esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Validation of In-Flight GNSS Signal Reception during Jamming for Aeronautics Applications
## 5 Conclusions  
This article has shown a virtual validation method able to predict the signal power received on the airborne side during flight. The method has been tested and validated with measurement data from a flight campaign, showing very good agreement between the simulated and measurement data, much better than the one obtainable when considering simplified antenna characteristics. By incorporating actual antenna measurement and installed performance analysis into the system flight scenarios, we can gain valuable insights into the performance to be expected in flight, even before flying. This approach allows for a more realistic representation of the conditions and parameters involved, enabling better insights and predictions for the test outcomes and saving a lot of time and money needed for multiple actual flight trials. This showcases the tool's significant capability as a predictive tool, enabling the planning of experiments or even virtually simulating various antenna+aircraft configurations while operating in specific interference scenarios to obtain results that closely approximate actual flight conditions.  
Figure 16: Simulation and Measurement results for the GNSS reception with Ant.4 (affected duration).  
Figure 17: Simulation results for the GNSS reception with GPS patch antenna and Isotropic antenna (Solid lines–Measurement: Ant.4, Dashed lines–Simulation: Ant.4, DashDotDash lines–Simulation: GPS Patch, Dotted lines–Simulation: Isotropic).  
Figure 18: Approximate cumulative integral results for the deviation between the simulated GNSS reception and the measurements (Solid lines: Ant.4, Dashed lines: GPS Patch, DashDotDash: Isotropic).  
**Author Contributions:** Conceptualization, V.T. and S.C.; methodology, V.T. and S.C.; software, V.T.; validation, V.T. and S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** We extend our gratitude to [PERSON] for his valuable assistance with flight data, to [PERSON] for his contributions to antenna measurement, to [PERSON] for his efforts in planning and executing the flight campaign, and Ansys/STK for their support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### GNSS Equipment, Installation and Raw Data Processing  
Five single frequency u-blox NEO-M8T GNSS [37] receivers coupled with Tallysyman TW3740 antennas were installed and distributed, as shown in Figure 1. With such receivers, millimetric accuracies have been proved to be achievable [25], in line with the requirements for dam monitoring. One (PT0) was located close, but outside the dam body and used as a reference point, three (PT1-3) were installed on the dam crest and one (PT4) on the downstream shell. PT0 was powered by a battery tied to a solar panel, while PT1-4 were connected to the electric network. Antennas mounted on the dam crest were connected to their receivers through coaxial cables with a maximum length of 50 m to reach the unique unit gathering all the receivers. In addition, PT0 was controlled by relative positioning with respect to the closest available CORS belonging to a national reference network.  
For the installation of PT stations, steel pillars supporting the antennas were monumented on granite blocks integrated with the crest's concrete walls or concrete plinths realized for the purpose. With such monumentations, the stability of the installed antennas was ensured, as well as sky visibility in the case of persistent snowfall (see Figure 2).  
Daily position solutions were computed for each of the four PT1-4 stations through double-difference relative positioning with respect to the reference station PT0. Bernese 5.2 software was used to process the 5 s sampling observed sessions, and phase ambiguity resolution was performed by applying the Sigma method [38]. Since the baseline lengths are in the order of a few tens of meters and there is almost no height difference amongst the points, atmospheric effects such as tropospheric and ionospheric delays can be neglected by using the double difference approach [25]. The reference PT0 coordinates were previously determined on the basis of a static positioning session (one week long) performed by assuming the closest CORS as the reference station. ETRF2000-RDN [39] PT station coordinates were determined, but for the purpose of dam monitoring, a local reference system was defined. This local reference system was centered at PT0 with X-Y axes referring to the horizontal coordinates rotated to be aligned with the longitudinal and transversal directions of the dam. Time series of the 3D coordinates of the PT monitoring stations were computed and made suitable for further processing and analyses aiming at determining the detectable time-dependent displacements of the dam. The analyzed time series refer to the period Jul/2019-Nov/2020.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 2 Passive Constant Flow Regulators--General Considerations
### Working Principle
This method is exploited in constant flow rate implantable pumps for pain management (e.g., Infusaid-400, Medtronic Isomed, Codman 3000, Tricumed IP2000), wherein the reservoir pressure is generated by the vapor pressure of propellant (typically 2.5 bar), or in elastomeric pumps dedicated to ambulatory infusion of medication [6]. In both cases, a fluidic restriction (capillary) is used to set the flow rate in production, but the strategy to maintain the flow rate during the reservoir emptying is different. The propellant vaporizes progressively as the drug is infused toward the patient, while in the second type of device, the elastomeric balloon is designed  
Figure 2: Ideal flow rate versus pressure gradient characteristic of a three-stage passive constant flow regulator. At low pressure (stage I), the regulator has a constant and low hydraulic resistance. In stage II, the flow is regulated at \(Q_{0}\) for a pressure gradient in the range \(\big{[}\Delta P_{\text{reg min}};\ \Delta P_{\text{reg max}}\big{]}\). At high pressure (stage III), the device could, according to its specific design, either stop the flow at \(\Delta P_{\text{off}}\) (solid line) or conversely, deliver a large flow rate (dashed line). to keep the pressure almost constant during the treatment. A discussion of the flow rate accuracy of constant implantable pumps is provided by [PERSON] et al. [9]. For disposable and other portable infusion pumps, including devices powered by a spring, an elastomeric balloon, a chemical reaction, or a vacuum, overall flow rate accuracy of \(\pm 40\%\) was reported [10; 11; 12]. Also, these devices are sensitive to outlet pressure variations and temperature-induced change of the viscosity. The combination of a pressurized reservoir and a passive constant flow regulator is recommended to avoid startup overflow and the constant decrease of the infusion rate over time as well. The infusion duration is better controlled and a flow rate error less than \(\pm 10\%\) can be achieved [9].
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 2 Related Work
### Load Forecasting Approaches  
The authors of [32] explained that the need for load forecasting is given by the advanced metering infrastructure (AMI), which is a main component of the smart grid. It encapsulates the main technological innovations in this domain: the smart meters. Then, cutting-edge applications such as DR models can be implemented. To be successful, DR campaigns need to profile and further forecast the energy usage reported by each smart meter within each AMI. In addition, the authors identified a need for characterizing and forecasting the usage patterns of individual appliances within a household, since it allows to manage the daily electricity usage of each appliance, to engage further in the DR using the smart meters over the AMI, and therefore select suitable price plans. The latter idea was stated in [35], in which the authors conclude that load forecasting can be useful to provide energy-efficient scheduling for smart homes.  
Different from grid-level forecasting, which measures the total energy consumption in a household, i.e., at smart meter level, appliance load forecasting has received less attention in past years [32]. However, in recent publications, researchers have taken advantage of the many benefits it can bring for smart grid applications [32; 33; 35; 36]. In these works, the authors agreed on the challenge of developing a single model that operates at the device level.
## 3 Proposed Methodology
### Heuristics Algorithm in DSS
#### 3.2.1 Solution Representation  
Figure 6 presents the solution representation used in this study corresponding to the type of crop (_i_) assigned to each zone (_z_). For each planting zone/area, there will be different solution representations. Furthermore, the initial solution is generated by assigning a permutation of a randomly generated sequence at each zone. In other words, the sequence represents the priority of the crop to be planted at each zone. This initial random solution procedure might have an infeasible assignment of crop type to a particular zone. Thus, this condition is repaired by neglecting the infeasible assignment and continuously considering the next available crop option in the associated sequence.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### The Need for Blockchain and IoT in the Coffee Supply Chain
#### 1.3.5. Standardized Multi-Party Access  
Conventional supply chain systems often operate in silos, where each party maintains separate records and data inconsistencies are common. Blockchain provides a shared and synchronized ledger that allows all authorized participants to access the same set of information in real time. Farmers, cooperatives, exporters, auditors, and retailers can each interact with the system according to their role, with custom access controls in place to protect sensitive information. This shared infrastructure minimizes reconciliation delays, increases transparency, and fosters better coordination among supply chain actors.
## 5 Discussion
### Cybersecurity Challenges in Environmental Subsystem  
The environmental subsystem includes all the organizational internal and external environment-related factors. The literature states that environmental factors include all those issues and conditions determined or controlled by the surrounding environment [114]. The factors can include climate, geographical, global, or environmental changes that strongly impact an organization. The environmental subsystem explains all the cyber-security challenges that emerged due to the ecological changes and have an impact on organizational internal or external operations in the HERS in Australia amidst major crises.  
The cybersecurity challenges in the environmental subsystem contain two primary sub-themes, as shown in the figure: (1) Internal environment-related cybersecurity challenges, and (2) External-environment-related cybersecurity challenges.  
Analyzing this theme outlines all the internal and external-environment-related cybersecurity issues that emerged amidst the major HERS crisis, causing obstacles to successfully running organizational operations.
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
## 5 Discussion and Perspectives--Towards Operational Sinkhole Early Warning
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Earth Observation Technology** & **Sinkhole Hazard Phase** & **Advantages** & **Limitations** & **Recommendations** \\ \hline \hline \multicolumn{6}{c}{_Sinkhole inventories_} \\ \hline Ground geophysics--i.e., Seismics, EET, GPR & Underground cavity detection and delineation & Detect underground cavities in addition to describing the shape, size and overall geometry of subsurface cavities. In-depth characterisation of structures and dating of sediments & Limited spatial coverage, limited to site-specific investigations: Active data frequently unavailable-historical sinkholes masked by terrain transformation & Limited spatial coverage, limited to site-specific investigations: Active data frequently unavailable-historical sinkholes masked by terrain transformation & Complement investigations by drilling, trending to confirm nature of anomalies \\ \hline Satellite and airborne remote sensing (optical and multispectral) & Sinkhole inventory population-dentifying of historical sinkhole events, extracting geomorphological parameters and estimating time of occurrence & Image analysis and elevation model generation can be useful for detecting earthquakes in the area under past sinkholes & Sinkhole geomorphological expression & High resolution is required depending on typical sinkhole dimensions in the area under investigation \\ \hline Infrared & Sinkhole inventory compilation- & between sinkholes and surrounding features; suitable for machine learning classifiers & & Extensive validation and verification remains to be performed \\ \hline LiDAR & Sinkhole inventory compilation through elevation model generation and subsequent geomorphological analysis & High resolution, high accuracy digital surface models are provided: Useful for early detection of tension cracks and collapse features & Total time required for processing and analysis Limited in spatial coverage; Limited in spatial coverage, and difficult to reproduce. Sinkhole features masked by presence of vegetation or urban structures & Manual inspection and feature extraction is intensive, error-prone and difficult to reproduce. Automated feature extraction algorithms to be investigated \\ \hline UAV data collections & Sinkhole inventory compilation through elevation model generation and subsequent geomorphological analysis & Low cost alternative to LiDAR collections & Limited spatial coverage, limited to site-specific investigations: Active data frequently unavailable-historical sinkholes masked by terrain transformation & Manual inspection and feature extraction is intensive, error-prone and difficult to reproduce. Automated feature extraction algorithms to be investigated \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The advantages and limitations of various earth observation technologies employed for the compilation of sinkhole inventories and the detection of sinkhole precursors.
## 4 Experiments and Results
### Ablation Study
#### 4.5.1 Effectiveness of the RRD Module  
In the encoder module, we employ the Residual-RepGhost-Dblock as our feature extraction block. This approach leverages the RepGhost bottleneck module to reduce feature dimensionality while retaining feature information. Additionally, we utilized convolution blocks with varying expansion coefficients to extract features with receptive fields of different scales. The results, presented in Table 3, demonstrate a performance improvement from 77.40% to 79.93% in the mIoU indicator, which indicates that incorporating the RRD module has led to a significant improvement in the network's performance.
## 2 Materials and Methodology
### Methodology
#### 2.2.2 Ensemble Learning  
We chose the eXtreme Gradient Boosting (XGBoost) algorithm and the random forest (RF) algorithm as the experimental models in this paper to determine the final model. They are all types of ensemble learning and tree-based models.  
The ensemble learning method gives them excellent performance in capturing complicated patterns within data. Moreover, the special structure of the tree-based model allows them to explain the model's decision-making clearly, thus enhancing its interpretability. As mentioned in Section 1, current data-driven car-following models based on deep learning suffer from a lack of interpretability and efficiency, which makes it difficult to apply them in real-world scenarios. The theory-driven car-following models are limited in flexibility and accuracy, and their mathematical formula is too abstract to explain the human decision-making process. Therefore, we applied XGBoost and RF models that can strike a balance between interpretability, accuracy, and latency to address this issue.  
Ensemble learning is one of many machine learning methods. The core idea is to integrate the results of all basic learners. A major question needs to be answered in ensemble methods: How to combine basic learners to yield the final model? Accordingly, two types of ensemble methods are identified: the Bagging method and the Boosting method. The RF model and the XGBoost model are representatives of them, respectively.
## 3 Establishment of Adaptive Identification Model for Flexible Air-Breathing Hypersonic Vehicle
### Online Updating for DNN  
Assume there exists weight values \(W^{*}\) and \(\beta^{*}\) such that the system (4) can be approximated by the model  
\[ \begin{array}{l}\dot{x}=Ax+BW^{*}\Phi(x)+B\beta^{*}\Psi(x)u+\omega(x,u)\\ y=x\end{array} . \tag{8}\]  
Define the error between (5) and (8) as \(e=y-\hat{y}=x-\hat{x}\). Assuming \(\omega(x,u)\) is zero, we obtain  
\[\hat{e}=Ae+B\widetilde{W}\Phi(\hat{x})+B\widetilde{\beta}\Psi(\hat{x})u, \tag{9}\]  
where \(\widetilde{W}=W^{*}-\hat{W}\) and \(\widetilde{\beta}=\beta^{*}-\hat{\beta}\) which are undated by the adaptive law (15) and (16) derived later. To obtain the stable updating laws, the Lyapunov second law is used. Considering the following Lyapunov function  
\[V=\frac{1}{2}e^{T}Pe+\sum_{i=1}^{11}\frac{1}{2\eta_{i}}\widetilde{W}_{i}\widetilde {W}_{i}^{T}+\sum_{i=1}^{11}\frac{1}{2\lambda_{i}}\widetilde{\beta}_{i}\widetilde {\beta}_{i}^{T}, \tag{10}\]  
where \(\eta_{i}\) and \(\lambda_{i}\) are positive constant which is referred to as the learning rate of the DNN, \(\widetilde{W}_{i}\) and \(\widetilde{\beta}_{i}\) is the \(i\)th row vector of matrix \(\widetilde{W}\) and \(\widetilde{\beta}\), respectively, i.e. \(\widetilde{W}= \begin{bmatrix}\widetilde{W}_{1}&\widetilde{W}_{2}&\cdots& \widetilde{W}_{11}\end{bmatrix} ^{T}\), \(\widetilde{\beta}= \begin{bmatrix}\widetilde{\beta}_{1}&\widetilde{\beta}_{2}& \cdots&\widetilde{\beta}_{11}\end{bmatrix} ^{T}\). And because \(\beta\) is a \(11\times 11\) diagonal matrix, then \(\widetilde{\beta}_{i}= \begin{bmatrix}0&\cdots&0&\widetilde{\beta}_{ii}&0& \cdots&0\end{bmatrix} ,i=1,2,\ldots,11\). The derivative of the Lyapunov function is  
\[\dot{V}=\frac{1}{2}e^{T}Pe+\frac{1}{2}e^{T}P\dot{e}+\sum_{i=1}^{11}\frac{1}{ \eta_{i}}\widetilde{W}_{i}\dot{\widetilde{W}}_{i}^{T}+\sum_{i=1}^{11}\frac{1}{ \lambda_{i}}\widetilde{\beta}_{i}\dot{\widetilde{\beta}}_{i}^{T}. \tag{11}\]  
When Substituted (9) into (11), we obtain  
\[ \begin{array}{l}\dot{V}=\frac{1}{2}e^{T}A^{T}Pe+\frac{1}{2}e^{T}PAe+\frac{1} {2}\Big{(}B\widetilde{W}\Phi(\hat{x})+B\widetilde{\beta}\Psi(\hat{x})u\Big{)}^ {T}Pe\\ +\frac{1}{2}e^{T}P\Big{(}B\widetilde{W}\Phi(\hat{x})+B\widetilde{\beta} \Psi(\hat{x})u\Big{)}+\sum\limits_{i=1}^{11}\frac{1}{\eta_{i}}\widetilde{W}_{ i}\dot{\widetilde{W}}_{i}^{T}+\sum\limits_{i=1}^{11}\frac{1}{\lambda_{i}} \widetilde{\beta}_{i}\dot{\widetilde{\beta}}_{i}^{T}\end{array} , \tag{12}\]  
where \(P>0\) satisfies the Lyapunov equation \(PA+A^{T}p=-Q\). Since \(\Phi^{T}(\hat{x})\widetilde{W}^{T}B^{T}Pe\) and \(u^{T}\Psi^{T}(\hat{x})\widetilde{\beta}^{T}B^{T}Pe\) are scalars, that is  
\[ \begin{array}{l}\Phi^{T}(\hat{x})\widetilde{W}^{T}B^{T}Pe=e^{T}PB\widetilde{ W}\Phi(\hat{x})\\ u^{T}\Psi^{T}(\hat{x})\widetilde{\beta}^{T}B^{T}Pe=e^{T}PB\widetilde{\beta} \Psi(\hat{x})u\end{array} . \tag{13}\]  
Hence, Equation (12) will be  
\[
## 2 Overview of the Power Ramp-Rate Control Methods  
A simplified scheme of an LSSPP with PRRC, similar to Coto Laurel, is shown in Figure 2. The DC voltage of each PV array is transformed to AC using three-phase voltage-source converters (VSC). The output of each converter is connected to PREPA using a central feeder transformer. Similarly, the ESS is composed of a set of battery banks. The voltage of each battery bank is transformed to AC using a VSC. The ESS has its own transformer to inject or absorb power from the AC bus. The PRRC block reads the power in the PV feeder and generates a power reference to the VSC so that the power injected into the main grid fulfills the PRR requirements.  
Figure 1: Coto Laurel Satellite View. Photo from Map Data © 2021 Google.  
The output power in the ESS feeder can be positive (battery charge) or negative (battery discharge). As seen in Figure 3, when the PRR in the PV violates the requirement, the battery absorbs or injects energy to smooth the output power to the main grid.  
Depending on the PRRC method, the battery may achieve a higher or lower DoD, and the SOC at the end of the day may vary as well. These two factors drastically affect the battery degradation and the cost associated with ESS for PRRC.  
Following, a brief description of each of the PRRC methods analyzed in this work is presented. To simplify notation, the power injected into the main grid is represented by:  
\[\text{P}_{\text{out}}{=}\text{P}_{\text{pv}}{+}\text{P}_{\text{ess}}. \tag{1}\]  
Furthermore, the PRR of the power injected by the PV array at time \(\text{t}=\text{kT}_{\text{s}}\) is represented by:  
\[\text{PRR}_{\text{pv}}[\text{k}]=\frac{\text{P}_{\text{pv}}[\text{k}]-\text{ P}_{\text{pv}}[\text{k}-1]}{\text{T}_{\text{s}}}, \tag{2}\]  
where \(\text{T}_{\text{s}}\) is the sampling period. Additionally, the maximum allowed PRR at the POI by regulation is defined as \(\text{PRR}_{\text{max}}\).  
Figure 3: Battery behavior during PRR events.  
Figure 2: Basic scheme of an LSSPP with PRRC.
## 3 Results
### Monthly Change of GPP in the Year 2021  
The Landsat-8 GPP failed to overcome the effects of clouds every month since its long revisit. Additionally, with the help of the MVC method, monthly NIRvs derived by MODIS and Sentinel-2 were generated. As a result, Figure 5 shows only the MODIS and Sentinel-2 GPPs, as well as the changes in monthly temperature and precipitation for the year 2021.  
The monthly GPP of MODIS and Sentinel-2 showed two peaks in 2021. One peak was in April, with GPPs 0.85 and 0.95 Tg C m\({}^{-1}\) for MODIS and Sentinel-2, respectively. The other peak was in July, when the GPPs of MODIS and Sentinel-2 reached their summit for the whole year: 1.43 and 1.12 Tg C m\({}^{-1}\), respectively. Between the two peaks was an evident decrement in GPP in May. GPP decreased by 16.4% and 25.9% according to the estimations of MODIS and Sentinel-2, respectively.  
In the growing season, the MODIS GPP was generally lower than Sentinel-2 GPP. For example, from July to September, the average of Sentinel-2 GPP was 1.31 Tg C m\({}^{-1}\), which was 21.3% higher than MODIS GPP in the same period. Additionally, a similar difference was also found in the growing peak in April. Moreover, in winter and early spring MODIS GPP and Sentinel-2 GPP were similar.  
As Figure 6 showed, a significant (\(p<0.001\)) linear relationship was found between the air temperature and GPP. R\({}^{2}\) for the temperature-GPP relationships were 0.81 and 0.89 for Sentinel-2 and MODIS GPP, respectively (Figure 6a,b), which was higher than the precipitation-GPP relationship (Figure 6c,d). Therefore, the change in GPP was mainly determined by the air temperature in the year 2021. Additionally, the positive coefficient of slopes for the linear relationships indicated that temperature and precipitation had positive effects on GPP. However, the GPP declined when temperature and precipitation increased in May. This decrement in GPP implies that, besides climatic factors, some other factors determined the change in GPP in May. In urban areas, the impact of anthropogenic activities on vegetation cannot be ignored.  
Figure 5: The monthly change of Suzhou’s GPP in the year 2021.