) and from CVON/Dutch Heart Foundation, Stryker, TWIN Foundation, Health Evaluation Program Netherlands (unrelated; all paid to institution). C.B.L.M.M. is a shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical imaging analysis. H.A.M., N.A.T. and P.K. are co-founders and shareholders of inSteps B.V.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 4 Results
### Potential of the Combined Use of SAR and MODIS for Snow Monitoring
The evaluation of the additional SCA contribution provided by the HRS&I SWS product in the Pyrenees reveals relatively low variability during snow accumulation and stabilization phases but exhibits significant variations during snowmelt (Figure 8b). From late autumn through winter, the additional SCA contribution generally remains below 21%. Among these months, November exhibited the least variability (0.1-7.1%), while December showed the highest variation (0.5-17%). However, occasional outliers reaching peaks close to 100% were observed, indicating significant cloud cover on certain days. As mentioned earlier, the variability range widens notably during the snowmelt months (ranging from 3.4% to 74.2%) (Figure 8b).
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 3 Results and Discussion
### Catalytic Performance Test Using Sequential Batch Experiments  
#### 3.2.1 The Removal Performance of COD in Different Systems  
This study investigates the COD removal performance of FSC/Fe(VI), FSC/H\({}_{2}\)O\({}_{2}\) system, Fe(VI)/H\({}_{2}\)O\({}_{2}\) system, and FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system by a sequential batch experiment. Figure 4a shows the adsorption performance of FSC for COD. After 2 h, adsorption reached equilibrium, and only 1.4 mg COD was removed due to adsorption onto the catalyst surface. The adsorption phenomenon is related to the porous structure on the surface of the catalyst as described in Section 3.1. However, the removal rate of COD is only 4.3% by the adsorption of FSC, which can be ignored. Figure 4b shows the removal performance of COD in different oxidation systems under the same total oxidant concentration. In the FSC/Fe(VI) system, the COD degradation rate is the lowest, at only 11.69%, indicating that the activation performance of FSC for Fe(VI) is poor. The FSC/H\({}_{2}\)O\({}_{2}\) system has a higher COD removal efficiency than the FSC/Fe(VI) system, reaching 43.03%. This is because FSC can activate H\({}_{2}\)O\({}_{2}\) to generate hydroxyl radicals (\"OH) with strong oxidizing properties, thereby non-selectively attacking organic substances through hydrogen extraction and electron transfer pathways, significantly enhancing COD removal efficiency [25]. Moreover, the removal rate of the Fe(VI)/H\({}_{2}\)O\({}_{2}\) dual-oxidant synergistic system is only 28.57%, indicating that the oxidation effect of the single oxidant is poor. Surprisingly, the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system achieved a 57% COD removal rate, and the removal rate exceeded 43.03% for the FSC/H\({}_{2}\)O\({}_{2}\) system. It also surpassed the 30% removal rate of the single-oxidant Fenton system in real wastewater [26]. The excellent COD removal effect might be attributed to the possible synergistic effect between Fe(VI) and hydrogen peroxide in the presence of FSC.
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 3 Materials and Methods  
This work is focused on modeling the dynamic of the oxygen in the reactor, in particular through the use of the LSTM structure because of the promising results it has obtained in several different fields. In the physical models ASM1, ASM2, and ASM3, the oxygen concentration (output), \(O(t)\), is mainly connected to a few manipulable variables (input): the inlet flow of the wastewater, \(q_{in}(t)\), and its Chemical Oxygen Demand, (\(COD_{in}(t)\)); the oxygen inlet flow, \(q_{\mathrm{oss}}(t)\); the outlet flow of the clarified liquid filtered by the membrane, \(q_{ol}\), \((t)\), and the outlet flow manually removed from the reactor, \(q_{om}(t)\).  
Even if this notion is general, as clearly stated in the Introduction, every plant has its own peculiarities, due to the local law limits, design constraints, the average chemical composition of the waste and other factors [19]. This fact should require a model identification that is typically impossible in view of the limited number of variables measurable on a real plant. Black-box models, and in particular NNs, can be a reasonable compromise to overcome this limitation.  
In the following, the plant is described in detail; then, the proposed LSTM is reported and compared with a classic ARX.
## 4 Results and Discussion
### Soil Water Retention Curve Measurement Compared to SWRC Using the Standard Method  
Finally, the suction and moisture logging collected at equilibrium are used to plot the SWRC shown in Figure 11 in comparison to the SWRC measured by the standard hanging column method. In Figure 11, there is a slight difference in initial density between the two methods of around 2-3%. The zone of residual moisture content is around 8-9% for both methods, whereas the capillary storage (moisture content changing with soil suction) and AEV show significant differences between these two methods. Even the porosity controlled for the standard method is higher, the AEV is 1.5 kPa higher than the AEV by the large column test. In fact, the larger porosity should provide a smaller AEV.  
Moreover, due to the smaller AEV achieved in the large column test, the slope of SWRC is extended, so there is rather a gradual reduction in moisture content by increasing the soil suction than the sharper wetting front measured by the standard hanging column method. Previous studies on the spatial variation in suction calculated using the height difference between a ceramic disk and water table have demonstrated the sensitivity of suction determination by varying the specimen thickness [9]. This is reconfirmed again in our experimental exploration as one of the initiative motivations of this experimental study.  
However, it is still not possible to thoroughly conclude the failure or inaccuracy of using the standard method due to the insufficient repetition for constructing such a large experimental operation. Furthermore, for a non-deformable soil matrix, the initial density control determines the pore size distribution, which governs the static SWRC and AEV [63; 64]. This could be also a reason for the differences. This trial still further upgrades our understanding of SWRC regarding the spatial variation in suction and  
Figure 10: (**a**) The comparison between tensiometer measurement and theoretical water pressure; (**b**) the dynamic response of tensiometer on each point.  
moisture. There will be a higher expectation that with further repetition of the same test in the laboratory and field, with systematic control of impact factors from environmental conditions and better precision of moisture given by the inverse analysis of the spatial TDR technique, the dynamic and spatial effects of SWRC can be more comprehensively unveiled for better two-phase flow seepage simulation, unsaturated soil strength estimation and deformation prediction.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### Environmental Sustainability
(2019) noted that the involvement of multiple jurisdictions and ownership transfers make it challenging to establish and enforce environmental regulations effectively [54], and a study by [PERSON] (2016) found that national laws governing commercial space activities are often inadequate or unclear, leading to the lack of effective enforcement of environmental regulations [49; 55]. In conclusion, the environmental impacts can trigger these two principles in the sense that, if not meticulously handled, they could create inequalities among nations, especially by widening the gap between well-established space-faring nations and the up-and-coming ones.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Contingency Response  
According to NIAC, contingency response shows the resilient ability of the smart grid, which can be divided into three aspects: robustness, resourcefulness, and rapid recovery according to the periods of encountering an incident and adaptability after the incident.  
The quantitative metrics method is commonly introduced in this period, which quantifies the system resilience capability during the different time periods of an incident. A general smart grid resilience framework during an incident is illustrated in Figure 3[7; 89].  
The smart grid resiliency performance starts to degrade from time \(t_{1}\), which is the direct result of the incident. However, incidents like cyber-physical attacks happened before time \(t_{1}\), referring to the \(t_{0}\) point. After the degradation, the smart grid system starts to analyse the incident and prepare for the restoration during \(t_{2}-t_{3}\). In \(t_{3}-t_{4}\), the smart grid initially recovers the critical electric services, and the grid will perform in an acceptable state \(M_{1}\). Then, after \(t_{4}\), the infrastructures gradually start to be recovered and adapt to new patched rules.
## 4 Devices' Vulnerabilities and Requirements
### IoT Attacks
#### 4.5.7 Attacks Based on Information Damage Level  
Sensors monitor and collect information from IoT devices. Attackers can change the information easily, which is fluctuating and open. Based on modifying the level of information by attackers, it can be divided into six categories [77].  
* Interruption: An interruption attack is an attack that creates an obstruction of any kind during the communication process between one or more systems. As a result, resource exhaustion is one of the outcomes of an interruption and the systems, which are under attack by illegitimate users, become unusable which results in the system being in shutdown mode.
* Eavesdropping: When a hacker uses unprotected or insecure data transmission to steal data shared or communicated through electronic devices, the attack is known as an eavesdropping attack. A sniffing or snooping attack is another name for it. In this attack, there are no anomalies in the network communications themselves, which makes the probability of this attack higher. IoT systems' confidentiality breaches when there is an eavesdropping attack.
* Alteration: As the name suggests, in this attack the message which is sent by the sender is altered and transmitted to the destination by an unauthorized user. The integrity of the message and security requirements of the system is threatened by this type of attack. The receiver receives the exact message that an attacker is wanted to send. It will result in the poor performance of the network. To deceive the communication protocol, the adversary does it in an undisciplined manner.
* Fabrication: In this type of attack, an intimation data is inserted into the network by an unauthorized user as if it is a valid user who threatens the authentication of the IoT system. As a result, the message's confidentiality, authenticity, and integrity are compromised.
* Message Replay: A replay attack (also known as a playback attack) is a type of network assault in which genuine data is captured, and the original message is resent or delayed in order to breach the target IoT devices. The IoT recipient device will be confused by a replayed message, which could harm the IoT system. This is one of the lower-tier variations of man-in-the-middle attacks.
* Man-in-the-middle: As the term implies, an attacker stands between the transmitter and the recipient; the attacker relay and possibly alter the communication between two parties, and the attacker collects information from the communication channel. Two parties feel they are conversing directly with each other in this situation.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Routes Design Based on Radar Geographic Information
#### 2.3.3 Calculation of the Geodetic Coordinates of the UAV Route Endpoints  
If conditions permit, the relative orientation between the UAV route and the radar is preferred to be \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) to facilitate checking and verification of the route coordinates. With reference to the coordinate calculation method in geodesy, the geodetic coordinates, i.e., latitude and longitude, of the two endpoints of the UAV route at a specific elevation angle are calculated by combining the horizontal distance between the two endpoints of the UAV route and the radar. Knowing the geodesic coordinates of the radar system \((\varphi_{1},\lambda_{1})\), we can determine that its azimuth angle to the UAV is \(\theta\), and the horizontal distance between them is \(R=L\times\cos\alpha\). Using the geodesic function of the Python Geopy library, the geodesic coordinates \((\varphi_{2},\lambda_{2})\) of the route endpoints are obtained  
Figure 2: Schematic diagram of UAV and radar observation.  
by calculation. Finally, the geodetic coordinates and altitude information are entered into the UAV. The specific calculation codes are shown in the Table 2.
## 3 Experimental Validation and Analysis
### Reconstruction of Overall Deformation by Prediction Results  
Acceleration Reconstructed Displacement  
The dynamic displacement reconstruction based on the acceleration predictions from the CEEMDAN-SC-LSTM, CEEMDAN-LSTM, and LSTM models is shown in Figure 15, along with the reconstruction errors. To evaluate the reconstruction accuracy, the mean peak error (\(\overline{E}\)), correlation coefficient (R), and RMSE were used as metrics, with the statistical results presented in Table 2.  
Figure 15 provides a visual comparison of the performance of the CEEMDAN-SC-LSTM, CEEMDAN-LSTM, and LSTM models in reconstructing displacement from predicted acceleration. It is evident that the displacement curve reconstructed using the CEEMDAN-SC-LSTM model aligns most closely with the true values, almost perfectly overlapping, whereas the other two models exhibit noticeable deviations. As shown in Table 2, the correlation coefficient between the reconstructed displacement from the CEEMDAN-SC-LSTM model and the true values is as high as 99%, with an RMSE of 3 mm  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE\%**} \\ \cline{2-7}  & **Displacement/m** & **Acceleration/m/s\({}^{2}\)** & **Displacement/m** & **Acceleration/m/s\({}^{2}\)** & **Displacement** & **Acceleration** \\ \hline CEEMDAN-SC-LSTM & 0.011 & 0.002 & 0.007 & 0.002 & 0.346 & 0.516 \\ CEEMDAN-LSTM & 0.030 & 0.068 & 0.026 & 0.046 & 7.220 & 4.577 \\ LSTM & 0.028 & 0.070 & 0.023 & 0.045 & 6.879 & 5.550 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compares the predictive performance of the three models.  
Figure 15: (a) Reconstructed Displacement from Predicted Acceleration; (b) Reconstruction Errors.  
and an average peak error of less than 0.01. These experimental results indicate that the CEEMDAN-SC-LSTM model achieves high accuracy in displacement reconstruction from predicted acceleration data.
# Fault-Tolerant Visual Secret Sharing Schemes without Pixel Expansion
## 5 Conclusions  
This paper presents the design of a (2, 2)-threshold visual secret sharing scheme that is fault-tolerant without pixel expansion; the original information can be identified even when we stack the two resulting shares imperfectly. As we know, this paper is the first one to discuss the VSS scheme that can solve the misalignment problem without pixel expansion. That will make the implementation more likely to be realized. For \(n=3\), the swapping of black and white can be expected in the analysis when we stack the shares with one pixel shift (as shown in Table 9). As for recognizing the graph, the original information still can be identified in this situation. On the other hand, for the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes Capability** & **[PERSON] and [PERSON] 2004 [14]** & **[PERSON] _et al._, 2009 [15]** & **Our Scheme** \\ \hline Fault-tolerant & Yes & Yes & Yes \\ Without pixel expansion & No & No & Yes \\ By random grid & No & No & Yes \\ Flexible & No & Yes & Yes \\ (\(k,n\))-threshold VSS scheme & No & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of our scheme with the previous works.  
Figure 9: Comparison of our schemes with MTVSS scheme for shifting the same percentage of the shares.  
purpose of restoring the original image, there will be some space for improvement. In addition, we also expect further study for designing the encryption scheme when \(n\) is seven or more. However, the incurred distortion of the graph will be more severe. This challenge will be further studied. Besides, we shall also work on designing a (\(k\), \(n\))-threshold VSS scheme that addresses the misalignment problem without pixel expansion and improving the existing algorithms with a larger transmittance gap between the black and white areas of the resulting stacked image, so that the restored image could be recognized more easily.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research under Contract No. MOST 104-2918-I-260-004. All authors discussed the main idea and scientific contribution. [PERSON] performed the experiments and wrote the first draft. [PERSON] contributed in manuscript writing and revisions. [PERSON] proposed the idea, was supervising computational experiments and analyzed the results.  
The authors declare no conflict of interest.
# Emotion Estimation Using Noncontact Environmental Sensing with Machine and Deep Learning Models +
## 3 Materials and Methods
### Evaluation Method  
To evaluate the proposed model, we used a 5-fold cross-validation method for each subject, ensuring reliability and unbiased assessment of the model's performance. In this method, the dataset for each subject is divided into five subsets, with each subset sequentially used as a validation set while the remaining subsets serve as the training set. This process was repeated five times. By averaging the evaluation results from each run, we obtained an overall evaluation score for each subject. These scores were further averaged across all subjects to derive a comprehensive evaluation score for the model.  
To measure the model's accuracy, we used the coefficient of determination (R\({}^{2}\) score) as a performance metric. The R\({}^{2}\) score indicates the proportion of variance in the dependent variable that can be predicted from the independent variables, serving as a standard metric  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline num\_boost\_round & 1000 \\ early\_stopping\_round & 10 \\ \hline \end{tabular}
\end{table}
Table 4: Parameters for early stopping.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Filter** & **(kernel Size, Activation)** & **Output** \\ \hline Input & & & (None, TS, 14, 1) \\ Conv\_1 & 16 & ((1, 4), relu) & (None, TS, 14, 16) \\ Conv\_2 & 32 & ((1, 4), relu) & (None, TS, 14, 32) \\ MaxPool\_1 & & poolsize = (1, 4) & (None, TS, 4, 32) \\ Conv\_3 & 64 & ((1, 4), relu) & (None, TS, 4, 64) \\ Conv\_4 & 128 & ((1, 4), relu) & (None, TS, 4, 128) \\ MaxPool\_2 & & poolsize = (1, 4) & (None, TS, 1, 128) \\ Conv\_5 & 256 & ((1, 4), relu) & (None, TS, 1, 256) \\ Reshape & & & (None, TS, 256) \\ LSTM & 128 & activation = relu & (None, 128) \\ Dense\_1 & 16 & activation = relu & (None, 16) \\ Dense\_2 & 1 & activation = linear & (None, 1) \\ \hline \end{tabular}
\end{table}
Table 5: The structure of the CNN-LSTM model.  
for assessing the goodness-of-fit of regression models. A high R\({}^{2}\) score suggests that the model has a good predictive capability for the data. This metric enables consistent evaluation, especially when comparing the accuracy of multiple models.  
To gain a more comprehensive understanding of the model's performance, we calculated the mean R\({}^{2}\) score, standard deviation (SD), and 95% confidence interval (CI) based on the cross-validation results, thereby assessing the model's fit. The SD indicates the variability in the model's performance across different folds, while the standard error estimates the uncertainty of the mean performance score. We selected a 5-fold cross-validation to maintain an adequate sample size in the validation data. Moreover, the use of 5-fold cross-validation is a common practice, providing a balance between the sample size and the stable model evaluation.