esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methods
### Evaluation of Susceptibility to Geological Disasters  
Based on the geological environment and the prevalence of geological disasters in the research area, coupled with insights from previous experiences and related literature [30; 31; 32], from the aspects of topography, geological conditions, and ecological environment, nine factors are selected for the evaluation of geological disaster susceptibility: five factors for topography, including slope, degree of relief, elevation, aspect, and landform; three factors for geological conditions, including lithology, distance from faults, and distance from rivers; and the Normalized Difference Vegetation Index (NDVI) for the ecological environment (Figure 3). To comprehensively analyze multiple influencing factors and ascertain their relative importance, the information volume method is integrated with the analytic hierarchy process (AHP). Using ArcGIS, each factor is classified, and data such as the number of disaster points, grid numbers, and other relevant metrics for each level of each factor are collected. The information volume for each level of each factor is then calculated. The 1 to 9 scaling method is applied to rank each factor, facilitating the calculation of the weight for each evaluation index (Table 3). Finally, the weighted information volume for each evaluation index at different levels is derived through weighted processing. The specific calculation formula is as follows:  
\[J=\sum_{j=1}^{n}W_{j}J_{j}=\sum_{j=1}^{n}W_{j}\ln\frac{S_{j}/A_{j}}{S/A} \tag{5}\]  
Figure 3: Geological hazard susceptibility evaluation factor map: (**a**) Lithology; (**b**) Slope; (**c**) Degree of relief; (**d**) Elevation; (**e**) Aspect; (**f**) Landforms; (**g**) NDVI; (**h**) Distance from fault; (**i**) Distance from river.  
In the formula, \(J\) represents the total amount of information provided by various factors in the study area for geological disasters. \(W_{j}\) denotes the weight of the \(j\)-th factor, while \(J_{j}\) indicates the amount of information provided by the \(j\)-th factor for geological disasters. A refers to the total area of the study area, and \(A_{j}\) represents the area occupied by the \(j\)-th factor. \(S\) is the total number of geological disasters that have occurred in the study area, and \(S_{j}\) specifies the number of geological disasters that have occurred within the area of the \(j\)-th factor.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules  
### Sensor Q4 QC Inputs  
A fuzzy rules-based system was implemented to assess the data quality at the sensor level. The system includes provisions for both Type A (rate of change of output values, cumulative rate of change of output values and node differences) and Type B (time since last calibration and time since last maintenance) uncertainty parameters. The rules' inputs are as follows:
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 6 Conclusions  
In this work, we estimated the bounds of HAP coverage extension given the operational parameters. Theoretical models for estimating the area and spectral efficiency of an extended HAP coverage area were also derived. By evaluating the average capacity, spectral efficiency, and average area spectral efficiency for cells pointing at increasing distances from the models, we showed that coverage over an extended service area of 60 km radius is achievable. The results show all three variables decrease with increasing sub-platform point distance and that the average area spectral efficiency reduces significantly at extended areas due to the increase in cell size and path loss. Additionally, we enhanced and validated our proposed cell-pointing algorithm to deliver contiguous cellular coverage over an extended service area from a HAP and derived expressions for evaluating the average and area spectral efficiencies for cells pointing at any given location. Simulated annealing verification showed that the algorithm delivered close to optimal cell pointing. The aim was to understand the practicality of an extended HAP coverage. We studied the performance of the algorithm over the estimated 60 km radius service area, using a uniform planar antenna array, which is considerably larger than the area of 30 km radius that much of the HAP-related literature thus far focuses on. It is shown that users in a system using the enhanced and validated scheme achieve CINR values between 5 and 15 dB better than the other schemes with better control of beam overlap. More than 90% of users in our scheme achieve CINR greater than zero as against 45-70% in the compared schemes, which highlights its significant performance improvement.  
Conceptualization, S.C.A., D.G. and P.D.M.; methodology, S.C.A., D.G. and P.D.M.; validation, D.G. and P.D.M.; formal analysis, S.C.A.; investigation, S.C.A.; resources, S.C.A.; data curation, S.C.A.; writing--original draft preparation, S.C.A.; writing--review and editing, D.G. and P.D.M.; visualization, S.C.A.; supervision, D.G. and P.D.M.; project administration, D.G.; funding acquisition, D.G. All authors have read and agreed to the published version of the manuscript.  
This research was carried out by University of York and was partially funded by Orange under research agreement No: H09121.  
The authors would like to thank [PERSON] for providing the results of some of the comparative schemes in Section 5.  
The authors declare no conflict of interest.  
Figure 20: Average CINR vs. capacity per user of the different schemes.
## 3 GNSS-Free IoT Localization Methods  
End-node devices are the core of IoT systems. End nodes provide the required information for the proper operation of the IoT system. For effective use of provided data, end-node location determination is essential. On the other hand, to provide appropriate location-based service for end nodes, the LPWAN network server should be aware of end nodes' location. According to the explanation, IoT localization is a key requirement for system performance. In this section, localization methods are expressed in two general categories: (1) Signal-Based localization methods and (2) Learning-Based localization methods.  
In Signal-Based localization methods, Time-Based localization methods provide more accuracy, but they lead to extra cost. Learning-Based localization methods provide more reliable estimation in the presence of environment error sources. The best localization method should be adopted to make a good trade-off between achieved performance, time, and cost [39]. A large category of studies on outdoor location estimation works on accuracy improvement regardless of system category. In the following section, we describe some of these proposed methods.
## 4 Materials and Methods
### Validation of DEGs by RT-qPCR  
Specific primers were designed from the _M. graminicola_ cloned sequences using the Beacon Designer 7.0 software (Premier Biosoft International, Palo Alto, CA, USA) (Table S6). RT-qPCR assays were conducted on RNAs extracted from pre-parasitic J2 and from susceptible or resistant rice plants after a 2-, 4-, or 8-day challenge with _M. graminicola_. We used the duplicate RNAs samples from the sequenced libraries and produced a third replicate in this study. The qPCR assays were performed on cDNA samples (diluted 1: 100) as described previously [10]. Amplifications were carried out in an Mx30005P thermal cycler (Stratagene, USA) using the Takyon Kit for SYBR assays (European, France). The SATQPCR tool ([[http://satqprc.sophia.inra.fr/cgi/home.cgi](http://satqprc.sophia.inra.fr/cgi/home.cgi)]([http://satqprc.sophia.inra.fr/cgi/home.cgi](http://satqprc.sophia.inra.fr/cgi/home.cgi))) was used to calculate the relative expression between the samples R and S at each time-point using the \(2^{-\Delta\Delta\text{Ct}}\) method. The _M. graminicola_ actin gene and _Mg17272_ were chosen by the SATQPCR tool as reference genes to normalize data. Statistical analysis of RT-qPCR data was performed to test whether the means were different (Student t-test; statistical significance \(p<0.05\)).
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 2 Background
### WAMS Components
Several protocols that consider the necessity of real-time monitoring in smart grid development have been codified in the SGAM, which has served as a framework for assessing smart grid technology development across five interoperability layers, domains, and zones, as shown in Figure 4 (more details in Section 2.2). In addition, another application is real-time system monitoring (RTSM) for stability assessment and state measurement. Phasor measurements at nodes help the system operators gain a dynamic view of the power system and initiate the necessary measures at the proper  
Figure 3: Centralized wide-area measurement system (WAMS) architecture [46]. time [21]. This is performed following the latest IEEE standard (C37.118-2005), developed to standardize the data transmission format and sampling rates of PMUs [21; 47; 48]. The stability assessment algorithms, designed to take advantage of the phasor measurement information, can significantly support this [47]. In the past, post-event analysis was an application of synchrophasors (PMUs) without wide-area communication, where data were archived locally [21; 24]. However, it was not a useful online (dynamic) control tool. Recently, the real-time control of WAMS has become a robust control and analysis tool that provides a new view of power systems [47; 49]. This is achieved by improving the communication network capabilities while maintaining PMUs as central components [13; 21; 24; 25; 37; 47]. Using PMUs for real-time control increases accuracy since the data are measured online [21; 42]. Also, it will enhance the power system stability and delivery automation capabilities after the challenges of new data communication requirements across the system are resolved. The depth of observability is another advantage for PMUs. It means measuring the bus voltage phasor directly or calculating it using the nearest connected bus's PMU voltage and line current [47; 48]. This is the cost-effective path since it reduces the number of data acquisition instruments and tools needed across the network, as the measuring line currents can extend the voltage measurements to buses with no PMUs [18; 37; 41; 43].
## 3 Results and Discussion
### Comparison of Permeability Coefficient  
As can be seen from Figures 14 and 15, after curing by microorganism, the permeability coefficient of Silt A with pore ratio of 0.75 and fine particle content of 75% is 7.08 \(\times\) 10\({}^{-8}\) cm/s, while that of the same type of untreated silt is 1.18 \(\times\) 10\({}^{-4}\) cm/s, which decreases by 4 orders of magnitude. This indicates that calcium carbonate precipitation induced by microorganisms technology can significantly improve the permeability coefficient of silt, making it nearly impervious to water. It can be seen from the figure that, when the injection amount of single round cementing fluid is 150 mL, the permeability coefficient of silt has decreased significantly compared with that of untreated silt, and when the injection amount of cementing fluid continues to increase, the permeability coefficient can still decrease by 1-2 orders of magnitude.  
Figure 14: Influence of different pore ratio on permeability coefficient.  
Figure 13: Relationship between the content of calcium carbonate and unconfined compressive strength.  
As can be seen from Figure 14, when the porosity ratio was 0.75 and the cement injection amount was 150 mL, the permeability coefficient of silt with different fine particle contents decreased by 2 orders of magnitude compared with that of untreated silt. When the pore ratio is 0.9, the permeability coefficient of silt with different fine particle content does not decrease significantly because of the large and abundant pores in the samples. On the contrary, the permeability coefficient of silt with fine particle content of 63% and 47% only decreases by one order of magnitude compared with that without treatment, which is similar to the production amount of CaCO\({}_{3}\). All are caused by the large porosity in the sample. [PERSON] et al. [29] conducted permeability tests on samples with different relative densities, and the results showed that with the increase in calcium carbonate precipitation amount, the permeability of both loose and compact samples decreased.
## 2 Methods
### MRI Data Acquisition and Processing  
The experiment consisted of several MRI sessions as detailed in Section 2.3. Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and at the end of the study, were obtained from 16 participants. All MRI sessions took place between 8:00 a.m. and 10:30 a.m. The anatomical scans were performed on a 3T system (GE, MR-750) using a T1-weighted, 3D, inversion-prepared, gradient-echo BRAVO sequence defined with the following parameters: sagittal-oblique orientation, voxel size of 0.9 mm3, inversion delay of 450 ms, TR/TE of 6.6/2.5 ms, acceleration factors in the phase/slice directions of 1.75/1.25 for a total time of 3 min and 50 s. An eight-channel head coil was usually used, except for the end-of-the-year session of the first-round group in which, for technical reasons, the head configuration of the clinical HNS coil was employed. To ameliorate the potential bias, we added the group round as a covariate for the statistical model as detailed later.  
Preprocessing using the Statistical Parametric Mapping software (SPM12), adapting the optimized protocol described by [PERSON] et al. [39], consisted of an initial segmentation of all volumetric images to three constituents: the GM and WM neural tissues and the surrounding CSF. The preprocessing steps that were applied to each tissue segment were: (1) SPM old normalization utility fitting the end-of-the-year image to the beginning-of-the-year image keeping the amounts of the segments in the resulting images; (2) old normalization fitting the beginning-of-the-year to a matching standard MNI template segment and applying the same transformation parameters to the normalized end-of-the-year segment to the written volumes while keeping the concentrations of the segments in the resulting images; (3) spatial smoothing with an 8 mm kernel. The output of this pipeline provided the voxel-wise relative change of each tissue segment over the year.  
The final, MNI-space segments were fed into a paired \(t\)-test factorial design, adding the participant group as a covariate and selecting the ANCOVA regressors in the SPM analysis options. Each tissue segment was analyzed separately. The threshold applied to the statistical maps was \(p<0.001\), uncorrected. The reported GM clusters survived a threshold with \(p<0.05\), corrected for false detection rate.
## 5 Delay Calculation Error Analysis  
In the following, on the basis of the simulation results of 178 sets of examples, firstly we analyzed the delay calculation results of the single conversion standard and the multi-conversion standard by-vehicle type. The error values of the average delay of cars and buses calculated by the two methods were compared to verify that the multi-conversion standard delay calculation method was more accurate. Secondly, we compared the delay calculation errors of by-vehicle type and by-bus-line type of the multi-conversion standard, and then determined the delay calculation method of bus priority coordination control.
## 3 Results and Discussion
### Enrichment Factors and Principal Component Analysis  
Figure 3 presents EF of elements in the house dust samples. Several metals such as Fe, V, Mn, Sr, and Ba were of natural origin (EF < 10), while other metals such as Cu, Se, Zn, Hg, Cd, Pb (EF > 40) and Cr, Ni, As (20 < EF < 40) appeared to derive from anthropogenic processes. Figure 3: Enrichment factor (EF) of elements in house dust. Figure 2: Concentrations of COMPAHs in smoking and nonsmoking houses. Principal Component Analysis (PCA) was performed for the PAHs and the metal contents of all dust samples. Eight PCs (Principal Components) were extracted with eigenvalues >1, by which 91.9% of the total variance was explained. The predominant components of each factor (>0.5) are presented in bold (Table 3). Concerning PAHs, the first column includes Flut, Pyr, B [a] A, Chr, B [b] F, B [k] F, B [a] P, Ind, D [ah] A and B [ghi] P, while of the metals mainly includes Hg and less Ni. Thus, Factor 1 appears to be related to smoking indoors at home since the aforementioned PAHs belong to the COMPAHs category and are related to combustion processes, while both the Hg and Ni have also been suggested as derivatives of smoking [38]. This is also confirmed by Figure 2, where the concentrations of COMPAHS are considerably high in the houses of smokers compared to those of nonsmokers. Similar results were obtained for Hg (smokers' houses: 0.5, nonsmokers' houses: 0.2 ug/g) and to a lesser extent for Ni (smokers' houses: 24, nonsmokers' houses: 22 ug/g). On Factor 2, the contribution of Acy, Ace, FI, and Phe is important and to a lesser extent the one of Ant, indicating that their common source is the combustion from vehicle emissions. Therefore, this factor is related to traffic and the aforementioned species originating from ambient air penetrate indoors. On factor 3, the main contribution of Al, followed by Sr and V, indicates that the above elements have
## 3 Fault Diagnosis
### Current New Fault Diagnosis Algorithm
#### 3.2.2 Intelligent FD Methods Using Transfer Learning Theories
(2) Empirical and depth feature extraction  
Taking the empirical feature and multi-scale depth feature as the input, the sensitive feature selection and identification ability of random forest are used to complete the identification of the operation state of MSAE [120]. The parameters of the empirical feature extraction model and ResNet network were adjusted using the back-propagation network and gradient optimization algorithm. (3) Feature transfer learning  
The transfer learning needs to go through the following process. The empirical and depth joint feature transfer learning model for multi-source sensing information is designed, and the multi-core function space combination structure is constructed. Then the joint features are mapped to high-dimensional space. Finally, the popular learning mapping network is designed to complete the low-dimensional re-projection of high-dimensional spatial features. (4) Fault pattern recognition  
The hybrid kernel functions and the structural parameters of the network are optimized by the iterative method, and then the model training is completed. Based on the deep transfer learning FD model, combined with the incremental learning method, the network parameters are optimized using the continuously collected sensor data to further improve state recognition accuracy. Figure 8: Step of feature-based transfer learning approaches [119]. Because of the above characteristics of transfer learning theories, it is essential to obtain sufficient labeled data, which relieves the limitations of traditional machine learning on FD model training. Accordingly, an intelligent FD model using this method could solve the problem from theory to marine engineering application.
# Ontology-Based Spatial Data Quality Assessment Framework
## 2 Materials and Methods
### Framework  
The framework proposed in this study is depicted in Figure 1. The SfO Creation component is responsible for formalizing the rules according to the organization's application schema. Formalization of the rules for a domain can be stored and reused for further assessments. A GUI is designed for domain experts considering they may have no expertise in Semantic Web technologies. As a result of the rule formalization process with GUI, two files are generated: an R2 RML file to be used for data conversion and the other one is an SfO ontology for the institution. The data conversion component uses an R2 RML mapping file to convert spatial data to be validated, into RDF. The ontology mapping component maps the generated SfO, SDQO, GeoSPARQL, and data ontologies. The rule-based mapping carried out is established through Java programs with a GUI. Initially, using a now simple GUI, mappings can be declared by the domain expert, and are then converted to a CSV file. This CSV file is automatically reorganized and yields the ontologies and R2 RML mapping file. The mapping can also be edited. The Data Quality Assessment component is mainly based on SWRL rules created within SDQO, and assessment is carried out by inferencing with the help of a SWRL supporting reasoner, in this case, Openllet, an open-source version of Pellet [33], was chosen. The Java code uses facilities of the SWRLAPI library, as well. As a result of the quality assessment process, a final data quality report is produced.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Topo-Semantic Consistency Elements** & **Rule** & **Example** \\ \hline Must not overlap. (Different features of the same class) & No feature in a particular class overlaps with any other feature within the same class. & Buildings must not overlap. \\ \hline Must be within & Each feature in a class must be spatially within at least one feature in the other feature class. & A building must be within a cadastral parcel. \\ \hline Must not overlap with other class. & Features belonging to one class do not overlap with features in the other class. & Buildings must not overlap with the parcel. \\ \hline Must not cross & Features in one class do not cross over features in the other class. & Roads must not cross over buildings. \\ \hline \end{tabular}
\end{table}
Table 1: Topo-semantic consistency elements in the study.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Domain Consistency Elements** & **Rule** & **Example** \\ \hline No Null value/non-empty & For a class, a features cannot be empty. & Parcels have a non-empty PID value. \\ \hline Constant value & A given attribute of features in a specified class has a constant value. & All main roads have the same code. \\ \hline Value range & A given attribute of features in a class has values in a defined range. & The residential buildings can have at most five floors. \\ \hline \end{tabular}
\end{table}
Table 2: Domain consistency elements in the study.
## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.2 Machine Learning Algorithms  
Machine learning algorithms are able to analyze information and detect changes that may indicate a medical problem [60; 61; 62]. Early-stage health prediction has been approached using various machine learning techniques. Notable models and methods in this area include logistic regression models, ensemble methods, and decision trees [45; 63; 64; 65]. Through rigorous training on large datasets, these algorithms reveal hidden patterns and relationships in the data structure. Consequently, these detected patterns can be used to predict individuals' future health trajectories by gaining insights from their medical history and a constellation of relevant factors. Logistic regression models have been widely used in health status prediction because of their simplicity and interoperability [45; 66; 67; 68]. These  
Figure 5: Early health prediction approaches.  
models are suitable for binary classification tasks that involve predicting the presence or absence of a particular health state based on patient data. Ensemble methods, such as random forest and Gradient Boosting, have gained popularity due to their ability to combine multiple base models to improve prediction accuracy [46; 47; 69; 70]. They have been applied in the context of early health prediction to capture complex interactions in health data.