esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Study
### Parametric Analysis
#### 3.5.2 Influence of the Pile Young's Modulus  
The results of the calculations for the variation in Young's modulus of the GPF from \(2\times 10^{5}\) MPa to \(2\times 10^{7}\) MPa are shown in Figure 15. When Young's modulus is \(2\times 10^{5}\) MPa, the characteristic value of the lateral bearing capacity for GPF is 0.4 kN. As [PERSON]'s modulus increases to \(2\times 10^{6}\) MPa, the lateral bearing capacity increases to 1.2 kN,  
Figure 14: Influence of soil friction angle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Friction Angle of the Soil** & **Young’s Modulus of the Pile** & **Length of Pile** \\  & \(\varphi\)/(\({}^{\circ}\)) & **E/MPa** & **L/m** \\ \hline \multirow{3}{*}{Value} & 19 & 200 & 0.8 \\  & 22 & 2000 & 0.9 \\ \cline{1-1}  & 25 & 20,000 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The adopted parameters of the GPF in parametric analysis.  
and further increasing Young's modulus to 2 \(\times\) 10\({}^{7}\) MPa results in a capacity of 1.6 kN. The increase in capacity is 0.8 kN and 0.4 kN, respectively. This indicates that the material properties of the foundation are an important factor influencing the lateral bearing performance of the GPF, and a higher Young's modulus of the foundation can provide a higher lateral bearing capacity. However, beyond a certain range, further increasing Young's modulus does not significantly improve the lateral bearing capacity of the GPF.
## 3 Results and Discussion
### Monitoring Crop Phenology  
Another application is vegetation dynamics, which allows us to monitor the seasonal progression of vegetation. Coarse resolution satellite images have been used extensively to study land surface phenology at continental to global scales (e.g., [62]). However, smaller ecosystems are particularly important as they are associated with their environment and hence can be more easily interpreted and associated with the local ecosystem.  
In Figures 5-8 we demonstrate the provision of rapid and precise (in terms of radiometry and spatial distribution) information on land surface phenology for smallholder farm fields. More precisely, Figure 5 shows the evolution of individual fields within the crop season as captured by DG satellites, while Figure 6 presents the progression of vegetation indices for two fields in Mali and Nigeria for the years 2014 and 2015, respectively. Figure 7 depicts a 3D spectro-temporal spectral signature representation indicating the eight-band signatures for the acquisitions for two classes of interest, before and after the sowing date for a single farm. Based on these field-specific metrics, regional statistics can be derived. For instance, in Figure 8, we show the average and standard deviation of NDVI from all available images processed from the workflow for all peanut fields in Sukumba, Mali, for 2014 and for all maize fields in Kofa, Nigeria, for 2015. In this figure, users can observe the phenology of a specific crop for a given area cumulatively and derive information on crop statistics for a larger area instead of an individual farm. A total of 17 and 12 observations were collected for the Mali and Nigeria fields, respectively. This indicates that, in spite of using all the available DG satellites, the number of cloud-free images acquired from this constellation in the typical climatic conditions of the tropical savanna is frequently constrained by cloud coverage. The results indicate a fair agreement with the spectral evolution of the crop cycle and could possibly be improved further by a cross-sensor calibration inclusion in the workflow, which is planned for the future. While the image footprints of DG and RapidEye data are not large enough to cover a whole country, data from Sentinel-2 could potentially be ingested in this workflow to address the need for countrywide information. However, the limitations imposed by the coarser spatial resolution have to be examined and addressed; for
## 2 Theoretical Background
### Parametric Lifetime Models
#### 2.4.1 Exponential Distribution  
This distribution represents the useful-life period in the bathtub curve [19]. The probability-density function (PDF), cumulative-distribution function (CDF), failure-rate function (FRF), and the mean time to failure (MTTF) are given by the following formulae, respectively [19]. Here, the time to failure \(t\) is considered a random variable, and \(\lambda\) is the scale parameter [19].  
\[f(t;\lambda)=\begin{cases}0&for\ t<0\\ \lambda e^{-\lambda t}&for\ t\geq 0\end{cases} \tag{3}\]  
\[f(t;\lambda)=\begin{cases}0&for\ t<0\\ 1-e^{-\lambda t}&for\ t\geq 0\end{cases} \tag{4}\]  
\[h(t;\lambda)=\frac{f(t)}{R(t)}=\frac{\lambda e^{-\lambda t}}{e^{-\lambda t}}=\lambda \tag{5}\]  
\[MTTF=\int_{0}^{\infty}R(t)\ dt=\frac{1}{\lambda} \tag{6}\]
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 1 Introduction
This paper is organized as follows: Section 2 provides an overview of the background knowledge and related work; Section 3 describes the distributed multi-source remote sensing metadata transformation and integration; Section 4 details the data management methods, including the LSI spatial organization model, full-text index construction, and distributed data retrieval; Section 5 introduces the experiments and provides an analysis of the proposed program; and Section 6 provides a summary and conclusions.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 3 System Model and Problem Formulation
### Problem Formulation  
The offloading delay includes transmission delay and computation delay, which are presented in (9) and (10) separately. To minimize the sum of the offloading delay in the period \(T\) which contains \(N\) slots, the optimization problem can be expressed as follows:\[\min_{a_{k,i},P_{k,i},\mu_{0,j},\kappa_{k,i}}\sum_{i=1}^{N}\max_{ \forall k\in\mathcal{M}}t_{k,i} \tag{17}\] \[s.t.0\leq\sum_{k=1}^{M}\alpha_{k,i}\leq 1\] (18) \[0\leq\alpha_{k,i}\leq 1,\quad k\in\mathcal{K},i\in\mathcal{T}.\] (19) \[R_{k,i}=\log_{2}(1+\frac{P_{k,i}h_{k,i}}{\sigma^{2}}),\] (20) \[t_{0,i}=\frac{\alpha_{0,i}D_{i}s}{C_{0}},\] (21) \[t_{k,i}=\frac{\alpha_{k,i}D_{i}s}{C_{0}}+\frac{\alpha_{k,i}D_{i} }{R_{k,i}B}+2\frac{d_{k,i}}{c}\] (22) \[\sum_{i=1}^{N}r_{m}C_{0}^{2}D_{i}\alpha_{0,j}+\sum_{i=1}^{N}\sum _{k=1}^{M}\frac{P_{k,i}\alpha_{k,i}D_{i}}{R_{k,i}B}\leq\mathcal{E}_{1}\] (23) \[\sum_{i=1}^{N}r_{k}\alpha_{k,i}D_{i}C_{k}^{2}\leq\mathcal{E}_{2}\] (24) \[\sum_{i=1}^{M}P_{k,i}+r_{m}C_{0}^{3}\leq\mathcal{P}_{m}\qquad \qquad\qquad\qquad\qquad\qquad\qquad. \tag{25}\]  
In order to eliminate denominator variables, we introduce auxiliary variables and derive a new expression. Here, \(\kappa_{k,i}\) represents the reciprocal of \(R_{k,i}\) based on the initial problem formulation:  
\[\kappa_{k,i}\log_{2}(1+\frac{P_{k,i}\mu_{0}}{\sigma^{2}d^{2}})=1 \tag{26}\]  
The reformulated problem is as follows:  
\[\min_{a_{k,i},P_{k,i},\kappa_{k,i}}\sum_{i=1}^{N}\max_{\forall k \in\mathcal{M}}t_{k,i} \tag{27}\] \[s.t. t_{k,i}=\frac{\alpha_{k,i}D_{i}s}{C_{0}}+\frac{\alpha_{k,i}\kappa_ {k,i}D_{i}}{B}+2\frac{d_{k,i}}{c}\] (28) \[t_{0,i}=\frac{\alpha_{0,i}D_{i}s}{C_{0}}\] (29) \[\sum_{i=1}^{N}r_{m}C_{0}^{2}D_{i}\alpha_{0,i}+\sum_{i=1}^{N}\sum _{k=1}^{M}\frac{P_{k,i}\alpha_{k,i}D_{i}\kappa_{k,i}}{B}\leq\mathcal{E}_{1}\] (30) \[0\leq\alpha_{k,i}\leq 1,\quad k\in\mathcal{K},i\in\mathcal{T}.\] (31) \[R_{k,i}=\log_{2}(1+\frac{P_{k,i}\mu_{0}}{\sigma^{2}d^{2}}),\] (32) \[\sum_{i=1}^{N}r_{k}\alpha_{k,i}D_{i}C_{k}^{2}\leq\mathcal{E}_{2}\] (33) \[\sum_{i=1}^{M}P_{k,i}+r_{m}C_{0}^{3}\leq\mathcal{P}_{m},\] (34) \[\kappa_{k,i}\log_{2}(1+\frac{P_{k,i}\mu_{0}}{\sigma^{2}d^{2}})=1. \tag{35}\]  
Nevertheless, there are still a significant number of equality constraints and coupling variables. Traditional methods like gradient descent often struggle to achieve satisfactory results when dealing with such nonconvex optimization problems.  
The increasing penalty dual decomposition (IPDD) algorithm excels in handling the complexity of nonconvex problems and efficiently optimizing the solutions by combining the penalty method and the Alternating Lagrangian (AL) method [16]. However, the IPDD algorithm cannot take advantage of the distributed structural characteristics, and cannot obtain the globally optimal solution.
## 1 Introduction
Simultaneously, it orchestrates discharge-resilient paths that prioritize latency-sensitive AI flows along shallow-discharge routes while implementing payload-aware topology pruning to deactivate non-critical LISLs during computation lulls. Central to this approach is a novel battery aging model that explicitly couples DoD stress profiles with LISL power transients under characteristic AI workloads. The main contributions of this work are as follows:  
* Development of a comprehensive energy-efficient routing model that integrates satellite energy dynamics with operational strategies to optimize network longevity and performance in edge-AI-driven SONs;
* Introduction of a global traffic model that addresses spatial and temporal imbalances in traffic demands, incorporating load dormancy strategies to enhance edge AI sustainability;
* Proposal of a novel topology planning scheme leveraging payload dormancy to minimize energy consumption in large-scale satellite constellations;
* Design of the green task-aware routing planning (GreenRP) algorithm, validated through extensive simulations to optimize network performance and energy utilization. The organization of the remainder of this paper is as follows: Section 2 analyzes energy bottlenecks in edge-AI-driven SONs, examining operational constraints and battery degradation mechanisms. Section 3 details GreenRP's architectural design, including its task-offloading engine and aging-aware routing protocols. Performance evaluation and analysis are presented in Section 4. Finally, Section 5 concludes the study.
# Modelling the Structure and Anisotropy of London Clay Using the SA_BRICK Model
## 4 Validation of the SA_BRICK Model
### Verification of the Modelling of Anisotropy
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Basic BRICK Parameters**} \\ \hline _fSL_ & \(\varphi\) & \(\iota\) & \(\lambda\) & \(\kappa\) & \(\beta^{G}\) & \(\beta^{\phi}\) & \(v\) & \(\mu\) \\
0.8 & 20.5\({}^{\circ}\) & 0.0036 & 0.08 & 0.035 & 4 & 3 & 0.2 & 1.3 \\ \hline \multicolumn{10}{c}{**Anisotropy Parameters (after [45])**} \\ \hline _Elu/Ev_ & _Ghlh/Gvh_ & _vhlh_ & \(\zeta\) & & & & & \\
3 & 2 & 0 & 0.04 & & & & & \\ \hline \multicolumn{10}{c}{**Structure Parameters**} \\ \hline \(\alpha\) & \(\omega\) & & & & & & & \\
1.2 & 0.85 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Input parameters for SA_BRICK. Figure 8: Schematic of the geological stress history of London clay (after [47]). A good agreement can be observed between the modelled and measured data in all diagrams. All aspects of the mechanical behaviour of the tested samples are within reasonable limits, covering both compression and extension shearing. Generally, in undrained tests, effective stress paths in the laboratory were driven by the changes in pore pressures. This change was underestimated by the SA_BRICK model so there was no possibility to model the stress path exactly (e.g., Figures 9a and 10a), which was reflected at large strains so that the absolute value of the maximum deviator stress \(q_{max}\) was almost exactly matched but twice as large as the axial strain \(\varepsilon_{a}\) (e.g., Figures 9b and 10b). The sample 27 UC (Figure 11) was sheared along pre-existing fissures which was reflected again at large strains so that the prediction of \(q_{max}\) was about 50% larger that the measured value. In particular, a good agreement was observed in the area of small strain behaviour, that is, for the shear strains within the region 0.0001% to 0.1%, in which elastic plateau was modelled within the 10% margin. These results enabled a next stage in this research, which was the modelling of the tunnel excavations in London clay, which will be explained in continuation.
# Users Empowered in Smart Grid Development? Assumptions and Up-To-Date Knowledge
## 3 Results
### Energy Saving  
Smart grid systems are expected to stimulate residential users to save electricity. This expectation stems from two integrated smart grid developments. First, the diffusion of distributed renewable energy may trigger a motivation to save energy. Here, the mere possession of renewable energy technologies is assumed to trigger further behavioral change. A literature review reported that little is known about this effect [22]. While the installation of a PV system stimulates many households to reduce their overall electricity consumption according to themselves, the rare reports on actual consumption show otherwise, partly due to rebound effects.  
Secondly, smart meters are applied to provide residential users with regular information about their electricity consumption who hitherto lacked such insight. The related assumption is that energy consumption data, supplemented with, for instance, the related financial incentives, or a comparison with other households will stimulate a reduction in electricity consumption. The data provided with smart meters, however, are seldom the preferred and recommended real-time data.  
For groups of users, energy saving seem to be quite important. [PERSON] et al. [14], for instance, reported that not all discussants in the focus groups were convinced of the primacy of the time-shifting problem over other sustainability issues. [PERSON] and [PERSON] [8] concluded on the basis of a comparison of 11 Danish experiments that other motivations are an interest in new energy technologies, being more environmentally friendly, and reducing the overall energy consumption.  
Few studies have investigated energy saving in smart grid pilot projects. One of the exceptions is the Danish eFlex project. The study showed an overall reduction in electricity consumptions in response to the feedback from home energy management systems: \"the 'control' household group, [with only the management system]... had on average saved approximately 10% on their kilowatt hour consumption during the project period March 2011 to February 2012\" [15].  
While energy saving is of importance for specific user groups, it has received too little attention to arrive at conclusions about the relationship between smart grids and energy saving. A complicating factor is that the term \"energy saving\" has become ambiguous with the introduction of smart grids. Some researchers use it for demand shifting rather than reducing the overall energy consumption (see, for instance, [20]). Moreover, due to the electrification trend accompanying the smart grid development, just assessing increases or decreases in kilowatt hour consumption has little meaning. The meaning of, and aspirations regarding energy saving hence needs reviewing in the context of smart grids.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 5 Conclusions and Future Work  
In this work, we presented a novel approximate image-space multidirectional occlusion shading model for volume rendering. Our model was a unified shading model to simulate different light sources, and it provided more visual cues with minor performance overhead compared to using only local illumination. The occlusion for the opaque and transparent medium was handled separately using the full and partial occlusion methods. Our model caused small overhead in texture memory compared to the previous techniques and did not require any preprocessing. The transfer function and light position could be changed interactively. Moreover, we introduced an opacity approximation that allowed us to compute opacity values at any depth z instead of using additional opacity buffers. In order to produce a more natural shading effect, we blended the occlusion shadings from multiple layers, and we were able to produce realistic shading results through a transparent volume. Multiple directional light sources enhanced visual cues more with a  
Figure 12: ISMOS on the beetle data. Opaque and transparent transfer function settings are used in (**a**–**d**) and (**e**–**h**), respectively. The image-space horizon-based ambient occlusion [5] is used in (**b**).  
Figure 13: Comparison of the deep shadow map [27] and ISMOS on the bonsai data. Local illumination in (**a**), ambient occlusion in (**b**), deep shadow map in (**c**), and our hard shadow by ISMOS in (**d**) are compared.  
small overhead. Then, we tested our ISMOS model with various datasets and proved that the ISMOS provided better visual cues with a small amount of additional computation. Our model can be applied to any rendering system since the ISMOS is decoupled from the rendering pass. As future work, we will optimize the ISMOS computation. Since the light source can be located anywhere in a volume, we will study an efficient image-space subsurface scattering effect. Moreover, we will investigate more efficient layer management to handle more layers in the future.  
All authors contributed to this study. [PERSON] developed the system and wrote the article. [PERSON] developed a software module in the system. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Basic Research Program through the National Research Foundation of Korea(NRF) funded by the MSIT (2019R1A4A1021702), in part by Institute of Information & communications Technology Planning & Evaluation(IIITP) grant funded by the Korea government(MSIT) (No.2019-0-00242, Development of a Big Data Augmented Analysis Profiling Platform for Maximizing Reliability and Utilization of Big Data), and in part by the Institute for Information & communication Technology Planning & evaluation(IITP, Korea) funded by the Ministry of Science and ICT(MSIT, Korea) (2019-0-00795, Development of integrated cross-model data processing platform supporting a unified analysis of various big data models).  
Not applicable.
## 2 Materials and Methods
### Scope of Application for Effective Electromagnetic Parameters  
Based on numerous field experiments (Figure 1), it has been observed that modern geodynamic processes lead to rock reorganization--changes in structural and textural characteristics. These modifications manifest as variations in the macro anisotropic electromagnetic parameters of the geological media at the macroscale level. These phenomena were first recognized by [PERSON] [51]. To study these phenomena, it is necessary to explore the connection between the microstructure of rocks and their macroscopic parameters. This connection is addressed through the EMA theory, which considers effective electromagnetic parameters of micro-inhomogeneous media. The EMA theory assumes a relationship between the macroscopic parameters of the medium and its micro-composites, resulting in anisotropy [62]. Some electrical anisotropy studies started in the 20 th century include those by [PERSON] and [PERSON] [39], [PERSON] et al. [63], and [PERSON] [64].  
In the context of the central Tien Shan region and its deep-seated rocks (depicted in Figure 4), the investigation of effective electromagnetic parameters becomes crucial. These rocks exhibit a micro-heterogeneous nature, and understanding their behavior requires the calculation of effective electromagnetic parameters. This relationship aligns well with the observations made in the MTS turnaround analysis depicted in Figures 2 and 3. By studying the relationship between the microstructure and macroscopic parameters of such rocks, we can gain insights into the anisotropic properties and their impact on electromagnetic monitoring data.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 3 Methodology
### BRDF Model
#### 3.1.2 BRDF Model Construction  
The key of the BRDF model construction method proposed in this paper is to screen the images of sunny day which are extracted by using TOA BT and VC. At the Dunhuang and Golmud sites, a 6 \(\times\) 6-pixel (6 km \(\times\) 6 km) region of interest (ROI) is selected, and the time-series TOA reflectance of the ROI in 2014 is calculated. Then, TOA BT in band 31 and VC in band 1 are calculated by average TOA radiance. It is assumed that the TOA-BT variation of adjacent date images should be less than 10 K in clear weather. Then, we built the EBT and compared the difference between EBT and BT of each image. The image with the temperature difference less than 10 K is regarded as imaged in a sunny day, otherwise, cirrus spissatus existed in the image. Figure 5 shows the extraction results of a sunny day.  
Moreover, it is assumed that if the VC of the image is more than 4% it is due to the presence of cloud fragmentation. Then, the image is excluded from sunny day images. Figure 6 shows the extraction results of TOA reflectance (MODIS band 1) from sunny day images with VC less than 4% and the difference less than 10 K between TOA BT and EBT.  
Finally, images satisfying the difference between EBT and TOA BT less than 10 K and VC less than 4% are selected as valid sunny day images, and the information of TOA reflectance and observation angles (including solar zenith angle, solar azimuth angle, view zenith angle, and view azimuth) are extracted. Then, the BRDF correction coefficients are calculated using the kernel-derived model  
Figure 5: Top-of-atmosphere (TOA) bright temperature (BT) of all images (blue hollow rhombus) and images of sunny days with difference less than 10 K between TOA BT and envelope brightness temperature (EBT) (red solid frame): (**a**) Dunhuang; (**b**) Golmud.  
Figure 6: TOA reflectance of all images (red hollow box) and images of sunny days with variation coefficient (VC) less than 4% (blue solid rhombus): (**a**) Dunhuang; (**b**) Golmud.