esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Acquisition
### MODIS Data and Calculation of Vegetation Indices  
MODIS Land Products MOD09A1 for the years of 2000 to 2012 was downloaded from NASA ([[http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html)]([http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html)), accessed on 26 June 2017).  
Figure 1: The location of the study area.  
The dataset features 7 spectral bands in HDF format at a spatial resolution of 500 m, a time resolution of 8 days, and corrections for atmospheric, aerosol scattering, and cloudiness. In this study, 5 spectral bands were used to generate the spatial dataset for visible albedo (VA), enhanced vegetation index (EVI), and land surface water index (LSWI) with the equations [32] below:  
\[\mathrm{VA}=0.331\cdot\rho_{\mathrm{red}}+0.0424\cdot\rho_{\mathrm{blue}}+0.0246 \cdot\rho_{\mathrm{green}} \tag{1}\]  
\[\mathrm{EVI}=\frac{2.5\cdot(\rho_{\mathrm{mir}}-\rho_{\mathrm{red}})}{\rho_{ \mathrm{mir}}+(6\cdot\rho_{\mathrm{red}}-7.5\cdot\rho_{\mathrm{blue}})+1} \tag{2}\]  
\[\mathrm{LSWI}=\frac{\rho_{\mathrm{mir}}-\rho_{\mathrm{swir}}}{\rho_{\mathrm{ mir}}+\rho_{\mathrm{swir}}} \tag{3}\]  
where \(\rho_{\mathrm{red}}\), \(\rho_{\mathrm{blue}}\), \(\rho_{\mathrm{mir}}\), \(\rho_{\mathrm{green}}\), and \(\rho_{\mathrm{swir}}\) are the reflectance of the red (620-670 nm), blue (459-479 nm), near-infrared (841-876 nm), green (545-565 nm), and shortwave infrared (1628-1652 nm) bands, respectively.  
### MODIS Data and Calculation of Vegetation Indices  
Daily precipitation, temperature, sunshine duration, wind speed, and relative humidity were obtained from the China Meteorological Data Sharing Service System ([[http://cdc.cma.gov.cn](http://cdc.cma.gov.cn)]([http://cdc.cma.gov.cn](http://cdc.cma.gov.cn)), accessed on 12 November 2016) for the years of 2000 to 2012 at 17 meteorological stations in and surrounding the region. Daily solar radiation data were not available and were instead estimated with the Angstrom equation [33; 34], \(\mathrm{R_{a}}=\mathrm{R_{s}}\cdot(a+b\cdot\mathrm{n}/\mathrm{N})\), where \(\mathrm{R_{a}}\) and \(\mathrm{R_{s}}\) are the terrestrial surface and extraterrestrial radiation, and n and N are the observed and maximum possible daily sunshine duration, respectively. The coefficients \(a\) and \(b\) were set to 0.18 and 0.55, respectively, as suggested by [PERSON] et al. for the Loess Plateau [35]. Photosynthetically active radiation (PAR) was considered as half of the total radiation [18], and the units were converted to mol m\({}^{-2}\) using the conversion coefficient of 4.57 umol J\({}^{-1}\)[36].  
To match the time and spatial resolution of the MOD09A1 data, time series data representing the mean temperature and total PAR for each 8-day period were prepared year-by-year for 2000 to 2012, based on daily records from the 17 meteorological stations. Then, the data were interpolated sequentially to obtain the values for each grid at a size of 500 \(\times\) 500 m using the ordinary kriging approach.
## 1 Introduction
Section 5 presents the numerical results, and the conclusion of this paper is given in Section 6. Notation: The following notations are adopted throughout this paper. Boldface upper case letters refer to matrices, and boldface lower case letters denote column vectors. The transpose, conjugate transpose and inverse are represented by \((\cdot)^{T}\), \((\cdot)^{H}\), and \((\cdot)^{-1}\), respectively. \(\left\|\cdot\right\|\) and \(\left\|\cdot\right\|_{F}\) stand for the \(l_{2}\)-norm of vectors and the Frobenius norm of matrices, respectively. The expectation of a variable is noted by \(\mathbb{E}\{\cdot\}\). \(\odot\) and \(\otimes\) denote the Hadamard products and the Kronecker products of two matrices, respectively. We use \(\mathbf{A}=\text{diag}[a_{1},a_{2},\cdots,a_{N}]\), which represents an N-dimensional diagonal matrix with \(a_{1},a_{2},\cdots,a_{N}\) on the diagonal, and \(\mathbf{A}=\text{blkdiag}[\mathbf{A}_{1},\mathbf{A}_{2},\cdots,\mathbf{A}_{N}]\) represents a diagonal matrix where the diagonal entries are the same as the matrix \(\mathbf{A}_{1},\mathbf{A}_{2},\cdots,\mathbf{A}_{N}\). The normal distribution is denoted by \(\mathcal{N}\big{(}0,\sigma^{2}\big{)}\), where \(\sigma^{2}\) is the variance of the normal distribution. The ceil value of \(x\) is represented as \(\lceil x\rceil\).
# Review of Dew Point Evaporative Cooling Technology for Air Conditioning Applications
## 5 Discussion and Summary
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Region** & **Experiment** & **Numerical** & **Performance** & **Reference** \\ \hline (B) DPIEC (with heat and mass exchanger) & e.g., Beijing, China & no & yes & \(\begin{array}{c}\epsilon^{tWB}=125\%\\ \varepsilon^{tDP}=90\%\\ \text{CO}^{max}_{el}=40\end{array}\) & [28] \\ \hline (B) DPIEC and vapor compression system & North Italy & partial & yes & \(\begin{array}{c}37.6\%\text{ of energy}\\ \text{savings}\end{array}\) & [44] \\ \hline (B) DPIEC and vapor compression system & Beijing, China & no & yes & \(\begin{array}{c}38.2\%\text{ of energy}\\ \text{savings}\end{array}\) & [45] \\ \hline (C1) Solar desiccant cooling system & e.g., Buszehr, Iran & no & yes & \(\overline{\text{CO}P}_{th}=0.498\) & [51] \\ \hline (C2) Solid desiccant cooling system & Simulated range & yes & no & \(\text{CO}^{max}_{th}=0.85\) & [52] \\ \hline (C2) Solid desiccant cooling & e.g., Singapure & no & yes & \(\begin{array}{c}\text{SCOP}=2.8\\ \text{energy savings}\\ \text{43.5\%}\end{array}\) & [53] \\ \hline (C2) Solid desiccant cooling & United Arab Emirates & no & yes & \(\overline{\text{CO}P}_{th}=0.46\) & [56] \\ \hline (C1) Solar desiccant cooling system & Taxila, Pakistan & yes & no & \(\text{CO}^{max}_{th}=1.2\) & [57] \\ \hline (C3) Liquid desiccant cooling system and vapor compression system & Simulated & no & yes & \(\begin{array}{c}\text{Energy savings}\\ \text{22.4\% to 53.2\%}\\ \text{CO}^{max}_{el}=7.1\end{array}\) & [54] \\ \hline (C3) Liquid desiccant hybrid membrane cooling system & Simulated & yes & yes & \(\begin{array}{c}\varepsilon^{tWB}=122\%\\ \varepsilon^{tDP}=81\%\end{array}\) & [55] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Studies of systems using DPIEC in hot and humid regions.
## 3 Applications of VI-Based Inverters
### Microgrid  
VI control strategy can be implemented for the microgrid to reduce the voltage fluctuation on the AC and DC bus. A microgrid is defined as a small-scale power grid which operates independently or collaboratively with other small power grid. Various alternating current (AC) loads are connected through converters with varying frequency. Hence, VI-based inverters assist in the regulation of frequency by participating in the inertial response. The rural electrification process requires microgrid, remote standalone power system, and VI plays a crucial role to stabilize the standalone power system. It includes the power extraction from RES, home appliances, uninterruptible power supply (UPS),and other components which require an inverter. Similar topology can be implemented for data center, hospital, and any other place which requires a continuous and stable power supply. Residential microgrid and industrial power systems are the main application for VI-based inverters.
## 2 Related Works
### Progress in Heterogeneous Geological Data Integration  
In the field of geological data fusion, many studies focus on the integration of multi-source data and the quantification of uncertainty. [PERSON] et al. (2021) [24] integrated various geological data (such as stratigraphy, airborne magnetic surveys, and gravity measurements) into a 3D model, using a Bayesian inference framework and Markov Chain Monte Carlo (MCMC) sampling techniques to quantify model uncertainty. [PERSON] et al. (2023) [25] proposed a method for extracting unstructured data from geological reports through a geological dictionary and pattern matching. They constructed a 3D model based on these structured data, enabling the effective fusion of report data. [PERSON] et al. (2023) [26] introduced a point-surface data fusion method (PSDOF) based on optimal transport theory, combining geological survey points with remote sensing data to improve geological remote sensing interpretation (GRSI) accuracy. Research on data view construction primarily focuses on dynamic data presentation and interaction optimization, change detection and trend prediction, and personalized user navigation and display needs. The VAI framework proposed by [30] (2015) [27] combines visualization, analysis, and interaction to use the Storygraph tool to display spatial and temporal data, helping users filter and analyze dynamically. This approach addresses the interactivity issues in multidimensional data display. However, handling real-time updates and changes presents another challenge in data view construction. [31] [32] proposed a strategy to address change detection issues by automatically refreshing and using time-series functionality, helping users identify short-term and long-term data trends. [33] [34] optimized real-time data presentation and user navigation by constructing a multi-level big data view system with role-based design and providing rich interaction methods, emphasizing the adaptability between data presentation and user needs.  
Previous studies have demonstrated significant advances in integrating and quantifying uncertainty in multi-source geological data through techniques such as Bayesian inference, pattern matching, and point-surface data fusion based on optimal transport theory. These developments provide fresh insights into dynamic data presentation, change detection, and real-time updates. However, current research often focuses on specific data types or individual domains, failing to fully address the challenge of achieving real-time, interactive aggregation in large-scale heterogeneous data environments. Therefore, integrating data from multiple platforms and formats to achieve dynamic, real-time information aggregation while optimizing data visualization and analysis methods is crucial to supporting digital transformation and enabling reliable decision making in geological exploration.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 2 Related Works  
### Hash Algorithms  
In the subsection on hash algorithms, it is highlighted that hash algorithms play a critical role in the field of financial security, as they provide a foundation for data integrity and security (2017). An introduction to hash verification algorithms is offered, including their characteristics and applications in detecting financial attacks. Hash algorithms are functions that transform an input of arbitrary length (usually a file) into a fixed-length string, referred to as a hash value (2018). The main characteristic of a hash algorithm is its unidirectional nature, meaning that it is computationally infeasible to reverse-engineer the original input from the hash value. Furthermore, hash algorithms should satisfy the following important properties:  
1. **Determinism**: A hash algorithm must produce the same output for any given input. For example, for input \(x\), the output of the hash function, \(H\), should always be the fixed \(H(x)\).
2. **Efficient computation**: Computing the hash value \(H(x)\) should be quick for any input value \(x\).
3. **Collision resistance**: It should be extremely difficult to find two different inputs (\(x\) and \(y\)) that yield the same hash value, i.e., \(H(x)=H(y)\).  
**Avalanche effect**: A small change in the input value should cause a significant change in the hash value. In other words, if \(x\) and \(x^{\prime}\) differ slightly, then \(H(x)\) and \(H(x^{\prime})\) should be significantly different.  
This paper will focus on how these hash algorithms are applied to the secure processing of financial data, particularly in scenarios involving the integration with Merkle trees and the Transformer model. By combining the data integrity protection of hash algorithms with the efficient data processing capabilities of the Transformer model, the proposed Merkle-Transformer model aims to provide an innovative solution for the secure processing of financial data.
# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
## 4 Feature Engineering for Affection Recognition
### Feature Selection
#### 4.2.2 Mutual Information Feature Selection  
The feature selection introduced previously evaluates each feature independently. Thus, the selecting is unable to deal with redundant or jointly relevant features. To overcome this problem, we compare to a feature selector based on the idea introduced in [55]. This algorithm uses mutual information (MI) [56] to select non-redundant features for the classification task.  
In each round, the MI algorithm selects the feature that best explains the misclassified samples, and adds it to the feature set. This process continues until no further improvement in classification accuracy is achieved for 50 consecutive rounds. Therefore, the features are ranked according to the MI between the samples and labels, and the highest-ranked feature is added to the set of selected features. Then, a classifier is trained based on all samples and the selected features. All correctly classified samples are removed from the MI computation for the following round, and the algorithm repeats. Intuitively speaking, in each round, the algorithm finds the feature that explains the currently wrongly classified samples best,somewhat like the AdaBoost algorithm. In Algorithm 1, we show the pseudo-code of this feature selector. We highlight again that we train personal models, and thus the selected feature set is different among participants.  
```
1:Input: \(X\), \(y\) [training samples and labels]
2:Output: \(S\), \(m\) [selected feature set and trained model]
3:Initialize: \(S\leftarrow\emptyset\)
4:\(X^{*},y^{*}\gets X,y\)
5:\(criterion\leftarrow False\)
6:repeat
7:\(F_{max}\leftarrow\underset{F_{1}}{\operatorname{argmax}}\text{$MI(X^{*},y^{* })$}\)\(\triangleright\) determine feature with highest MI
8:\(S\gets F_{max}\)\(\triangleright\) add feature in selected feature set \(S\)
9:\(x_{cc},y_{cc}\gets cls(S^{\prime},y^{S})\)\(\triangleright\) get correctly classified (cc) samples using \(S\) feature set
10:\(X^{*}\gets X\backslash x_{cc}\); \(y^{*}\gets y\backslash y_{cc}\)\(\triangleright\) remove correctly classified samples from set
11: CheckStopCriteria()\(\triangleright\) at the end of each round, check stop criteria
12:untilcriterion\(\leftarrow\) True
```  
**Algorithm 1** Feature selection based on mutual information
## 3 Current Status of Research
### Other Forms of Optical Communication  
In 2016, [PERSON] et al. designed a wireless optical communication system for shipboard environments capable of transmitting real-time voice, data, and image information. The system was tested under different weather conditions, such as sunny days, cloudy days with light rain, and foggy days. It achieved a communication rate of 155 Mb/s and a distance of up to 8.5 km [18].  
In 2021, Changchun University of Science and Technology conducted a one-point-to-multipoint communication verification test. The airship in the test was at an altitude of 200 m and realized 2.5 Gbps of one-to-many high-speed laser communication over a distance of 2 km [19].  
Light and small UAV optical communication technology has become a rapidly developing field in recent years and covers a variety of communication forms, including air-to-air optical communication, air-to-ground optical communication, and some special optical communication forms, such as star-to-air optical communication and shipboard optical communication. In this paper, we provide an overview of the development of high-speed data transmission technology for lightweight and small UAV platforms, focusing on their classification according to the differences in their communication forms and introducing them in detail according to chronological order. At the same time, we pay close attention to the key parameter of communication rate to comprehensively analyze the current status and future development trend of optical communication technology for small and light UAVs.
## 4 Conclusions  
The use of the Dirichlet-to-Neumann (DtN) operator has been, here, shown to be a suitable way to efficiently evaluate the near-zone electromagnetic field in a scattering problem, when such a problem is numerically solved by means of differential formulations, discretized with the finite elements method. Indeed, thanks to the DtN operator, the numerical problem is cast in a bounded computational domain where the boundary conditions are imposed exactly, with analytical expressions. Specifically, in this paper the DtN map was provided with reference to a circular boundary, in terms of a series expansion of Henkel functions. Given its features, the proposed approach may be applied to scatterers with arbitrary shapes, topology, and material properties.  
The analyzed case-studies, referring to bundles of conducting wires with or without dielectrics, compare the DtN approach to the state-of-art absorbing boundary condition, i.e., the perfect matching layer (PML). For a given accuracy, the DtN solution requires a lower number of mesh nodes compared to the PML one: For instance, for a fixed maximum error of \(1\%\), the mesh nodes are reduced by a factor of \(5\). In addition, for a given number of mesh nodes, the conditioning number of the stiffness matrix associated to the DtN is definitely better than that related to the PML solution. Both these results lead to a lower computational cost.  
Figure 7: Case study 3 (cable bundle with dielectric). Condition number of the stiffness matrices associated to the two numerical solutions versus the number of mesh nodes.  
**Funding:** The work was partially supported by the Project: \"Smart Distributed Systems\", funded by MIUR, Italian Ministry of University and Research, under the program \"Dipartimenti di Eccellenza 2018-2022\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Material and Methods
### ECG Signal Preprocessing
#### 3.3.2 Powerline Interference  
A band reject filter is used to remove or attenuate the powerline interference present in the ECG signal. The band frequency is typically 50 Hz or 60 Hz, depending on the country. Therefore, the equation for a band reject filter for ECG signals can be written as:  
\[P(f)=\frac{1}{(1-2r\cos(\frac{2\pi f}{fs})+r^{2})} \tag{3}\]  
where:  
* \(r\) is the filter pole radius, which determines the notch frequency;
* \(f\) is the frequency in Hertz;
* \(fs\) is the sampling frequency in Hertz;
* \(\cos()\) is the cosine function.  
Using the Neurokit [48] tool, the powerline interference with a frequency cutoff value of 50 Hz and an order of 5 is removed.
## 3 Numerical Formulations for the Normalized Fokker-Planck Equation
### Discretization of the Model Equation
For the model equation, the BTCS implicit scheme, also known as the simple (Laasonen) implicit scheme, leads to the following formulation  
\[\frac{u_{j}^{n+1}-u_{j}^{n}}{\Delta t}=-v\frac{u_{j+1}^{n+1}-u_{j-1}^{n+1}}{2 \Delta x}+\rho\frac{u_{j+1}^{n+1}-2u_{j}^{n+1}+u_{j-1}^{n+1}}{\left(\Delta x \right)^{2}} \tag{29}\]  
or  
\[u_{j}^{n+1}+\frac{c}{2}(u_{j+1}^{n+1}-u_{j-1}^{n+1})-d(u_{j+1}^{n+1}-2u_{j}^{n +1}+u_{j-1}^{n+1})=u_{j}^{n} \tag{30}\]  
which can be rearranged as  
\[-(d+\frac{c}{2})u_{j-1}^{n+1}+(1+2d)u_{j}^{n+1}-(d-\frac{c}{2})u_{j+1}^{n+1}= u_{j}^{n} \tag{31}\]  
By considering all spatial nodes Equation (31) produces a tridiagonal system of equations that can be solved efficiently using the [PERSON] algorithm [17]. The CN scheme evaluates the spatial derivative at the average of the \(n\)th and \((n+1)\)-th time levels, i.e., at the \((n+1/2)\)-th time level. For the model equation, the CN scheme gives  
\[\frac{u_{j}^{n+1}-u_{j}^{n}}{\Delta t}=-0.5v\Bigg{(}\frac{u_{j+1}^{n}-u_{j-1}^ {n}}{2\Delta x}+\frac{u_{j+1}^{n+1}-u_{j-1}^{n+1}}{2\Delta x}\Bigg{)}+0.5p \Bigg{(}\frac{u_{j-1}^{n}-2u_{j}^{n}+u_{j+1}^{n}}{\left(\Delta x\right)^{2}}+ \frac{u_{j-1}^{n+1}-2u_{j}^{n+1}+u_{j+1}^{n+1}}{\left(\Delta x\right)^{2}} \Bigg{)} \tag{32}\]which can be rewritten as  
\[\frac{\partial u}{\partial t}+v\frac{\partial u}{\partial x}=0 \tag{24}\]0  
Equation (33) is consistent with Equation (25) with a truncation error of \(O[\left(\Delta t\right)^{2},\left(\Delta x\right)^{2}]\) since the Taylor expression is made about \((j,n+1/2)\), hence second-order accurate spatial differences are inserted into this algorithm. The CN method offers a considerable improvement over the FTCS and BTCS implicit schemes whose local truncation errors are only first-order accurate in time. A von Neumann stability analysis indicates that the CN scheme is unconditionally stable.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 5 Model Regression Results
### PGA Attenuation Relationship  
Figure 14 compares the horizontal PGA attenuation curves for different magnitudes, with solid lines representing the attenuation curves based on Model I and dashed lines (including dot-dashed lines and other line types) representing the attenuation curves based on Model II. Figure 14 infers that when the magnitude is \(M_{\text{S}}\leq 5.0\), the attenuation rate of Model I is noticeably faster than that of Model II.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Prediction Equation** & **Model** & **Standard Deviation** \\ \hline \multirow{2}{*}{PGA} & \(\text{lg}PGA=4.916+0.867M-4.085\text{lg}\big{(}R+9.669e^{0.303M}\big{)}\) & I & 0.457 \\ \cline{2-4}  & \(\text{lg}PGA=-1.399+1.186M+(0.468-0.422M)\text{lg}\big{(}R+2.290e^{0.373M} \big{)}\) & II & 0.342 \\ \hline \multirow{2}{*}{EPA} & \(\text{lg}EPA=4.797+0.873M-4.119\text{lg}\big{(}R+9.217e^{0.303M}\big{)}\) & I & 0.493 \\ \cline{2-4}  & \(\text{lg}EPA=-2.443+1.476M+(0.921-0.554M)\text{lg}\big{(}R+3.650e^{0.350M} \big{)}\) & II & 0.357 \\ \hline \multirow{2}{*}{PGV} & \(\text{lg}PGV=3.451+0.573M-3.152\text{lg}\big{(}R+20.0e^{0.141M}\big{)}\) & I & 0.345 \\ \cline{2-4}  & \(\text{lg}PGV=-3.123+1.693M+(0.654-0.642M)\text{lg}\big{(}R+20.0e^{0.148M} \big{)}\) & II & 0.329 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Horizontal ground motion peak prediction equations for the Loess Plateau region.  
Figure 13: Comparison of PGA attenuation curves and PGA observed values.  
The ground motion parameters derived from strong motion record data analysis, such as PGA, are called \"observed values\". Meanwhile, those calculated through ground motion parameter prediction equations are termed \"predicted values\". Figure 15 selects two sets of observed values with average magnitudes of \(M_{\rm S}\) 4.2 and \(M_{\rm S}\) 5.0 for comparison against the attenuation curves. Figure 15 reveals few observed values at epicentral distances \(R\) < 10 km, especially within R < 4 km, indicating a lack of sufficient samples near the epicenter to effectively control the epicentral fitting accuracy of the prediction equations. In regions with epicentral distances R > 50 km, the observed values for average magnitudes of \(M_{\rm S}\) 5.0 and \(M_{\rm S}\) 4.2 mix, indicating a high dispersion in PGA observed values, which do not increase significantly with increasing magnitude.  
Figure 14: Comparison of PGA attenuation curves between Model I and Model II.  
Figure 15: Comparison of PGA prediction curves with observed values.