esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Step 1. GIS Analysis  
In the first step, all necessary data--collected from previous research and fieldwork--were compiled into a structured geodatabase. The data were standardized and imported into ArcGIS Pro [20], chosen for its robust desktop and online capabilities and the authors' familiarity with the platform.  
For each hazard examined, specific variables were selected based on their relevance and established practices in the Hellenic area. These variables were processed and classified according to their contribution to hazard susceptibility, using methods tailored to each hazard type:  
* Flood hazard: A preliminary assessment was carried out using morphological slope and geological data. Areas with low gradients and recent alluvial deposits were identified as more likely to experience flooding. This simplified approach was used due to the limited history of flood events on the island.
* Landslide hazard: The Analytical Hierarchy Process (AHP) was applied--a widely used method for evaluating multiple criteria by assigning relative weights based on pairwise comparisons. This method allowed for a structured combination of  
Figure 8: Workflow diagram of the research process followed in this study. The analysis begins with data collection and geodatabase creation, followed by GIS-based hazard mapping for floods, landslides, wildfires, and desertification. The resulting maps are then integrated into an interactive web application, leading to the publicly accessible Lefkas Island Hazard Explorer application.  
different factors, such as slope, lithology, and land use, to estimate susceptibility to landslides [21].
* Wildfire hazard: The method was based on the Guide for Drafting an Action Plan for the Prevention of Forest Fires, developed as part of the LIFE-Forest Cities program, drawn up in the framework of the European LIFE--Environment Program entitled \"Local Authorities for Forest Fire Prevention\" [22]. Parameters such as slope, land use, slope orientation, and proximity to roads, settlements, and power lines were included and weighted accordingly.
* Desertification hazard: The assessment followed the MEDALUS framework [23], which evaluates environmentally sensitive areas based on four indices: soil quality, climate quality, vegetation quality, and land management.  
Each dataset was spatially analyzed and normalized in ArcGIS Pro, leading to the creation of detailed hazard maps for each type of hazard.  
After generating individual hazard maps, a multi-hazard analysis was also conducted. This step involved combining the four hazard layers to highlight areas affected by more than one hazard following the methodology described by OECD 2008 [24]. The resulting map helps identify locations with cumulative hazard exposure and provides a broader perspective for spatial planning and disaster preparedness.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 3 Results
### Landslide Susceptibility Mapping by the WoE Model  
The relationship between the factors calculated by W\({}^{+}\) with the WoE method and disasters is shown in Table 4. The most sensitive range of slope is 20-30\({}^{\circ}\) (0.09). In the aspect, the west direction has the largest weight, 0.61. When it is at the height of 4800-5100 m, it is most prone to disasters (0.17). The most affected lithology is hard rock (1.23). When the distance to faults is 1800-2100 m, the probability of disaster occurrence is the largest, with a weight of 0.52. The 900-1200 m group displays the factor of the distance from the roads with the highest probability of disaster (0.89). At 900-1200 m away from the river, the calculated weight reaches the maximum (0.26) (Figure 6a). The final hazard assessment map is divided into five categories based on the natural discontinuity method: very low, low, medium, high and very high (Figure 7a). The percentages of disaster area showing the five classes from very low to very high are 0.23%, 2.11%, 7.59%, 26.20% and 63.86%, respectively (Table 5).  
The relationship between the factors calculated using the WoE method W\({}^{-}\) and disasters is shown in Table 4. The change in the W\({}^{-}\) calculation weight of each factor at each stage is shown in Figure 6b. The most sensitive slope class is 20-30\({}^{\circ}\) (0.32), the west direction has the largest weight (0.86), and the areas at a height of 4800-5100 m are most prone to disasters (0.47). The most affected lithology is hard rock (2.11). When the fault > 2100 m, the probability of disaster occurrence is the largest, with a weight of 1.14. The highest probability of the distance to roads between 900 and 1200 m is 1.05. At 900-1200 m away from the river, the calculated weight reaches the maximum (0.36). The final hazard assessment map is also divided into five categories based on the natural discontinuity method (Figure 7b). From very low to very high, the percentages of disaster area are 0.55%, 3.93%, 10.36%, 33.40%, and 51.76%, respectively (Table 5).  
Figure 5: Landslide susceptibility mapping by the IVM model.
# Fast and Efficient Lunar Finite Element Gravity Model
## 1 Introduction
### Chebyshev Polynomial Interpolation  
Developing a more efficient theory to solve higher-order Laplace's equation for gravity requires a structure that is both radially adaptive and efficient. If implemented correctly, interpolation at any radial or point on the surface can be conducted. One approach involves using the Chebyshev polynomial method. Created by mathematician [PERSON] in the nineteenth century, Chebyshev polynomials are two sequences of polynomials related to the cosine and sine functions denoted as \(T_{n}(x)\) and \(U_{n}(x)\), respectively. The polynomials can be defined starting with trigonometric functions where the Chebyshev polynomials of the first kind (\(T_{n}\)) are given by:  
\[T_{n}(cos\,\theta)=cos(n\,\theta) \tag{1}\]  
and the polynomials of the second kind (\(U_{n}\)) are:  
\[U_{n}(cos\,\theta)\,sin(\theta)=sin((n+1)\,\theta) \tag{2}\]  
in which \(n\) is any integer number that can be used to convert these definitions into a polynomial form. Chebyshev polynomials are orthogonal polynomials that are solutions of a special kind of Sturm-Liouville differential equation called a Chebyshev differential equation. Most importantly, the roots of \(T_{n}(x)\), i.e., the Chebyshev nodes, are used as estimation points. Approximating at this point produces efficient polynomial interpolation, which is useful for precise radial adaptation and other applications which require higher-order approximation.  
Although typically used as a numerical method for solving initial value problems (IVPs) and certain classes of boundary value problems (BVPs), this method can also be used to approximate solutions of linear or non-linear ordinary differential equations (ODEs) like that of Laplace's equations for gravity. Using these Chebyshev polynomials, an orthogonal approximation can be placed for the FEM modeling process in which the path approximations would encourage computational speedup.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 4 Results
### Metrics Evaluation and Prediction  
Table 5 provides a comprehensive overview of the metrics evaluation outcomes for the COVID-19 3D lung construction segmentation task using UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet architectures. Focusing on the architecture with the most prominent performance, Attention UNet emerges as the standout performer in this analysis. Among the evaluated metrics, Attention UNet attains superior results, underscoring its effectiveness in segmenting the 3D lung construction affected by COVID-19. Specifically, Attention UNet achieves an IoU score of 85.36%, a dice score of 91.49%, an accuracy rate of 98.63%, an F1 score of 98.64%, a sensitivity of 98.63%, and an impressive specificity of 99.32%.  
In Table 6, a comparison of the ground truth and prediction results of each model is presented in 2D, along with the 3D projection of each model. The visual analysis of the predictions provides additional insights into the performance of each model. The visualizations demonstrate that Attention UNet captures the intricate details of the lung construction more accurately, resulting in more precise segmentation of the infected areas compared to other models.
# Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
## 2 Related Work
### Distance Calculation by SAX  
The implementation of SAX mainly consists of three steps: Z-normalization of the dataset, dimensionality reduction using PAA, and finally symbolization. First of all, it is common to normalize the time series data so that each time series has a mean of 0 and a variance of 1 because time series data with different offsets and amplitudes are not directly comparable. Then, using a sliding-window approach, the original time series data are divided into w equal-length subsequences, and each subsequence is represented by its mean value. The normalized time series data follow a normal distribution, which  
Figure 1: Symbolized approximate representation of time series data SAX.  
provides mathematical support for dividing the probability distribution corresponding to the time series data into equal area regions and can ensure that the probability of the time series data falling into each interval is equal. The number of regions is determined by the input parameter \(\alpha\), which also represents the size of the character set. Each region is then represented by a symbol. Finally, based on the region in which the mean value of each subsequence falls, the corresponding symbol for that region is used to replace the subsequence, resulting in the symbolization approximation representation of the time series data.  
Assuming we have two time series data \(Q\) and \(C\) with the same length \(n\), which is divided into \(w\) time segments represented as \(q\) and \(c\), \(\hat{Q}\) and \(\hat{C}\) are the symbol strings obtained after applying the SAX algorithm. The SAX distance between \(Q\) and \(C\) can be calculated as the sum of the distance between each corresponding symbol and can be expressed as follows:  
\[\text{MINDIST}(\hat{Q},\hat{C})=\sqrt{\frac{n}{w}}\sqrt{\sum_{i=1}^{w}{(dist(q_ {i},\hat{c_{i}}))^{2}}} \tag{1}\]
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Reputation Models and Calculation  
The efficacy of the proposed reputation model is significantly enhanced by the incorporation of two pivotal components: the attenuation factor and the numerical threshold. These elements are instrumental in fine-tuning the reputation evaluation process, thereby ensuring both the timeliness and the accuracy of the trust assessments for each network node. The attenuation factor is a critical element within the reputation evaluation mechanism. It is a dynamic coefficient that reduces the influence of historical data on a node's present reputation. This factor is essential to maintain a balance between past and recent behaviors, thereby preventing outdated data from excessively influencing the current trust assessment. The precise calibration of the attenuation factor ensures that the reputation system remains responsive to the evolving trustworthiness of nodes, thereby safeguarding the network against both stale data and rapidly changing vehicular behaviors. Conversely, the numerical threshold establishes a clear-cut metric that the system utilizes to differentiate between normal and potentially malicious node actions. It acts as a predefined criterion that, when surpassed, triggers an alert within the system indicating the necessity for further investigation or immediate action. This threshold is determined through extensive analysis and is set to optimize the sensitivity and specificity of the system's response to anomalous behaviors. In operational terms, the attenuation factor and the numerical threshold are employed in tandem to maintain a robust and adaptable security posture within the VANET. The attenuation factor ensures that the reputation scores are reflective of the latest network interactions, while the numerical threshold provides a steadfast benchmark for automated response protocols. Together, they form a composite framework that significantly mitigates the risk of sophisticated cyber threats such as collusion and false information injection, thereby enhancing the overall security and functionality of the VANET.
## 3 Results and Discussion
### Quantitative Analysis of Concentration Based on CEM  
The test samples with a series of concentration and the weathering samples in seven days were used to analyze the quantitative accuracy of the method. In order to calculate the concentrations of the test samples and weathering samples, the processing occurred as follows:  
Firstly, the emission spectrum matrix of the initial concentration of test samples was \(146\times 1\), and the emission spectrum of concentration diluted by half was \(146\times 1\). The spectra of the two concentrations were combined to form test sample spectral matrix of \(146\times 2\). The spectral processing of weathering samples was similar to the above method.  
Secondly, the database of interpolated CEM generated above was decomposed into a series of matrices (\(146\times 2\)), with one column representing the spectrum of the \(n\)-th column and the other column representing the spectrum of the \((n+21)\) column in the concentration direction.  
Finally, the similarity between the decomposed matrices and the test matrices was used to calculate the concentration of test samples and weathering samples. The maximum similarity coefficient (r)  
Figure 5: The interpolation CEM spectra of 6 kinds of oil samples.  
means that the concentration was the predicted concentration of the test sample or weathering sample. The quantitative principle is shown in Figure 6.  
The similarity coefficient (r) of the matrix [19] is:  
\[\mathrm{r}\ =\ \frac{\sum_{m}\sum_{n}\bigl{(}A_{nm}\ -\overline{A}\bigr{)}\bigl{(} \bigl{(}B_{nm}\ -\ \overline{B}\bigr{)}\bigr{)}}{\sqrt{\bigl{(}\sum_{m}\sum_{n}\bigl{(}A_{nm}\ -\overline{A}\bigr{)}^{2}\bigr{)}\bigl{(} \sum_{m}\sum_{n}\bigl{(}B_{nm}\ -\ \overline{B}\bigr{)}^{2}\bigr{)}}} \tag{4}\]  
where \(\overline{A}\) is the mean (mean (\(A\))), and \(\overline{B}\) is the mean (mean (\(B\))).  
The accuracy of the quantitation of the six oils was evaluated based on two indexes: recovery rate (R) [20] and relative error of prediction (Rep) [21]. The formula is as follows:  
\[\mathrm{R}_{i}\ =\ \frac{\hat{C}_{i}}{\hat{C}_{i}}\ *\ 100\% \tag{5}\]  
\[\mathrm{REP}\ =\ \frac{\bigl{|}\hat{C}_{i}\ -\ C_{i}\bigr{|}}{\hat{C}_{i}}\ *\ 100\% \tag{6}\]  
where \(\hat{C}_{i}\) and \(\hat{C}_{i}\) are the predicted concentration and the actual concentration in the \(i\)-th unknown sample.  
Based on the interpolated CEM of six oil samples, the concentrations of test samples and weathering samples could be predicted. Figure 7 shows the predicted results of test samples with a series of concentration and weathering samples.  
Figure 6: Schematic diagram of the concentration quantification.  
Figure 7: _Cont._  
It can be seen that the recovery rates of predictions for test samples and weathering samples of six oils were between 86.8% to 116.11%, and the relative errors of predictions ranged from 2.09% to 15.2%. The results demonstrate that accurate quantification of the concentration of oils can be effectively realized based on CEM spectra and similarity analysis.
## 4 Algorithm Description
### Proxy Node Selection  
At initial epochs, nodes exchange meta-data with other nodes through opportunistic communications, e.g., epidemic routing [31]. When \(MC\) moves within the field, e.g., following the random waypoint mode, the neighboring nodes would send their sensed data and meta-data to the collector. After some time, \(MC\) would have accumulated enough meta-data for the selection of proxy nodes.  
The proxy selection is based on the \(weight\) of a node, which is composed of two parts: the _accumulated weight of stationary stay_\(\mu_{i}\) and the _number of distinct encounters_\(\eta_{i}\):  
\[w(s_{i})=\alpha*\frac{\mu_{i}}{\mu_{max}}+(1-\alpha)*\frac{\eta_{i}}{\eta_{ max}} \tag{8}\]  
where \(\alpha\in[0,1]\) is the balance factor, \(\mu_{i}\) is defined at Equation (6), \(\eta_{i}\) is the distinct number of nodes that contact with node \(s_{i}\). \(\mu_{max}\) and \(\eta_{max}\) are the maximal values of corresponding parameters \(MC\) has ever known. They could be extracted by \(MC\) through initial rounds of data gathering and meta-data exchanging. Parameter \(\mu_{max}\) and \(\eta_{max}\) are then broadcasted to the sensing field, and every node would use these parameters to calculate its weight. If \(w(s_{i})\) is greater than a predefined threshold \(th\), or a node is a proxy node in previous epoch yet does not upload its data to \(MC\), then \(s_{i}\) would promote itself as the proxy node at current epoch. Also, the number of proxy nodes could be controlled by the parameter settings. Proxy nodes act as the intermediate data storage as other nodes would forward their sensed data to them; then the mobile collector would come and pick up the data from the proxy nodes.  
It is worth to be noted that given the \(\mu_{max}\), \(\eta_{max}\) and \(th\), a node could determine whether it would act as a proxy node or not. If a node changes its state of role, e.g., to be newly promoted as a proxy node or turn back as a ordinary node, it would advertise itself by broadcasting a _state-change_ message. In this way, other nodes and \(MC\) will be informed on that change. Also, although nodes are classified into _ordinary nodes_ and _proxy nodes_, the network is not partitioned into clusters of subregions or subnetworks, e.g., a node is attached to some cluster and it only route its data to the cluster head. Instead, within the network all nodes are mobile, and they adopt an opportunistic way for message transmissions. When two nodes are in contact, they would establish a temporary communication link for message exchange. Ordinary nodes would forward their data to the proxy nodes whoever they encounter.
## 6 Error Analysis and Model Reliability
### Unreliable Predictions without Local Fit  
To improve the reliability of a ML model, [PERSON] and [PERSON] [25] proposed two principles to determine the reliability of a single sample point: (1) density principle: the tested sample point should be close to the training set; (2) local fit principle: the model should predict accurately on the training points closest to the tested sample point. Based on these two principles, a prediction of the ML model can be considered reliable when the tested sample is close to the training set and lies in the region where the model prediction is accurate.  
The density principle has been taken into consideration in Section 6.1, in which the distance of a sample point from the training set is measured by its main linear feature only. Subsequent experiments have shown that alternative approaches to account for the density rule do not perform better in identifying unreliable predictions. The local fit principle is partially considered in Section 6.2, in which samples with high prediction error are sorted out by a single decision tree. During the training of the decision tree, however, we must ensure each leaf contains enough samples, so very rare cases may not be separately labelled during the process. Consequently, we add an alternative approach here by identifying all test sample points whose closest points in the training set are predicted poorly by the model. The closest three points in the training set are found by nearest neighbour algorithm (KNN) [14]. Local accuracy of the three points is determined by their average absolute error. If the average error is as high as 90-percentile of all training error multiplied by 2.0, the local accuracy is considered low, and the prediction of the sample point is considered unreliable.  
The high error threshold as described above is set empirically, and a higher threshold results in fewer points identified as being unreliable. Figure 14 demonstrates the RMSE and critical error rate of all predictions and unreliable predictions identified with this approach. The identified data comprise all prediction points on average. The results shown in Figure 14 indicate that the method is also widely effective amongst various sites. The RMSEs of unreliable predictions are 0.6-1.5 times greater than average.
## 3 Methodology and Assumptions
### Economic Analysis  
The completion of a chemical plant design requires estimation of the major costs involved in the project. The total revenue requirement (TRR) method is applied to identify the costs associated with total capital investment, the fuel, operation and maintenance, and with the final products. According to the approach described in [36], the fixed-capital investment (FCI) is calculated as the sum of direct and indirect cost.  
The direct costs include the sum of onsite costs and offsite costs. Onsite costs include the purchased equipment cost (PEC), cost of installation, piping, instrumentation and control as well as cost for electrical equipment and materials. The PEC for pumps, heat exchangers, compressors, and expanders is based on data from [39; 40; 41], whereas in the cost calculations of the distillation columns also data from [42; 43; 44; 45] were used. Cost estimations for the SMR, ATR and DMR units were obtained from [39; 40; 45]. The modular method is applied along with an appropriate determination of a bare-module factor for the calculation of the onsite costs of each piece of equipment [40]. All data are brought to a common reference year (2016) by using the corresponding Chemical Engineering Cost Indexes (CEPCI).  
Regarding the offsite costs, civil, structural and architectural work is considered to be 30% of the total PEC, while contingencies amount to 15% of PEC. Other outlays include allowance for funds during construction, which are subject to an effective interest rate of 8% and a general inflation rate of 2.5%. After starting the construction in 2013, 40% of the FCI was invested in mid-year 2014 and the rest in mid-year 2015. The summation of the FCI and the other outlays gives the total capital investment (TCI). The operation of the plants started in 2016 and the economic life-time is assumed to be 20 years. The plants are operated with 8000 full load hours a year. The average price for natural gas was determined based on the Henry Hub natural gas spot price and is 2.9 US$/GJ. Regarding Case II, the costs for gaseous oxygen (GOX) were obtained from [46]. The fuel price is assumed to escalate by a rate of 1% annually.
## 2 Materials and Methods
### Reliability Study  
The reliability of spatial, temporal, and dynamic gait parameters as well as the location of COP, was assessed in a subset of 22 participants from the original sample of 42 by repeating the measurements one week after the initial testing under identical gait conditions. Intraclass Correlation Coefficients (ICC 3, \(k\)) were used to quantify reliability, employing a two-way mixed-effects model to evaluate absolute agreement across sessions [47]. ICC values > 0.5 indicated poor reliability, those between 0.5 and 0.75 suggested moderate reliability, values from 0.75 to 0.9 represented good reliability, and values < 0.9 reflected excellent reliability [47].  
The ICC values for absolute agreement across all walking conditions were good to excellent, ranging from 0.76 to 0.99 for the spatial variables, 0.76 to 0.97 for the temporal variables, 0.77 to 0.98 for the duration of gait phases, 0.78 to 0.96 for the location of COP, and 0.91 to 1.00 for the vGRFs exerted on various plantar regions of the foot.