esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation  
The considered multi-beam LEO satellite covert communication scenario is shown in Figure 1, which includes \(I\) user equipments (UEs), a legitimate LEO satellite with altitude \(H\), and an eavesdropping GEO satellite with altitude \(H_{e}\). The set of UEs is denoted as \(\mathcal{I}=\{i|i=1,2,\ldots,I\}\). There are two types of UEs: the normal user equipments (NUEs), and the covert user equipments (CUEs) requiring covert data transmission. The set of CUEs is denoted as \(\mathcal{J}\subset\mathcal{I}\), \(\mathcal{J}=\{j|j=1,2,\ldots,J\}\). The LEO satellite makes resource scheduling decisions in a time-slotted fashion, where the length of each time slot is \(\delta\) and the set of time slots is denoted as \(\mathcal{T}=\{t|t=1,2,\ldots,T\}\). In order to meet the communication requirements of UEs and realize flexible multi-dimensional communication resource allocation, the LEO satellite employs the beam-hopping strategy to receive UE data. At each time slot, the LEO satellite illuminates certain beam areas. The set of beam areas is denoted as \(\mathcal{N}=\{n|n=1,2,\ldots,N\}\). The carrier frequency of transmitted signals is denoted as \(f\) and the system bandwidth is \(B\). Both CUEs and NUEs transmit uplink signals to the legitimate LEO satellite on different bands, and the set of bands is denoted as \(\mathcal{M}=\{m|m=1,2,\ldots,M\}\). At time slot \(t\), UE \(i\) selects a power level from the set \(\{P_{k}|k=1,2,\ldots,K\}\) on each band for data transmission.  
Figure 1: Our considered scenario.  
In our proposed scenario, the eavesdropping GEO satellite aims to detect whether there exists covert data from its received signals. The CUEs aim to transmit covert uplink data to the legitimate LEO satellite while ensuring that the wireless signals are not detected by the eavesdropping GEO satellite.
# Hyperspectral Python: HypPy
## 3 Application Areas
### Consulting  
We explored the viability of employing laboratory-based short-wave infrared (SWIR) hyperspectral imagery for a commercial partner to identify and map the mineralogical composition of specific sedimentary drill core samples from the subsurface of the Netherlands. The scripting capabilities of HypPy proved essential in this project.  
Our findings indicate that utilizing laboratory-based short-wave infrared (SWIR) hyperspectral images with a spatial sampling of 200 um offers a valuable non-destructive approach for identifying and mapping white mica, clay, and carbonate minerals in sedimentary drill cores. Together with partners at the University of Groningen, using HypPy we succeeded in building a prototype processing chain for the quantification of the mineral content and deriving a number of geophysical properties of the drill cores.
# LDPC Coded Massive MIMO Systems
## 4 Design of LDPC Coded Massive MIMO System
\[\bar{I}_{VE}=1\text{ after running Algorithm \ref{eq:Lpcc},}\] \[\lambda_{2}\leq 2\sum_{d_{c}=2}^{d_{c,\max}}\rho_{d_{c}}/d_{c}, \tag{26}\] \[\sum_{d_{c}=2}^{d_{c,\max}}\rho_{d_{c}}=\sum_{d_{v}=2}^{d_{v,\max }}\lambda_{d_{v}}=1\quad\text{with}\quad\rho_{d_{c}},\lambda_{d_{v}}\geq 0,\]  
where the first constraint guarantees the convergence of JDD and the second constraint is used to satisfy Criterion (c) introduced above. The lowest \(E_{b}/N_{0}\), at which there exist \(N_{dec}\) such that the maximum \(R(\lambda,\boldsymbol{\rho})\) exceeds the target rate, is called the threshold and denoted by \((E_{b}/N_{0})^{*}\). Degree distributions and \(N_{dec}\) resulting in the lowest threshold are determined as optimal parameters of LDPC codes and the JDD strategy, respectively. Then, we construct the parity check matrix of LDPC codes based on optimally determined degree distributions by using the PEG algorithm.
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
## 2 Split-And-Delay Unit for XUV Interferometry
Partial beams from the two gratings propagate collinearly in every diffraction order, which is justified by the complete destructive interference in the zeroth order for \(\Delta\phi=\pi\). Spatial selection of a single order for detection allows for high-contrast interferometric measurement. the order of several percent [32]. An alternative approach using reflective optics was suggested by [PERSON] and [PERSON] in their lamellar-grating interferometer developed for Fourier spectrometry in the far-infrared [17]. The authors overcame the problem of the two-element split mirror--generation of two spatially separated beams--by using two interleaved multi-mirror arrays (lamellar gratings) shown in Figure 1b. Each grating represents a sequence of narrow rectangular facets separated by gaps wide enough to house the facets of another grating. When interleaved, the gratings form a sequence of alternating lamellae with neighboring elements belonging to different gratings. Optical performance of the lamellar-grating beam splitter was thoroughly investigated [17; 21; 34; 35]. In short, each grating diffracts the incident beam in a number of diffraction orders. The partial beams from both gratings propagate collinearly in every order which ensures that their interference depends only on the longitudinal offset between the gratings. This property makes the lamellar-grating SDU a reflective analog of a Michelson interferometer. As an example, for a \(\pi\) phase delay, diffraction from each lamellar grating results in constructive interference in odd orders and destructive interference in even orders. Upon focusing, the angular distribution of the diffracted light translates into a sequence of spots in the focal plane separated by:  
\[\Delta r=\frac{\lambda f}{d}, \tag{1}\]  
where \(d\) is the grating period, \(f\) is the focal length of the focusing optics and \(\lambda\) is the mean wavelength of the incident light. The intensity of the zeroth order recorded as a function of grating displacement yields the interferogram of the light source. Provided that \(\Delta r\) is larger than the focus size and can be resolved by the detection system, a single order can be selected for the signal accumulation. Compared to a transmission grating setup, the lamellar-grating SDU has fewer optical elements and offers higher efficiency.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results
### PER. Throughput, and Delay Performance When \(N_{u}=3\)  
Figures 10 and 11 show respectively the PER and throughput performance of D-NOMA-HARQ when \(N_{u}=3\). We show the performance variation with different code rates \(R\). As can be seen in Figure 10, when the code rate is high, the PER performance is poor in low SNRs and reduces slowly in the medium and high SNRs. The corresponding throughput is also poor in low SNRs and increase gradually when \(R=0.7\). This is because when the number of users are increased, each user experiences relatively higher interference. However, by reducing the rate, i.e., \(R=0.6\) and \(R=0.5\), the early saturation of PER can be avoided. This results in higher throughput in low SNRs. Therefore, with increasing the number of users with D-NOMA-HARQ, proper code rates should be chosen to achieve the target PER and throughput for all users.  
Figure 8: Packet error rate performance of each user with different \(k\) for each user at setting \(w_{1}=0.6\), \(w_{2}=0.4\) with CC-HARQ when \(m=1\), \(\alpha_{1}=0.5\), \(\alpha_{2}=0.4\), for packet lengths \(n=100\) and SNR = 4 dB.  
Figure 9: Throughput variation with different \(k\) for each user at setting \(w_{1}=0.6\), (\(w_{2}=1-w_{1}\)) with CC-HARQ when \(m=1\), \(\alpha_{1}=0.5\), \(\alpha_{2}=0.4\), for packet lengths \(n=100\) and SNR = 4 dB.
## 3 Methodology
### Dataset Pre-Processing & Normalization  
Pre-processing involves the standard mechanism to organize the data in a usable form, such as gratuitous spaces and eliminating non-value entries. The N-BaloT dataset is pre-processed to achieve great utility through the sklearn pre-processing label encoder. The deep learning algorithm solely reckoned on numeric values; the sklearn label encoder has converted all non-numeric values to numeric entities. Dataset normalization converts all numeric columns to the same scale without changing the range of values. It is only necessary to normalize datasets with a wide range of values. Minimax Scalar is used to normalize the N-BaloT dataset, which is generally scaled to a predetermined range between zero and one. The suggested model performs better and yields more valuable results with a normalized dataset.
## 3 Results and Discussion
### DFT Investigations
#### 3.2.2 MEP Map  
The molecular electrostatic potential (MEP) offers a vivid depiction of a molecule's electrostatic properties, projecting its potential energy onto a surface defined by constant electron density. This mapping allows us to visualize essential features of the molecule, such as its size, shape, and specific electrostatic potential values. MEP provides insights into key molecular characteristics like dipole moments, electronegativity, partial charges, and reactive sites, making it an invaluable tool for interpreting a molecule's polarity. The regions with negative electrostatic potential indicate areas where the molecule's concentrated electron density attracts protons, while the positive regions highlight areas where proton repulsion occurs due to a lower electron density and insufficient shielding of nuclear charges. By examining this contrast, MEP illustrates the distribution of electron density and its influence on molecular interactions. The electron density isosurface, a critical concept here, is essentially a boundary on which the molecule's electron density has a specific value, enclosing a defined fraction of its electron probability density. This surface aids in accurately representing the molecule's spatial electron distribution, thereby enhancing our understanding of its chemical behavior and reactivity [39; 40]. In this visualization, a gradient of colors represents the electrostatic potential across molecular surfaces. Each color corresponds to distinct regions: red, orange, yellow, green, and blue, with a progression that reflects the increasing potential. Specifically, the red areas denote zones of maximum negative potential, while the blue regions highlight areas of maximum positive potential.  
Figure 3: The proposed SQ (**5**) geometry applying the DFT/B3 LYP/6-311g (d,p) level.  
At the midpoint, green indicates areas of neutral or zero potential [41]. This color-coded approach offers a clear, intuitive way to understand the distribution of electrostatic forces within a molecule, aiding in the analysis of its chemical properties and interactions. The blue-colored regions on the surface imply an electrostatic potential that supports nucleophilic reactions, while the red areas highlight a favorable environment for electrophilic reactions [42]. To identify potential reactive sites for both electrophilic and nucleophilic attacks in the SQ (**5**), the MEP surface was generated based on its optimized geometry. This map was achieved using the B3 LYP/6-31 lg (d,p) computational method, and the resulting MEP distribution is shown in Figure 4. In the SQ compound, a red-colored surface surrounds the O1, O2, and O3 atoms, highlighting their vulnerability to electrophilic attack. In contrast, a blue region appears around the H1 atom, signaling its potential for nucleophilic interaction.
## 4 Spatial Distribution of Urban Vitality
### Spatial Clustering Characteristics of Urban Vitality  
The global Moran's I was utilized to measure the spatial correlation in urban vitality at subdistrict and block levels within central Qingdao (Figure 5). The results demonstrate that both scales exceeded the significance threshold (_p_ < 0.001), with Moran's I indices of 0.564 at the subdistrict scale and 0.679 at the block scale, indicating significant spatial aggregation of urban vitality at both levels, with aggregation more pronounced at the block scale.  
The spatial autocorrelation of urban vitality in Qingdao's central districts was analyzed using the Local Moran's I index, examining both subdistrict and block levels, and four distinct spatial patterns were identified (Figure 6), with an overall distribution characterized by a \"strong center and weak periphery\" pattern. At the block scale, several new \"high-high\" distribution areas emerged, including Chengyang Wanda Plaza, Huangdao Jiajia Yuan, Licang Wanda Plaza and Laoshan Jinshi Plaza. Specifically, 5.15% of areas at the subdistrict scale and 17.8% at the block scale fell into the \"high-high\" spatial state. These high urban vitality areas exerted a positive influence, elevating the vitality of surrounding regions. In terms of scale, only the \"high-high\" clusters in Shinn and Shibei Districts were relatively large, whereas the proportion of areas exhibiting the \"low-low\" clustering pattern increased to 27.6% at the subdistrict level and 48.6% at the block level, predominantly concentrated in the northern parts of Huangdao and Chengyang Districts, where diminished vitality values adversely affected adjacent blocks. This analysis demonstrates that the clustering of high and low vitality values markedly affects the vitality of neighboring areas. Overall, the proportion of areas with high vitality values at both scales is limited, indicating the necessity for further initiatives to stimulate new sources of vitality and enhance the overall dynamism of the city.  
Figure 5: [PERSON]’s I index of urban vitality in central Qingdao.
## 4 Discussion
For all mineral elements, the Ca\({}^{2+}\) content in the fruit increased most significantly after the NO treatment. Ca is a key regulator of thickness and mechanical strength of the cell wall, which determine the softness and slagging of the fruit [61]. Demethylated homogalacturonan (HGA) combines with Ca\({}^{2+}\) to form pectate calcium, to improve cell wall resistance to decomposition and increase mechanical firmness [62]. Earlier studies found that low concentrations of Ca\({}^{2+}\) made the cell wall softer and more prone to rupture, whilehigh concentrations made the cell wall stiffer and less plastic [61]. NO elevated the activity of H\({}^{+}\)-ATP (adenosine triphosphate) in the plasma membrane and hence increased the uptake of Ca in roots and shoots under environmental stress [62; 63]. Thus, the improved fruit firmness by NO treatment found in this study may be associated with the increased Ca content in blood orange pulp. Citrus fruit pigment formation is closely related to anthocyanin accumulation. In this study, NO treatment significantly increased the content of total flavonoids and anthocyanin in the pulp and upregulated the activity of key enzymes of the anthocyanin synthesis pathway (CHS, CHI, DFR, and ANS) to promote anthocyanin accumulation [64]. It has been confirmed that anthocyanin synthetases (ANS) were important links in the formation of colored anthocyanins from colorless anthocyanins [65]. The upregulation of genes encoding CHS, CHI, DFR, and ANS has been found to promote anthocyanin accumulation in strawberry fruits [64]. Moreover, NO could increase ROS accumulation and induce anthocyanin biosynthesis subsequently [66; 67]. Limonene is a ubiquitous monocyclic monoterpene essential oil in plants with a special aroma resembling that of lemon [68]. Monoterpenes form isopentenyl pyrophosphate (IPP), the central precursor of terpenoids, and dimethylallyl pyrophosphate (DMAPP) after catalysis by the MEP (2-C-methyl-D-erythmitol4-phosphate) and DXP (1-deoxy-D-xylulose 5-phosphate) pathways from acetyl coenzyme A, pyruvate acid, and glyceraldehyde-3-phosphate [68]. IPP and DMAPP are condensed by geranyl diphosphate synthase (GPPS) to produce geranyl diphosphate (GPP) and neryl diphosphate (NPP), the precursors of monoterpenes, and NPP is catalyzed by citrate synthase to synthesize limonene monoterpenes. NPP is catalyzed by lemon synthase to synthesize monocyclic monoterpene limonene [68]. In this study, NO treatment increased the content of limonene in the peel and increased the aroma of the peel. Consistent with a previous report [12], NO inhibited the conversion of pyruvate to acetyl coenzyme A, which is a key step in the terpenoid biosynthesis pathway. Tannin is the main cause of astringency in fruit [69]. Our results show NO treatment reduced the content of tannin in the pulp and reduced astringency. Studies have found that polyphenol oxidase (PPO) and peroxidase (POD) could catalyze phenolic compounds to form quinones and concentrate tannins into brown-colored polymers. Exogenous NO treatment inhibited PPO and POD activities in fresh-cut chestnut pulp and delayed tannin accumulation [70].
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Methodology for Numerical Simulation  
#### 4.1.1 Numerical Integration Procedure  
Numerical studies of the hole-by-hole blasting and three-hole simultaneous blasting were carried out using the finite element method (FEM) with an explicit integration procedure. Massively parallel processing (MPP) LS-Dyna code, which uses explicit central difference time integration, was adopted. The dynamic equation solved during the calculations had the following form [26]:  
\[M\ddot{x}_{n}=F_{n}^{ext}-\dot{r}_{n}^{\text{int}}-C\dot{x}_{n} \tag{12}\]  
where \(M\) is the diagonal mass matrix; \(F_{n}^{ext}\) is the external and body forces; \(F_{n}^{\text{int}}\) is the stress divergence vector; \(C\) is the damping matrix; and \(x\), \(\dot{x}\), \(\ddot{x}\) are the nodal displacement, velocity, and acceleration vectors.  
Assuming that \(\dot{x}\approx\dot{x}_{n-1/2}\), Equation (12) was solved with a numerical integration of acceleration \(\ddot{x}_{n}\):  
\[\ddot{x}_{n}=M^{-1}(F_{n}^{ext}-C\dot{x}_{n-1/2}-F_{n}^{\text{int}}) \tag{13}\]  
Implementation of the central difference equations for velocity and displacement yielded the following relations:  
\[\ddot{x}_{n}=\frac{1}{\Delta t_{n}}(\dot{x}_{n+1/2}-\dot{x}_{n-1/2})\Rightarrow \dot{x}_{n+1/2}=\dot{x}_{n-1/2}+\Delta t_{n}\ddot{x}_{n} \tag{14}\]  
\[\dot{x}_{n+1/2}=\frac{1}{\Delta t_{n+1/2}}(x_{n+1}-x_{n})\Rightarrow x_{n+1}=x _{n}+\Delta t_{n+1/2}\dot{x}_{n+1/2} \tag{15}\]  
The main disadvantage of this method is that it is conditionally stable and requires a time step to be limited according to the Courant-Friedrichs-Lewy (CFL) stability condition:  
\[\Delta t=\frac{L_{E}}{Q+\sqrt{Q^{2}+c^{2}}} \tag{16}\]  
where \(Q\) is a function of the viscous coefficients. \(C_{0}\) and \(C_{1}\) is formulated as follows:  
\[Q=\begin{cases}C_{1}c+C_{0}L_{E}\big{|}\dot{e}_{\text{kk}}\big{|}&\text{for } \dot{e}_{\text{kk}}<0\\ 0&\text{for }\dot{e}_{\text{kk}}\geq 0\end{cases} \tag{17}\]  
where \(L_{E}=V_{E}/A_{E\text{max}}\) is the characteristic length of the element; \(V_{E}\) is the volume of the element; \(A_{E\text{max}}\) is the largest side of the element area; and \(c\) is the adiabatic speed of sound.
## 5 Conclusions  
In this study, a comprehensive approach was employed to characterize the urban heat island (UHI) effect. We conducted a thorough analysis of UHI intensity, considering temporal variations, using a decade-long dataset (2012-2021) and information from the study area using 3S technology and MODIS data (MYD11A2 and MCD12Q1). This analysis encompassed annual, seasonal, daytime, and nighttime assessments, providing a comprehensive understanding of the UHI phenomenon of Zhengzhou City. The following conclusions can be drawn from the collected data. (1). The findings of this study demonstrate a substantial increase in the UHI effects over the past decade, coinciding with the rapid pace of urbanization in the study area. (2). It is also observed that the heat island is more intense at nighttime in winter than during the daytime, which might be due to the prevalence of heating pipes and indoor heating in built-up areas. The highest UHI during daytime was 0.8 \({}^{\circ}\)C, and at nighttime, it was measured to be 1.7 \({}^{\circ}\)C. (3). According to the results, autumn was the season with the highest heat island intensity in Zhengzhou, followed by summer and spring. (4). Additionally, the built-up area of Zhengzhou has the highest surface temperature, mainly contributing to UHI, while the woodland and water areas have lower surface temperatures. The results of this study have significant implications for mitigating the UHI effect in the future. Strategies such as constructing ponds and pools, strengthening urban greening efforts, and implementing summer mitigation measures can effectively slow down the UHI effect. These measures should be considered in urban planning and management to promote a more sustainable urban environment.  
Conceptualization, L.D.; methodology, L.D.; software, L.D.; validation, L.D. and S.K.; formal analysis, L.D.; investigation, L.D.; resources, L.D.; data curation, L.D.; writing--original draft preparation, L.D.; writing--review and editing, L.D. and S.K.; visualization, L.D.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The corresponding authors can provide the data upon reasonable request.  
The authors declare no conflict of interest.