esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Lidar Data from NASA GEDI  
The Global Ecosystem Dynamics Investigation (GEDI) is a NASA mission that measures forest structure in temperate and tropical forests between 51.6\({}^{\circ}\) north and south. It is a large-footprint lidar system placed on the International Space Station (ISS) since 2018 and it generates waveforms and has a footprint diameter of \(\sim\)25 m. The aim of this mission was to determine the effects of changing climate and land use on ecosystem structure and dynamics [14].  
The GEDI laser system consists of three lasers: a coverage laser that is split into two lasers and two full power lasers that remain unchanged. The tracks are located within a \(\sim\)4.2 km wide strip with a spacing of \(\sim\)600 m and a footprint spacing per track of 60 m. The GEDI data consist of footprint and raster datasets and include information on the 3D characteristics of the vegetation. These data are associated with different levels of data processing. For this study, the Level2A data were used. These data contain ground height, canopy height and relative height percentiles (here a relative height of 95% (RH95) was used) [14].  
For this study, data from 2019 to 2020 for the Amazon rainforest ecoregion were used. After filtering the data by quality flag and workflow processes about 110 million individual GEDI waveforms were available [3; 23]. GEDI waveforms provide structure-based snapshots of aboveground biomass and forest productivity that can be decoded with the FORMIND forest model.
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 3 A New Model for Predicting Minimum Void Ratio of Gap-Graded SRMs Incorporating Particle Breakage
### A Model for Predicting Minimum Void Ratio of Gap-Graded SRMs Incorporating Particle Breakage
\(e_{c}\) and \(e_{f}\) are the void ratio of pure coarse-grain particles and pure fine-grained particles, which can be easily obtained by conducting the compaction tests on pure coarse-grained and fine-grained particles. \(k\) and \(k^{\prime}\) are material constants. Data of \(k\) and \(k^{\prime}\) are plotted versus fractal dimension \(D\) of basic gradation in \[\Delta V_{A}=aV_{f} \tag{5}\]9 It can be found that the relationship between \(k\), \(k^{\prime}\) and \(D\) can be well fitted by a quadratic polynomial function, which is given by \(k\)_or_\(k^{\prime}=AD^{2}+BD+C\). \(A\), \(B\) and \(C\) are material constants irrespective of gap intervals. Therefore, if the parameters of \(D\), \(e_{c}\), \(e_{f}\) of a gap-graded SRM are given, we only need to conduct compaction tests of one gap interval to obtain the parameters of \(e_{c}\), \(e_{f}\), \(A\), \(B\), \(C\), and we can predict the minimum void ratio of gap-graded SRM with other gap-intervals. It is noted that we can obtain the parameters \(k\) and \(k^{\prime}\) by conducting some compaction tests if the gradation information is not given. Figure 13.0 Phase diagrams for fine-grained particles dominant: (a) pure fine-grained particles (before coarse-grained particles added), (b) mixture (limiting case), (c) mixture (general case).
## 5 Case Study
### Experimental Setup
#### 5.1.2 Evaluation Metrics  
The evaluation metrics used in this study are divided into three patterns, accuracy (RMSE), similarity (Ang, Len, and FSN), and computation time. The known values \(\left\{x_{ij}:(i,j)\in M_{0}\cup M_{1}\right\}\) in \(\mathbf{X}\) are the ground truth in evaluation. In particular, RMSE between predicted and true values is calculated by  
\[\left\{\begin{array}{l}\text{RMSE}_{0}=\sqrt{\frac{1}{\left|M_{0}\right|} \underset{(i,j)\in M_{0}}{\sum}1_{\{i_{j}\
eq x_{ij}\}}}\\ \text{RMSE}_{1}=\sqrt{\frac{1}{\left|M_{1}\right|}\underset{(i,j)\in M_{1}}{ \sum}\left(\hat{x}_{ij}-x_{ij}\right)^{2}}\end{array}\right.\Rightarrow\text{RMSE}= \sqrt{\text{RMSE}_{0}{}^{2}+\text{RMSE}_{1}{}^{2}} \tag{17}\]  
where \(\text{RMSE}_{0}\) and \(\text{RMSE}_{1}\) are RMSEs of categorical and continuous variables, respectively.  
Evaluation metrics of similarity include: (1) Ang, sum of angles of the first three principal components calculated for true (\(\mathbf{\theta}\)) and predicted (\(\hat{\mathbf{\theta}}\)) FRs (ideal value is 0); (2) Len, sum of length of projections of \(\mathbf{\theta}\) to \(\hat{\mathbf{\theta}}\) (ideal value is 3); (3) FSN: mean fraction of the same neighbors from the \(k\) nearest neighbors between true and predicted FRs (ideal value is 1).  
\[\text{Ang}=\sum_{i=1}^{3}\arccos\langle\mathbf{\theta}_{i},\hat{\mathbf{\theta}}_{i} \rangle, \tag{18}\]  
\[\text{Len}=\sum_{i=1}^{3}\sqrt{\sum_{j=1}^{3}\left\|v\big{(}\mathbf{\theta}_{i}, \hat{\mathbf{\theta}}_{j}\big{)}\right\|}, \tag{19}\]  
\[\text{FSN}=\frac{1}{N}\sum_{i=1}^{N}\frac{\left|M_{i}\cap\hat{M}_{i}\right|}{ \left|M_{i}\cup\hat{M}_{i}\right|}, \tag{20}\]  
where \(\left\|v(\mathbf{\theta}_{i},\hat{\mathbf{\theta}}_{j})\right\|\) represents the length of projections of \(\mathbf{\theta}_{i}\) to \(\hat{\mathbf{\theta}}_{j}\); \(M_{i}\) and \(\hat{M}_{i}\) are the \(k\) nearest neighbors' coordinates of \(\mathbf{x}_{i}\) and \(\hat{\mathbf{x}}_{i}\), respectively.
## 5 Quality Attribute Outcomes
### Privacy and Security
#### 5.1.1 Preventing Unauthorized Data Modification  
A blockchain's immutability ensures that once data is recorded, it cannot be modified or tampered with. This characteristic is essential for preventing unauthorized data alterations in sensitive applications. For instance, [PERSON] et al. [17] discuss how the SmartWalk  
\begin{table}
\begin{tabular}{l l} \hline
**Quality Attribute** & **Feature** \\ \hline Privacy and security & \\  & Preventing unauthorized data modification: [14, 16, 17, 18, 19, 21] \\  & Data immutability and traceability: [17, 22] \\  & Decentralized data storage: [16, 26, 27] \\  & Smart contracts and access control: [16, 26, 31] \\  & Encryption and anonymity: [24] \\  & Combined security impact of blockchain features: [16, 26] \\ \hline Data integrity, trustworthiness, transparency, and accountability & \\  & Data integrity and trustworthiness: [17] \\  & Transparency and accountability: [22] \\  & Combined Impact of integrity, transparency, and accountability: [26] \\ \hline Gamification, adherence, and personalization & \\  & Gamification and adherence in health and fitness: [24, 26] \\  & Personalization: [17, 24, 26, 31] \\ \hline Extensibility & \\  & Cost-effectiveness: [17, 24, 26] \\  & Scalability: [13, 24, 31] \\  & Automation: [16] \\ \hline \end{tabular}
\end{table}
Table 5: Quality attribute outcomes of using blockchain in PARFSET.  
system for older adults uses a blockchain to log activity data in a tamper-proof manner, ensuring healthcare providers can trust the data's authenticity and rely on it for making personalized care decisions.  
[PERSON] et al. [16] developed an IoT framework that integrates a blockchain network, utilizing Hyperledger Composer, to manage the streaming of fitness data from wearable IoT sensors. This blockchain-based solution ensures the integrity and confidentiality of the fitness data. The authors also analyzed the framework against common security threats, demonstrating that some of its strength comes from the inherent features of blockchain technology. For example, the framework is resilient against false data injection attacks because the consensus mechanism in a blockchain prevents unauthorized insertion of data blocks [16].  
Figure 4: Quality attribute outcomes of using a blockchain in PARFSET.  
[PERSON] and [PERSON] [21] have developed a blockchain-based system called B-PEIS, which securely stores student physical fitness data during training and evaluation. This ensures privacy while using data to enhance performance through tailored physical education plans [21].
# Impacts of Lightning-Induced Overvoltage on a Hybrid Solar PV-Battery Energy Storage System
## 4 EMTP-RV Model
### Lightning-Induced Overvoltage Model  
Generally, lightning-induced overvoltage is generated when the lightning strike does not directly strike any parts of the electrical network, but rather travels over the network or system. The return stroke phase can also cause induced voltage, which is responsible for many power outages. The most frequently used models to calculate lightning-induced voltage are the engineering models, which can be expressed based on a few coupling models, as listed below [61; 62; 63]:  
1. Rusck model;
2. [PERSON] model;
3. [PERSON] model;
4. Rachidi model;
5. [PERSON] model.  
In this study, the Rusck model was applied because of its simplicity and mathematically correctness, in addition to being consistent with experimental results [64]. The Rusck  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lightning Waveshape** & **Rise Time, \(\tau_{1}\) (\(\upmu\)s)** & **Fall Time, \(\tau_{2}\) (\(\upmu\)s)** & **n** \\ \hline
10/350 \(\upmu\)s & 1.65 & 490 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: List of parameters current waveforms [56; 60].  
Figure 9: Lightning waveshapes (10/350 \(\upmu\)s) at lightning current amplitudes of 3, 19, and 169 kA.  
model was validated with standard IEEE Standard 1410-2004 [64] and modeled using Equation (2):  
\[\mathrm{V(x,t)}=\frac{\zeta_{0}\mathrm{I_{0}h}}{4\pi}\beta\left(\frac{\mathrm{ct- x}}{\mathrm{d}^{2}+\beta^{2}(\mathrm{ct-x})^{2}}\left(1+\frac{\mathrm{x}+\beta^{2}( \mathrm{ct-x})}{\sqrt{\left(\beta\mathrm{ct}\right)^{2}+\frac{\mathrm{x}^{2}+ \mathrm{d}^{2}}{\gamma^{2}}}}\right)+\frac{\mathrm{ct+x}}{\mathrm{d}^{2}+\beta ^{2}(\mathrm{ct+x})^{2}}\left(1+\frac{\mathrm{x}+\beta^{2}(\mathrm{ct+x})}{ \sqrt{\left(\beta\mathrm{ct}\right)^{2}+\frac{\mathrm{x}^{2}+\mathrm{d}^{2}}{ \gamma^{2}}}}\right)\right) \tag{2}\]  
where \(\beta=\mathrm{v/c}\) is the ratio between the return stroke velocity and the light speed, \(\zeta_{0}\) is 376.730313 \(\Omega\) (free space characteristic impedance), \(\mathrm{I_{0}}\) is the return stroke current, \(\mathrm{d}\) is the horizontal distance between the lightning channel and the system, \(\mathrm{x}\) is the point nearest to the lightning strike, \(\mathrm{h}\) is the system height, and \(\gamma\) is equal to \(1/\surd\)(1-\(\beta^{\circ}\)2). The graph pattern for lightning-induced overvoltage at 19 kA is shown in Figure 10.
## 2 Data and Methodology
### Methodology  
The technical methodology of the study, illustrated in Figure 3, encompasses the following steps: (1) conducting a field survey in conjunction with Sentinel-2 NDVI curves to characterize the growth dynamics of garlic and winter wheat; (2) utilizing Sentinel-2 NDVI time series data to extract crops during the winter season within the study area employing random forests; (3) identifying a specific time point within the vegetation growth cycle and plotting time series curves using two different vegetation indices before and after the identified time point, subsequently stitching them together to generate comprehensive crop time series data; (4) formulating diverse combinations of classification features based on time series data from primary passive remote sensing bands or indices, employing random forests to reclassify winter crops using several different feature combinations, and validating the results and accuracy; (5) conducting a comparative analysis of the classification accuracy and outcomes for each combination of classification features and determining the optimal combination of features; and (6) utilizing the constructed garlic fragmentation index to assess the fragmentation of the classification results in each region. These steps represent a systematic approach to enhance the accuracy and reliability of winter crop classification, with a particular focus on garlic, within the study area.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 3 Satellite Sensors for SUHI Studies
### Aster  
In the reviewed studies, ASTER images have been the third most frequently-used data (7%). Such a small number arises from the fact that ASTER imagery had been costly from 1999 (Launch date)to April 2016 even though, since then, ASTER imagery has been available to all users at no cost. ASTER collects data in 14 spectral bands and the images have spatial resolution of 15 m (visible and near infrared bands 1-3), 30 m (shortwave infrared bands 4-9), and 90 m (thermal infrared bands 10-14). Similar to MODIS, the ASTER team also provides a variety of products [80] such as Surface Kinetic Temperature (AST_08), which provides the LST of the Earth's surface with an accuracy of 1.5 Kelvin [81, 82]. It is noteworthy that this product is available for daytime and nighttime and, thereby, provides a significant opportunity to study diurnal variation of SUHI [82]. Past researchers also derived LST from different thermal bands of ASTER for various types of SUHI studies [83, 84, 85]. However, the wide application of ASTER imagery to SUHI research was largely impeded by high cost and a limited data archive compared to other sensors like Landsat and MODIS [86]. Taking into account that ASTER data are now free, it can be expected that the use of ASTER imagery for SUHI studies will increase in the coming years. Note that almost 6% of past studies used imagery from multiple sensors to compensate for drawbacks of any given sensor's image.
## 6 Applications  
Five boundary treatments are considered here, and the performances are compared. All the normal boundary particles and ghost particles are included in the Poisson's equation, which means they are all assigned with certain pressure values. The additive boundary particles are not involved in any of the computations. The five different schemes are illustrated and explained in the following.  
Boundary treatment 1: additive boundary particles with dynamic dummy particles, as illustrated in Figure 6 (dummy particles are not really created so they cannot be visually seen but they are actually checked and involved in neighboring calculations, as described in Section 4.3).  
Boundary treatment 2: ghost particles only, as shown in Figure 7. In this treatment, the pressure of ghost particles is obtained by solving the Poisson's equation and these pressure forces could affect the velocities of the inner fluid particles so no penetration would be expected.  
Figure 5: Flowchart of the ISPH program.  
Figure 6: Sketch of boundary treatment 1: additive boundary particles.  
Boundary treatment 3: ghost particles and repulsive force. The model is the same, as shown in Figure 7. Meanwhile, the repulsive force from the first layer of the normal boundary particles is added to the inner fluid particles in addition to the pressure forces.  
Boundary treatment 4: uniform particle distribution with normal boundary particles is illustrated in Figure 8. Only one layer of normal boundary particles is arranged and the pressure values obtained by solving [PERSON]'s equation are added to the neighboring inner fluid particles, and these boundary particles are checked to determine whether they can contribute to the calculations of the neighboring inner fluid particles).  
Boundary treatment 5: uniform particle distribution with boundary pressure and repulsive force together. The model is the same as that shown in Figure 8. Both the pressure values and repulsive forces of the normal boundary particles are calculated and added to the neighboring inner fluid particles.  
To verify the efficiency of the proposed boundary treatment, several case studies are carried out.
## 5 Discussions  
### The Correlation Between Fracture Linear Density and Thickness of Stratum  
There is a correlation between stratum thickness and fracture development. Analysis of the thickness and fracture linear density in tight sandstone from field profiles in the Chang 7 member reveals a negative correlation between fracture linear density and rock layer thickness (Figure 11). Specifically, an exponential relationship is observed between the exposed fracture linear density and rock layer thickness (Figure 12). As rock layer thickness increases, the linear density of fracture decreases. Conversely, a reduction in rock layer thickness corresponds to an increase in fracture linear density. Notably, when the layer thickness exceeds 5.2 m, the change in fracture linear density becomes gradual (Figure 12). However, when the thickness falls below 2.5 m, there is a pronounced variation in fracture linear density.  
The observed phenomenon can be attributed to the distribution of external forces across rock layers. In thicker rock layers, the external force is distributed over a larger area, resulting in lower stress at each point. This dispersion of stress reduces the likelihood of fracture formation. Conversely, thinner rock layers experience concentrated stress over a smaller area, making fractures more likely to develop. Therefore, under identical tectonic stress conditions, rock layers with similar lithology are more susceptible to fracture formation as their thickness decreases. Conversely, thicker rock layers tend to exhibit poorer fracture development.  
Figure 10: Conversion relationship between dynamic and static Poisson’s ratio.
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 4 Numerical Results
### Coverage Metrics
Thus, UAVs might provide the most benefits in large areas with relatively lax outage constraints, though, in small areas with a critical traffic flow, direct connection might be preferred, since there is not much difference in the studied metrics between a direct connection and terrestrial relays with a low number of antenna elements. Terrestrial relays with a large number of antenna elements might prove beneficial in scenarios where the area is not large enough for a UAV to achieve sufficient benefits to justify a higher number of antenna elements, but is sufficiently large for there to be enough of a difference between a direct connection and a terrestrial relay in terms of coverage.
## 3 Results and Discussion
### Literature Revision of Materials Applied for Cool Surfaces
#### 3.3.4 Ceramic Materials
Glazes present a good complementary component for cool tiles as glass-ceramic materials; they are fabricated through a controlled crystallization process for a desired microstructure. The incorporation of cool pigments in the composition of glazes yields the total reflective performance of the product up to 82.8% [66]. The results of an experimental study show that a tile coated with glass ceramic material induces 20% energy savings, compared to TiO\({}_{2}\)-based paints, and that it could be used for roofs and pavements [107]. The application of engobes enhances the solar reflectance as well, by up to 0.90 [95]. As an intermediate layer between the substrate and the glaze, it provides a high degree of adhesion while taking into account the convenience of all the coefficients of thermal expansion. The application of developed glazes and engobes enhances the solar reflective performance of tiles [108]. In recent studies, the development of cool ceramic coatings was strongly discussed in terms of the low-cost routes, the use of secondary materials, and the self-cleaning abilities [109]. In turn, research has been focused on enhancing the compositions of glass ceramic frits, opacifiers, and pigments to reach the optimal potential for NIR reflectance. The development of coatings has been oriented toward the use of dynamic coatings, such as the passive ones: photochromic and thermochromic coatings [110]. It is clear that the research field is an intersection of multiple complementary techniques and materials, which include pigments, glazes, and tiles [73; 111], and this creates a wide range of opportunities to attain the energy-saving potential of cool materials in future studies.
## 4 Exploratory Data Analysis
### Geographical View  
As outlined in Section 3, the dataset pertains to data breaches reported in the United States, and, therefore, the geographical analysis is restricted to this country.  
As noted in Figure 2, the States that experienced the highest number of reported breaches were California, with 79 breaches, and New York, with 75. Conversely, during the period covered by the dataset, there were no reported breaches in the following States: Hawaii, Mississippi, Montana, New Mexico, North Dakota, South Dakota, West Virginia, and Wyoming.  
Among the States where breaches occurred, Alaska, Iowa, Maine, and Vermont registered the fewest incidents, with only one occurrence each. The Maine exposure transpired in 2012 and targeted New York State Electric & Gas (NYSEG) and Rochester Gas and Electric (RG&E), subsidiaries of Iberdrola USA. This breach resulted in the disclosure of 5100 records containing Social Security numbers, dates of birth, and financial institution account numbers [48]. In response to the breach, the company later offered a credit-monitoring-assistance membership ([[https://oag.ca.gov/ecrime/database/reports/sb24-22146](https://oag.ca.gov/ecrime/database/reports/sb24-22146)]([https://oag.ca.gov/ecrime/database/reports/sb24-22146](https://oag.ca.gov/ecrime/database/reports/sb24-22146)) accessed on 7 April 2024).  
Consistent with the choropleth map shown in Figure 2, the distribution of breach types among the 10 most affected States, as depicted in Figure 3, was predominantly led by California and New York. This chart also shows similar breach-type proportions in the States. It is remarked, however, that while hacking breaches represented a significant share of exposures in these States, New Jersey had only one reported case of this type.  
This case was the Heartland Payment Systems data breach that took place in 2009. This attack was orchestrated by international hackers and involved a U.S. Secret Service informant who returned to his criminal life as a hacker and exploited an SQL injection vulnerability [49]. The company waited a year to announce the disclosure of the credit card information of 130 million customers, causing a drop in its stock price by almost 80% [50].  
Figure 2: Geographical distribution of count of reported breaches by occurrence State.