esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2014**, _14_, 10258-10272; doi:10.3390/s140610258  
_Sensors_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Augmented GNSS Differential Corrections Minimum Mean**  
**Square Error Estimation Sensitivity to Spatial Correlation**  
**Modeling Errors**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Department of Electronics and Telecommunications, Politecnico di Torino,  
Corso Duca degli Abruzzi 24, 10129 Turin, Italy; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Ansaldo STS, S.p.A, [PERSON] 3-5, 16151 Genoa, Italy;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-011-227-6432; Fax: +39-011-227-6299.  
_Received: 17 April 2014; in revised form: 4 June 2014 / Accepted: 5 June 2014 / Published: 11 June 2014_  
**Abstract:** Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold.  
GNSS; augmentation system; reference station; differential correction; linear MMSE; correlation distance
## 5 Discussion
### Inequity in Data Access and Integration Frameworks  
Several main factors, such as technological and methodological inconsistency, cause difficulty in completely integrating datasets. Each data source functions with different resolutions, time intervals, and formats, which presents considerable challenges in harmonizing these datasets. [PERSON] and [PERSON] 2021 [64] considered that achieving alignment between SAR and optical data is notably difficult without comprehensive preprocessing, which may lead to errors and delays. Many studies, including [PERSON] et al. (2021) [20] and [PERSON] et al. (2023) [5], emphasize that sophisticated integration methods demand significant computational power and specialized knowledge in the field. This restricts their usage in areas where these resources are limited. This problem is further aggravated by the inequality of data availability among regions; for most developing countries, high-resolution data or advanced processing tools are not readily available, making adopting integrated monitoring systems more complex. This has been pointed out by [PERSON] et al. (2021) [3] and [PERSON] et al. (2021) [32]. For instance, studies like that by [PERSON] et al. (2021) [34] stress that the absence of integrated data protocols is one of the main reasons for significant inefficiencies in collaboration among researchers toward producing reproducible results. This rapid development of new methods for data collection, like real-time UAV imagery and AI-driven predictive models, has outgrown the integration frameworks; hence, it has created a gap between technological potential and practical application. Despite these technological advancements, full-platform integration remains elusive due to systemic and technical barriers. The prime reason is that integrating datasets of varied resolutions, temporal frequencies, and sensor modalities is inherently complex.  
As [PERSON] (2021) [36] mentioned, even for advanced thresholding techniques that allow the detection of shorelines, consistent results could not be provided when applied across diverse data sources.  
Moreover, regional inequalities in data availability and computational resources create a wide gap in the application of integrated systems. For example, high-income countries can apply AI and advanced geospatial tools, while resource-poor regions struggle to access even the fundamental datasets required for monitoring. Another critical factor has been the lack of collaborative frameworks or open-access platforms that would support global sharing and the integration of data. Studies such as that by [PERSON] et al. (2022) [21] have shown that without shared repositories and standardized workflows, dataset integration across scales and disciplines remains fragmented and resource-intensive.  
These gaps indicate the urgent need for unified global initiatives to make integrated coastal monitoring a reality.
## 6 Discussion
### Design Recommendations for Fabric-Based Interface for HAR Interactions
#### 6.1.6 Design Fabric-Based Interface That Foster Socially Acceptable Gestures  
The aim of our study is to design an unobtrusive fabric-based interface for HAR applications so that end-users are willing to use this interface in public settings. Usability is defined by [PERSON] as \"quality in use\" [57]. However, a user's readiness to use ubiquitous devices, particularly wearable devices, is not only limited to the quality of the device. In addition to the default usability aspect, the decision to use a wearable device, particularly a fabric-based wearable device, is based on numerous factors, mainly (1) wearable comfort and (2) social acceptability. [PERSON] et al. [58] reported that factors both internal and external to the wearers significantly influence how comfortable they feel about wearing and using the device; as such, they influence their intention to use it. Similarly, [PERSON] and [PERSON] [59] reported that social acceptability is also influenced by various factors, such as the device's appearance, social status, and cultural conventions associated with it. Our proposed design allows both the already familiar touch and mid-air gestural inputs in a subtle unobtrusive posture, and results from the users' subjective feedback show that the gestures in the user-preferred set are more suitable for HAR interactions in dynamic outdoor environments in terms of social acceptance and how comfortable they are to use. Furthermore, fabric-based wearable interfaces are useful for the places that are extremely cold (e.g., Siberia) where touch interactions on HAR devices require users to remove their gloves.
### Extraction, Smelting, and Manufacturing Process  
The most common smelting Ni process is pyrometallurgical. This process is used to smelt a high-quality nickel ore consisting of saprorite. The process is as follows:  
* Drying: Remove the moisture from the saprorite.
* Calcine/Reduction: Partially restore the nickel oxide through mixing with the restoring agent.
* Smelting: Produce Ni and Fe through this restoration process.
* Refining: Perform a final refining process to remove sulfur, phosphorus, carbon, etc. The FeNi is transformed into an ingot or a granule as FeNi or matte [2].  
The Ni extracted in this process comes from the oxidation of either the limonite or saprorite ore. Limonite contains 0.8-1.5% Ni, 40-50% Iron (Fe), and approximately 50-60% other gangue components. Alternatively, saprorite has 1.8-3.0% Ni, 10-25% Fe, and a mixed layer in which limonite and saprorite coexist. Processing ores with a high nickel content is more economically viable. However, even when using the same limonite and saprorite ore, Ni and Fe content can vary widely impacting the manufacturing cost accordingly [3]. The feedstock's selling price is generally priced at the net Ni weight. Fewer impurities, such as carbon, sulfur, and phosphorus, also increase the pricing. The smelting of Ni typically falls into two categories, high-power and low-power Ni ratio, which results in a high-grade and low-grade manufacturing. It is generally considered to be profitable for the high-power ratio if the Ni content of the ore is 2.1% or more and 1.7% Ni content for the low-power ratio. Although high-grade Ni ores ensure sustainable profits, smelters are often unable to acquire them due to worldwide scarcity [4].  
The Ni and Fe content also impact the manufacturing costs. A decrease in Ni content can lead to low productivity and low quality of Ni in the final product, stainless steel. This results in a profit decrease as consumers desire high-quality Ni to minimize their risk of product failure. It also impacts productivity, as stainless steel manufacturers experience a decrease of about 6000 tons/year when the Ni content in the ore decreases from 2.0% to 1.5%. Alternatively, an increase of Fe in the ores leads to a higher manufacturing cost. One study found that an increase in Fe content of 10% reduced the manufacturers profit approximately 2% [2].
# Vibration Compensation of the Frequency-Scanning-Interferometry-Based Absolute Ranging System
## 3 Experiments and Results
### Vibration with Given Parameters
Subsequently, the target was subjected to a sinusoidal vibration with different amplitudes (vibration amplitude was 10, 20, 30, 40, and 50 \(\upmu\)m, respectively, and the vibration frequency was 1 Hz). In other words, the average vibration velocity changed. The measurement was repeated 20 times before each change of the vibration amplitude. The distance value after the vibration compensation was compared with the corresponding average value of the 20 measurements to obtain the ranging error. As shown in Figure 5, the standard deviation gradually increased as the vibration velocity increased. At the average vibration velocity of 200 \(\upmu\)m/s, the standard deviation obtained by the proposed method was 39 \(\upmu\)m.  
Figure 2: The experimental setup. Finally, to obtain the applicable frequency range of vibration compensation, the target was sinusoidally vibrated at different vibration frequencies (vibration frequency was 1, 2, 4, 6, 8, and 10 Hz, respectively, and the vibration amplitude was 10 \(\upmu\)m). The same method was used to obtain the ranging error, and 20 measurements were carried out at each vibration frequency. The results are shown in Figure 6, where it can be seen that vibration compensation had a significant effect when the vibration frequency was lower than 6 Hz. Nevertheless, at the vibration frequency higher than 6 Hz, both the measurement error and the standard deviation began to increase rapidly. Figure 4: Distance error before and after the vibration compensation at the frequency of 1 Hz and the amplitude of 10 \(\upmu\)m.  
Figure 3: Distance spectrum before and after the vibration compensation at the frequency of 1 Hz and the amplitude of 10 \(\upmu\)m.
## 3 Results  
A total of 60 dental students responded to the questionnaire, with 14 from School A (Qatar) and 46 from School B (Turkey). The quantitative data related to ten closed-ended items with a five-point Likert scale were analyzed first followed by responses to open-ended items.  
The responses to closed ended items on the questionnaire were assigned a numerical code as follows:  
* Strongly agree: 2
* Agree: 1
* Unsure: 0
* Disagree: \(-1\)
* Strongly Disagree: \(-2\)  
A percentage count of average scores to each item are summarized in Table 1 including responses by school as well as combined responses.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Item** & **Institution** & \
\begin{tabular}{c} **Strongly** \\ **Agree** \\ \end{tabular} & **Agree** & **Unsure** & **Disagree** &
\begin{tabular}{c} **Strongly** \\ **Disagree** \\ \end{tabular} \\ \hline \multirow{3}{*}{1.} & \multirow{3}{*}{The case presentation on VR allowed me to clearly comprehend the tasks expected from me} & A & 42.86\% & 50.00\% & 7.14\% & 0.00\% & 0.00\% \\ \cline{2-7}  & & B & 45.65\% & 41.30\% & 10.87\% & 0.00\% & 2.17\% \\ \cline{2-7}  & & Combined & 45.00\% & 43.33\% & 10.00\% & 0.00\% & 1.67\% \\ \hline \multirow{3}{*}{2.} & \multirow{3}{*}{The hardness and texture of models in the VR felt similar to natural teeth} & A & 21.43\% & 42.86\% & 21.43\% & 14.29\% & 0.00\% \\ \cline{2-7}  & & B & 28.26\% & 47.83\% & 17.39\% & 14.29\% & 0.00\% \\ \cline{2-7}  & & Combined & 26.67\% & 46.67\% & 18.33\% & 6.67\% & 1.67\% \\ \hline \multirow{3}{*}{3.} & \multirow{3}{*}{The hardness and texture of models in the VR felt similar to artificial teeth used for endodontic training} & A & 7.14\% & 21.43\% & 7.14\% & 42.86\% & 21.43\% \\ \cline{2-7}  & & B & 15.22\% & 45.65\% & 21.74\% & 10.87\% & 6.52\% \\ \cline{2-7}  & & Combined & 13.33\% & 40.00\% & 18.33\% & 18.33\% & 10.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants’ responses on pre-clinical training in endodontics.  
The results were positive overall, as depicted in Table 2. The lowest score was observed for Question 8 'VR can replace the preclinical endodontics training on mannequin models'.
## Appendix A
### Flowchart for Systematic Literature Review
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N\({}^{\circ}\)** & **ID** & **Ref.** & **Title** & **Author** & **Year** & **Findings** \\ \hline
23 & S-033 & [61] & Distribution loss allocation in peer-to-peer energy trading in a network of microgrids & [PERSON] & 2020 & Design of a distribution loss allocation method tailored for P2P energy markets to ensure fair cost distribution. \\ \hline
24 & S-045 & [2] & Multi-Agent Based Optimal Scheduling and Trading for Multi-Microgrids Integrated with Urban Transportation Networks & [PERSON] et al. & 2021 & Formulation of a multi-agent-based scheduling and trading model for optimal energy management in community microgrids. \\ \hline
25 & S-046 & [51] & Mean-Field Multi-Agent Reinforcement Learning for Peer-to-Peer Multi-Energy Trading & [PERSON] et al. & 2023 & Implementation of a mean-field multi-agent reinforcement learning model for scalable optimization in P2P electricity trading. \\ \hline
26 & S-048 & [25] & A distributed robust ADMM-based model for the energy management in local energy communities & [PERSON] & 2023 & Design of a robust distributed optimization model using ADMM for dynamic P2P market clearing under uncertainty. \\ \hline
27 & S-051 & [96] & Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market & [PERSON] et al. & 2021 & Application of multi-agent reinforcement learning to automate P2P electricity trading and improve decision-making efficiency. \\ \hline
28 & S-052 & [85] & Coordinated management of aggregated electric vehicles and thermostatically controlled loads in hierarchical energy systems & [PERSON] et al. & 2021 & Proposal of a coordinated charging/discharging scheme for aggregated EV fleets participating in P2P energy trading. \\ \hline
29 & S-058 & [72] & Towards the detailed modeling of deregulated electricity markets comprising Smart prosumers and peer to peer energy trading & [PERSON] & 2023 & Development of a modeling framework for analyzing deregulated electricity markets including P2P trading dynamics. \\ \hline
30 & S-059 & [97] & Data-Driven Distributionally Robust Co-Optimization of P2P Energy Trading and Network Operation for Interconnected Microgrids & [PERSON] et al. & 2021 & Introduction of a data-driven co-optimization model using distributionally robust techniques for integrated P2P trading and reserve allocation. \\ \hline
31 & S-064 & [47] & Multi-agent energy management of smart islands using primal-dual method of multipliers & [PERSON] & 2020 & Development of a multi-agent based energy management framework for smart islands promoting sustainability and resiliency. \\ \hline
32 & S-071 & [98] & Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility & [PERSON] & 2022 & Proposal of a scalable multi-agent reinforcement learning strategy to manage distributed energy resources in smart grids. \\ \hline \hline \end{tabular}
\end{table} 
Table **A1**: _Cont._
## 4 Results
### Selected Features  
Figure 12 shows the first 70 features selected by the proposed FS approach for datasets A to E. Features are visualised with different colors and marks depending on the feature category they belong. The following conclusions could be drawn from the analysis of Figure 12: (i) Symptoms and medical imaging outcomes seem to be the most informative feature categories in dataset D in which the overall best performance was achieved. Specifically, eleven medical history outcomes and ten symptoms were selected in the first 55 features that gave the optimum prediction accuracy; (ii) nutrition and medical history characteristics were also proved to be contributing risk factors since approximately 20 out of the first selected 55 features were from these two feature categories (in dataset D). The full list of selected features for dataset D is provided in the appendix; (iii) similar results with respect to the selected features were extracted from the analyses in datasets A and E (in Figure 12a,**e**) that gave comparative prediction results (close to 72%); (iv) a different order in the selected features was observed in datasets B and C (as depicted in Figure 12b,**c**). The low accuracies recorded in these datasets (less than 67%) verify that the contained in these datasets features are less informative; (v) overall, it was concluded that a combination of heterogeneous features coming from almost all feature categories is needed to predict KL progression highlighting the necessity of adopting a multi-parametric approach that could handle the complexity of the available data.
## Abbreviations  
\begin{tabular}{l l} GIS & A Geospatial Information System is a system designed to collect, manage, analyze, store and produce different types of spatial data. \\ CO & Carbon Monoxide is a toxic gas and it has no has no color, taste, or smell, resulting from the incomplete combustion of fuel. \\ RMSE & The algorithm of the root mean square is used to calculate the differences between values \\  & estimated by a model and the observed values. \\ VISSIM & Software designed for traffic flow simulation at a micro-scale level, which is designed by \\  & Planning Transport Verkehr (PTV), Germany. \\  & The evolutionary polynomial regression, EPR, is one of the data-mining algorithms developed \\ EPR & based on evolutionary computing and the integration of numerical regression and \\  & genetic algorithm. \\ CFS & A correlation-based feature selection algorithm, which is a type of filter algorithm that selects \\  & features based on a heuristic (correlation-based) function. \\ LiDAR & Light Detection and Ranging is an advanced surveying technology usually used to create 3D \\ ENVI & Environment for Visualizing Images: professional software used for image analysis and \\  & remote sensing applications. \\  & A multilayer perceptron (MLP) is a class of feedforward artificial neural networks. An MLP \\ MLP & consists of, at least, three layers of nodes: an input layer, a hidden layer and an output layer. \\  & Except for the input nodes, each node is a neuron that uses a nonlinear activation function. \\  & Land Use and Land Cover are data files that describe the land surfaces such as water, \\  & vegetation and cultural features. \\ CFS-MLP & The proposed model that is the combination of two models, the correlation based feature selection algorithm and multilayer perceptron Neural Network algorithm. \\ \end{tabular}  
* [1]California Line Source Dispersion is one of the dispersion models used to estimate carbon monoxide emissions near roads based on various parameters related to geographic locations. Mean Absolute Error, MAE, measures the average magnitude of the errors in a set of predictions, without considering their direction. It is the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight. Relative Absolute Error is defined as the absolute error relative to the size of the measurement, and it depends on both the absolute error and the measured value. The relative error is large when the measured value is small, or when the absolute error is large. An Artificial Neural Network is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes--or learns, in a sense--based on that input and output.
## 3 Novel Taxonomy for SSNs
### SSNs Using Hybrid Level Virtualization
#### 3.3.3 Hybrid Virtualization Based on Virtual Machine/Dynamic Grouping Solution  
This branch handles the node level using virtual machine, while the high level is handled by dynamic grouping.  
* Melete system [60]: As described above, this system was one of the first trails towards SSNs, where for the node level virtualization a simultaneous execution of hosted applications can be achieved, nodes can serve up to five application concurrently, and each one stores its own code in a dedicated execution space without sharing any variables with each other to ensure that the failure of any one of them will not affect the others. While the network level virtualization is achieved by dynamic grouping of nodes to serve a certain application, where the nodes by default join a global logical group, called the associated group, and then join a logical group dedicated to serve a certain application, the nodes can also be members of multiple logical groups up to five groups.
## 3 Research Methodology
### Data Processing Procedure  
Figure 3 depicts the processing flow of the SBAS-InSAR method, which includes four main steps: data preprocessing, baseline estimation, differential interferometry, and phase unwrapping. The prioritization of interferometric pairs ensures adherence to spatiotemporal baseline conditions. This study used a spatial baseline threshold of 2% and a temporal baseline threshold of 40 days as standards. Interferometric processing was carried out on images obtained from three orbits, resulting in 126, 118, and 134 interferometric pairs. Coherence images with high quality were selected and, in conjunction with SRTM4 DEM and ground control points (GCPs), two inversion procedures were performed to estimate deformation rates and residual deformations. To geographically reference the study area's SRTM4 DEM data, annual average subsidence rate maps for the Ming City Wall area in Shanxi Province were obtained for the period from 2017 to 2022. Since the three deformation fields shared the same GCPs, seamless mosaic and analysis were conducted using ENVI, resulting in a comprehensive deformation field. In order to reduce computational load and suppress noise, a multi-view approach of two views in range and ten views in azimuth was applied to the interferograms. Furthermore, Goldstein filtering was employed to further eliminate noise from the multi-view differential interferograms, and the minimum cost flow method was used for phase unwrapping.  
Figure 3: Flowchart of SBAS-InSAR technique.