esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Data analyses were performed for each test step separately and for the whole test. Prior to every analysis, a close inspection of the chest strap HR readings was taken to check for artifacts or possible interruptions in the readings. To test for a possible influence of the participant's sex in the difference between methods, an independent sample \(t\)-test was performed. To test for the association between devices, the coefficient of determination (R\({}^{2}\)) was calculated through a simple linear regression using a statistical software package (SPSS 29, IBM, Armonk, NY, USA). As a rule of thumb, the correlation was interpreted as very weak if R\({}^{2}\) < 0.04, weak if 0.04 \(\leq\) R\({}^{2}\) < 0.16, moderate if 0.16 \(\leq\) R\({}^{2}\) < 0.49, high if 0.49 \(\leq\) R\({}^{2}\) \(\leq\) 0.81, and very high if 0.81 \(\leq\) R\({}^{2}\)\(\leq\) 1.0 [26]. Using the same software package, the interclass correlation coefficient (ICC) was also calculated to assess the consistency between the device readings. Values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and greater than 0.90 were deemed indicative of poor, moderate, good, and excellent consistency, respectively [27]. To check for the measuring agreement between the devices, a Bland-Altman plot was computed in SigmaPlot (SigmaPlot 14.0, Grafiti LLC, Palo Alto, CA, USA). The lower and upper limits of agreement (LoA) were computed, as usually seen in the literature, as the mean difference \(\pm\) 1.96 SD [28]. Additionally, the clinically preferable LoA were manually set to a mean difference of \(-\)10 and +10 b-min\({}^{-1}\). The agreement was considered valid if at least 80% of the values fell within both limits, as described elsewhere [29]. An association of r > 0.90 (R\({}^{2}\) > 0.81), a bias of <5 b-min\({}^{-1}\), and an ICC > 0.90 were used to deem the readings as accurate.
## 4 Computations and Results
### Deformation and Stress Response of the Lining  
The fault displacement of 0.6 m is taken as an example case. The continuity of the displacement between the tunnel and surrounding rock mass is observed from the displacement contour shown in Figure 5a. Meanwhile, the vertical displacement curves of the tunnel lining under different fault dislocations are shown in Figure 5b, where the horizontal axis shows the longitudinal position of the tunnel, and the vertical axis represents the vertical displacement of the tunnel. As shown in the curves, the displacement values of the tunnel in the football gradually increase with the increase of the magnitude of the fault dislocation, and a stable phenomenon in the hanging wall. The main deformation of the tunnel lining is concentrated within a limited range of 30 m along the tunnel axis on both sides of the fault plane, and the influence range is not affected by the magnitude of the fault dislocation.  
The axial stress curves of the tunnel under different magnitudes of fault dislocation are shown in Figure 6a; a positive value indicates tensile stress. Figure 6b shows the shear stress curves of the tunnel. It can be seen that the lining in the hanging wall is mainly subjected to tensile stress while the lining in the football is primarily compressed. The stress increases with the fault dislocation. Particles near the fault plane exhibit stronger effects on the tunnel.
###### Abstract  
Western Arabia routinely experiences geophysical phenomena that deform the surface of the earth in a variety of ways. These phenomena include earthquakes, volcanic eruptions, sinkholes, and earth fissuring and fracturing. We perform a time-series analysis of interferometric synthetic aperture radar (InSAR) observations derived from the ESA Sentinel-1 radar satellite constellation to map regional surface displacements in western Arabia as a function of time. We rely on InSAR products generated by the JPL-Caltech ARIA project to detect regions with short wavelength anomalies, and then manually reprocess InSAR products at a higher resolution for these regions to maximize spatial and temporal coverage. We post-process InSAR products using MintPy workflows to develop the InSAR time series. We report short wavelength anomalies localized within alluvial valleys across western Arabia and find a 5 cm/year line-of-sight surface displacement within the Al-Yutamah Valley. Part of the observed subsidence is correlated with surface fractures that developed in conjunction with severe rainfall events in regions characterized mainly by alluvial sediments at the surface. Regions of observed subsidence that are not associated with any surface fractures or fissures are correlated with the presence of basalt layers at the surface. Both regions are subject to groundwater exploitation. The observed subsidence is inferred to be driven by groundwater withdrawal perhaps modulated by the presence of a preexisting depositional environment (e.g., paleo-lake deposits) that promotes unconsolidated soil compaction.  
geodetic imaging; InSAR time series; earth fissuring +
Footnote †: journal: remote sensing
## 3 Assessment of Precise MGEX Orbit and Clock Products
### SLR Validation
#### 3.3.1 Galileo  
The mean bias and STD values of SLR residuals for Galileo satellites are summarized in Table 9. SLR residuals with absolute values larger than 0.5 m were removed as outliers for Galileo. In this section, Galileo in-orbit validation (IOV), full operational capability (FOC) and FOC in elliptical orbit (FOCe) satellites are discussed separately. Most products showed close STD values at the 3- to 4-cm level for IOV and FOC satellites, while GRG, SHA and WUM showed larger STD values at the 6- to 7-cm level for FOCe satellites. Among all the products, ESM showed the smallest STD values for IOV, FOC and FOCe satellites with mean biases less than 1 cm. The GRG solution adopted antenna thrust modeling in April 2018 [6], which resulted in an obvious increase in mean bias values. From DOY105 2018, the satellite-averaged mean bias value of GRG products increased from 0.9 to 2.4 cm. What is more, it is interesting to note that SHA was the only AC whose mean biases were negative for all of the satellites. This could be explained as a result of the antenna thrust modeling not being considered in the SHA solution.
### Performance Concerns  
There is a need to model highly performing communication networks considering the criticality of offshore wind farm operations being dependent on data flowing through the data-acquisition system. The software-defined IIoT-Edge communication network performance is evaluated using metrics such as throughput, which measures the amount of data successfully transferred from source to destination; latency, which measures the time a packet travels from source to destination; packet loss, which measures the percentage of packets that are sent but do not reach their destination; bandwidth utilization, which measures how efficiently the network's bandwidth capacity is used; and QoS compliance, which evaluates how well the network supports the differentiated handling of packets to meet the requirements of the wind farm operator [112; 100].  
A highly performing network maximizes the throughput, QoS compliance, and bandwidth utilization and minimizes the latency and packet loss [113; 114; 115]. These software-defined IIoT-Edge communication networks must meet the industry SLAs as denoted in Table 1. To enhance performance, the implementation of redundancy strategies and failover mechanisms is essential to handle potential network failures without disrupting the wind farm operations. This involves deploying multiple SDN controllers and links to ensure that there is no single point of failure, thus maintaining continuous service availability and performance integrity.  
Several standards are explored to assess the performance of such networks in offshore wind farms and other smart grids. [PERSON] et al. [116] assess the end-to-end communication delay between communication devices in machine-to-machine communication application scenarios highlighting standards such as IEEE 1646 (communication delivery times within and external to an electrical substation), IEEE C37.1 (for wide-area situational awareness network performance in SCADA and automation systems), IEEE 1379 (communication protocol stack mapping for substation network functionality), and IEC 61850-5 (application recovery delay for substation bus communication) [65].
## 4 Validation and Results
### Instantiating Half Spaces  
With the obtained appropriate boundary size and buffer, the B-Reps for half spaces are automatically generated using the developed scripts. Figure 9 presents two examples of the generated B-Reps. To have a better view of them, two subfigures are provided for each half space representation with 0% and 30% transparency, respectively. In addition, the Institute model, colored by red, is placed along with them as a spatial reference. It is confirmed that the generated B-Reps for half spaces are sufficiently large in space to cover a building model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Quantity of B-Box** & **Maximum _XDim_** & **Maximum _YDim_** & **Maximum _ZDim_** \\ \hline House-1 & 82 & 13.0 m & 10.0 m & 3.6 m \\ Institute & 448 & 42.0 m & 18.0 m & 4.2 m \\ Smiley & 831 & 7.5 m & 7.3 m & 3.8 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum _XDim_, _YDim_, and _ZDim_ for bounding boxes (B-Boxes) in House-1, Institute, and Smiley.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Building Element** & **House-1** & **Institute** & **House-2** & **Smiley** \\ \hline IfcBeam & x & & x & x \\ IfcBuildingElementProxy & & & x & \\ IfcColumn & & x & x & x \\ IfcDoor & x & x & x & x \\ IfcMember & x & & & \\ IfcRailing & x & x & x & x \\ IfcRoof & & & x & \\ IfcSlab & x & x & x & x \\ IfcStair & x & x & x & x \\ IfcWall & x & & x & x \\ IfcWallStandardCase & x & x & x & x \\ IfcWindow & x & x & x & x \\ Total entity & 82 & 448 & 172 & 831 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Building elements in models and total quantity of entities for each element. “x” indicates the inclusion of a building element.
## 5 Performance Measures of PAS
### MTTF of the PAS  
The mean time to failure (MTTF) is also a very important metric of the reliability of the system. It measures the average amount of time a non-repairable system operates before it fails. From an organizational point of view, a large MTTF value indicates the system's greater durability and ability to perform work, and a small MTTF value indicates a lower durability and ability to perform work. If organizations know the MTTF of a system beforehand, then they may plan the timely repair of the system. In order to calculate the MTTF of the system using Equation (30), setting all repair rates equal to zero and the taking the limit s \(\to\) 0, one can easily obtain the MTTF of the public address system.  
\[MTTF=\frac{1}{\lambda_{mic}+\lambda_{mix}+\lambda_{amp}+2\lambda_{sp}}+\frac{2 \lambda_{4}}{\big{(}\lambda_{mic}+\lambda_{mix}+\lambda_{amp}+\lambda_{sp} \big{)}\big{(}\lambda_{mic}+\lambda_{mix}+\lambda_{amp}+2\lambda_{sp}\big{)}} \tag{35}\]  
Now, in Equation (35), set the failure rates to \(\lambda_{mic}=0.00002\), \(\lambda_{mix}=0.00005\), \(\lambda_{amp}=0.00006\), and \(\lambda_{sp}=0.00007\).  
Now, varying each failure rate one by one from 0.00001 to 0.00009 while fixing the other failure rates, one can easily obtain different values of the MTTF for the possible combinations of the failure rates. The MTTF of the system is given below in Table 4 and Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Variation in the** \\ **Failure Rate** \\ \end{tabular} & \begin{tabular}{c} **Variation in the MTTF** \\ **with Respect to Failure** \\ **Rate**\(\lambda_{mic}\) \\ \end{tabular} & \begin{tabular}{c} **Variation in the MTTF** \\ **with Respect to Failure** \\ **Rate**\(\lambda_{mix}\) \\ \end{tabular} & \
\begin{tabular}{c} **Variation in the MTTF** \\ **with Respect to Failure** \\ **Rate**\(\lambda_{amp}\) \\ \end{tabular} &
\begin{tabular}{c} **Variation in the MTTF** \\ **with Respect to Failure** \\ **Rate**\(\lambda_{sp}\) \\ \end{tabular} \\ \hline
0.00001 & 6680.1619 & 8152.1739 & 8787.8787 & 7619.0476 \\ \hline
0.00002 & 6296.2963 & 7598.0392 & 8152.1739 & 7450.9804 \\ \hline
0.00003 & 5952.3809 & 7111.1111 & 7598.0392 & 7236.8421 \\ \hline
0.00004 & 5642.6332 & 6680.1619 & 7111.1111 & 7002.8011 \\ \hline
0.00005 & 5362.31884 & 6296.2963 & 6680.1619 & 6763.2850 \\ \hline
0.00006 & 5107.5269 & 5952.3809 & 6296.2962 & 6526.3158 \\ \hline
0.00007 & 4875.0000 & 5642.6332 & 5952.3809 & 6296.2963 \\ \hline
0.00008 & 4662.0047 & 5362.3188 & 5642.6332 & 6075.5337 \\ \hline
0.00009 & 4466.2309 & 5107.5268 & 5362.3188 & 5865.1026 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MTTF with respect to variation in the failure rates.  
Figure 6: MTTF with respect to variation in the failure rates.
## 2 Related Work
By considering the membership degree, FCM enhances the accuracy of classification, especially in transitional zones or areas with gradual changes. Additionally, FCM is employed for detecting environmental changes by segmenting satellite images captured at different times. This facilitates the monitoring of deforestation, urban expansion, and other dynamic processes [31]. In climate studies, FCM is used to identify areas with high pollution concentrations [32]. FCM has been used in healthcare to identify areas with high concentrations of disease cases (such as epidemics) where cluster boundaries are vague [33]. In the context of hotspot analysis, FCM can be used to identify areas with values significantly different from the average, such as regions with high crime, high traffic density, or fires. Despite the many applications of FCM, its use in the analysis of UHIs remains an unexplored field. This study aims to demonstrate the effectiveness of this clustering algorithm in detecting UHIs, highlighting its several advantages over traditional detection methods commonly found in the literature. By integrating FCM within a GIS-based framework, our research introduces an innovative methodology for identifying UHI hotspots, providing urban stakeholders with a robust tool for implementing targeted measures to mitigate UHI effects and enhance urban resilience.
# Efficient Discretization of the Laplacian: Application to Moving Boundary Problems
## 2 Materials and Methods
### Numerical Experiments
#### 2.3.3 Numerical Results  
To validate our method for approximating the Laplacian, we evaluate the accuracy of the numerical solution of (10). Here, we measure the accuracy in the discrete \(L_{2}\)-norm depending on the number of grid points. The results, displayed in Table 1, clearly demonstrate the efficiency of the proposed vectorized discretization method. For a relatively fine grid with 2420 grid points, the construction of the FD discretization matrix \(D_{h,\text{FD}}\) took approximately one-sixth of a second.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 3 Methodology
### Analytical Technique

Table 2: Description of variables and data source. where some variables are stationary at levels and others stationary at first difference, a dynamic model for panel data with cointegration is required, such as the Pooled Mean Group (PMG) and Dynamic Fixed Effect (DFE) models. To understand the long-run impacts and the long-run adjustment rate, it is necessary to identify each country's short-run dynamics. The long- and short-run effects of ICTs and covariates (such as net national income, domestic credit to the private sector, and rural population) on crop production index over time and across different African countries using the panel ARDL can be estimated thus:  
\[\Delta\text{ln}Y_{it}=\beta_{i}+\beta_{11}\text{ln}X1_{i,t-1}+ \beta_{22}\text{ln}X2_{i,t-1}+\beta_{31}\text{ln}X3_{i,t-1}+\sum_{j=1}^{p_{1}} \gamma_{1 ij}\Delta\text{ln}Y_{i,t-j}+\sum_{j=1}^{p_{2}}\gamma_{2 ij}\Delta\text {ln}X1_{i,t-j}\] \[+\sum_{j=1}^{p_{3}}\gamma_{3i}\Delta\text{ln}X2_{i,t-j}+\sum_{j=1} ^{p_{4}}\gamma_{4 ij}\Delta\text{ln}X3_{i,t-j}+\sum_{j=1}^{p_{5}}\gamma_{2 ij} \Delta\text{ln}X4_{i,t-j}+\sum_{j=1}^{p_{6}}\gamma_{3 ij}\Delta\text{ln}X6_{i,t -j} \tag{3}\] \[+\sum_{j=1}^{p_{7}}\gamma_{4 ij}\Delta\text{ln}X6_{i,t-j}+e_{it}\]  
where \(\Delta\) denotes first differences, \(\beta_{i}\) is a constant, \(\gamma_{nij(n=1,\ldots,7)}\) denote short-run coefficients, \(\beta_{mi(m=1,\ldots,6)}\) are long-run coefficients, and \(e_{it}\) is an error term. Equation (3) could be restated with an error correction term as follows:  
\[\Delta\text{ln}Y_{it}=\hat{\sigma}_{i}+\sum_{j=1}^{p_{1}}\gamma_{1 ij}\Delta \text{ln}Y_{i,t-j}+\sum_{j=1}^{p_{2}}\gamma_{2 ij}\Delta\text{ln}X1_{i,t-j}+ \sum_{j=1}^{p_{3}}\gamma_{3 ij}\Delta\text{ln}X2_{i,t-j}+\sum_{j=1}^{p_{4}} \gamma_{4 ij}\Delta\text{ln}X3_{i,t-j} \tag{4}\] \[+\sum_{j=1}^{p_{5}}\gamma_{5 ij}\Delta\text{ln}X4_{i,t-j}+\sum_{j =1}^{p_{6}}\gamma_{6 ij}\Delta\text{ln}X5_{i,t-j}+\sum_{j=1}^{p_{7}}\gamma_{7 ij}\Delta\text{ln}X6_{i,t-j}+\lambda_{i}ECT_{i,t-1}+e_{it}\]  
where \(ECT_{i,t-1}\) is the error correction term. The paper applied the [PERSON] [77] panel causality test to determine causality among variables. The model of the Granger causality is written:  
\[y_{it}=\alpha_{i}+\sum_{j-1}^{l}\lambda_{i}^{j}y_{i(t-)})+\sum_{j=1}^{l}\beta _{i}^{j}x_{i(t-j)}+\mu_{it} \tag{5}\]  
where \(y\) and \(x\) are the observables. \(\lambda_{i}^{j}\) denotes the panel autoregressive parameters, while \(\beta_{i}^{j}\) denotes the regression coefficient estimates, and both are assumed to vary across cross-sections. The null and alternative hypotheses are stated as follows:  
\[H_{o}:\forall j:\beta_{i}^{j}=0\ H_{o}:\forall j:\beta_{i}^{j}\
eq 0 \tag{6}\]
## 7 Validation of Quantitative HydroSAR Information Layers
### Validating WD30  
To evaluate the WD30 products, we used water level data from the Flood Forecasting and Warning Center of the Bangladesh Water Development Board (BWDB) as reference. The BWDB operates 58 gauge stations across Bangladesh and publishes the water levels at these stations through their web interface. We retrieved gauging heights from 11 stations which intersected with our validation area at the confluence of the Padma and Meghna rivers in Bangladesh (Figure 14a). The BWDB's gauging stations use the Public Works Datum (PWD) established in India under British rule. The PWD reference height is roughly 1.5 ft below the mean sea level. We corrected this vertical offset between the BWDB datum and the EGM2008 datum used in the GLO-30 DEM (and therefore the HAND and WD30). Then, the gauge station water heights were compared to the nearest valid WD30 pixel for cross-validation.  
Figure 14b shows a comparison of the BWDB gauging heights and WD30 data for our validation site at the confluence of the Padma and Meghna rivers in Bangladesh. Different colored lines correspond to different dates during the 2020 monsoon season for which cross-validation was performed. The \(R^{2}\) values were close to 0.6 for different dates in 2020. This is because one gauge station, located in an isolated water basin, showed an exceptional range of water levels. If this gauge station were excluded, then an \(R^{2}\) value near or larger than 0.8 could be observed. Also, a root mean square error (RMSE) of less than 2 m was achieved for each of the analyzed dates. This is in line with the relative vertical accuracy of the Copernicus GLO-30 DEM, which is expected to be better than 2 m in flat terrain (slope \(<\) 20%) and better than 4 m in sloping terrain (slope \(>\) 20%) [57].
## 8 LiteOS
### Architecture  
LiteOS follows a modular architecture design. LiteOS is partitioned into three subsystems: LiteShell, LiteFS, and the Kernel. LiteShell is a Unix-like shell that provides support for shell commands for file management, process management, debugging, and devices. An interesting aspect of the LiteOS is its LiteShell that resides on a base station or a PC. This leverages to support more complex commands as the base station has abundant resources. The LiteShell can only be used when there is a user present on a base station. Some local processing is done on the user command (parsing a command) by the shell and then it is transmitted wirelessly to the intended sensor node. The sensor node does the required processing on the command and sends a response back, which is then displayed to the user. When a more does not carry out the commands, an error code is returned. It is worth noting that Contiki and Mantis also provide a shell to interact with the motes but the implementation code resides on the mote, limiting the capabilities of the shell due to resource constraints. The second architectural component of LiteOS is its file system LiteFS. LiteFS mounts all neighboring sensor nodes as a file. LiteFS mounts a sensor network as a directory and then list all one hop sensor nodes as a file. User on the base station can use this directory structure just like the normal Unix directory structure and a user can also use legitimate commands on these directories. We discuss LiteFS in more detail later on. The third major component of LiteOS is the Kernel that resides on the sensor node. The LiteOS kernel provides concurrency in the form of multithreading, provides support for dynamicloading, uses round robin and priority scheduling, allows programmers to register event handlers through callback functions, and provides synchronization support. Figure 6 shows the architecture of LiteOS.
## 5 The Proposed ECPSO-MTP for Data Clustering  
In this section, computational experiments with accuracy and convergence speed are introduced in order to verify the effectiveness of the proposed ECPSO-MTP, and a suite of commonly used datasets, including Artificial and UCI datasets, are adopted. To further demonstrate the validity of the proposed ECPSO-MTP, five previous clustering approaches are employed as comparison approaches. All clustering approaches are executed on a Dell desktop computer equipped with an Intel 8.00 GHz i7-8550U processor and 16 GB of RAM, operating under the Windows 11 environment.
# Integrating BIM and GIS for an Existing Infrastructure
## 3 Interoperability Issues
### The Data Integration Problem  
The main challenge in BIM-GIS integration is the transfer of data between these domains. The main issue is the difference in the geometric models used in these systems. GIS uses boundary representation (B-Rep), while BIM uses construction solid geometry (CSG) representation. This difference makes it difficult to compare the two models and to map the data between them. On the other hand, both BIM and GIS have their own schemes and languages in the semantic field. The information is ordered and stored differently, which makes the mapping between the two domains at metadata level difficult [59; 60].  
Most studies to date have focused on the 3D representation of buildings in GIS. This interoperability has mainly been realised through the open formats IFC and CityGML [1; 7; 58; 61]. This leads to a loss of information, which is even more acute in the case of civil infrastructure. Firstly, the use of the IFC format implies the assumption of predefined parameters, focused on building. The conversion from IFC to CityGML format presents two main problems. First, the IFC format does not fully support the 3D representation of civil infrastructure. Second, the CityGML format is not designed to store all the information that is contained in the IFC format. This can lead to a loss of information during the conversion process. The mapping of information in the semantic field between BIM and GIS has been the subject of many studies [1; 7]. However, there is still no agreed-upon standard for these mappings, and different authors have used different approaches. This can make it difficult to integrate BIM and GIS models that have been created using different software applications. Although there have been significant improvements in this area, integration ultimately requires the simplification of the model [62].