esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Methods
#### 2.3.2 Calculation of the VDI  
Prior to calculating the VDI, this study preprocessed the MODIS 16-day 250 m resolution EVI data and CHIRPS high-resolution global rainfall data. To ensure that the data were at the same 250 m resolution, considering that the spatial resolution of the rainfall data was 0.05\({}^{\circ}\), ArcGIS spatial interpolation functionality was employed for data processing. Specifically, this study utilized the kriging interpolation method to interpolate the rainfall data to a resolution of 250 m. The purpose of this step is to maintain consistent spatial resolution in subsequent calculations, enabling comparison and analysis of \(EVI_{max}\) and \(Precip_{cumulative}\) on the same scale. All data in the experiment were projected onto the Albers equal-area projection, and all data were resampled to a 250 m resolution.  
This study employed the VDI developed by [PERSON] et al. in 2018, which integrates MODIS EVI data with precipitation data to detect vegetation disturbances in the Lanxi urban cluster from 2001 to 2021 [60]. The calculation formula is Equation (5):  
\[VDI=\frac{EVI_{max}/Precip_{cumulative}}{\overline{EVI_{max}/Precip_{cumulative}}} \tag{5}\]  
The VDI is a pixel-level vegetation disturbance index, where \(EVI_{max}\) and \(Precip_{cumulative}\) denote the maximum EVI and cumulative precipitation, respectively, during the growing season (June to September) of 2021, and \(\overline{EVI_{max}}\) and \(\overline{Precip_{cumulative}}\) represent the multiyear averages of the maximum EVI and cumulative precipitation from 2001 to 2021. Notably, this study used EVI data from the growing season to represent annual vegetation activity because EVI data from the winter months may be inaccurate, potentially leading to false vegetation trends, in calculating the VDI, pixels with maximum EVI values less than 0.025 were removed because they typically represent land surfaces associated with water bodies and snow/ice [64]. When vegetation is disturbed, the \(EVI_{max}\) value for that year significantly increases or decreases, resulting in a substantial deviation from the multiyear average; hence, the \(EVI_{max}\) value was used to assess vegetation disturbance on the basis of this criterion.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy
### Recording Media  
The most common recording medium for SXCM is an X-ray resist in the form of thin (typically a few hundredths of a nanometer) layers of a photosensitive polymer on top of a silicon wafer or a glass slide. Depending on the type of photoresist, the radiation damages the molecular structure of the resist, decreasing the local molecular weight of the polymer chains, and making the resist susceptible to the chemical development procedure (positive photoresist, i.e., PMMA (poly(methyl methacrylate)) [37].  
Photoresists must ensure high resolution, preserve the cellular structure, and be suitable for cell imaging, ensuring cytocompatibility. Cells must strongly adhere to the resist. However, the most commonly used photoresist for biological applications is PMMA. Many resists are used in photolithography (e.g., DNQ-Novolac photoresist, epoxy-based polymer, hydrogen silsesquivacaine) but are not suitable for cell imaging. Epoxidized Novolac resist (EPR) was also tested in SXCM, but without success [38].  
Photoresists are \"permanent\" media, meaning that once recorded, the image of the object remains there for an extended period and can be digitized long after the exposure if the resist is properly preserved. This also means that the resist does not allow a refresh rate when trying to record changes in a sample. Resists require chemical development procedures but provide the highest spatial resolutions, which are mainly limited by digitization and equipment, such as SEM or AFM.  
Another approach is to use lithium fluoride crystals (LiF) as the recording medium. Different centers are produced in LiF crystals when they are exposed to ionizing radiation [39]. Their emission intensity changes locally and is proportional to their density, thus revealing the 2-D X-ray intensity map and the radiographic image of the biological sample, which is in contact with LiF crystal during the exposure to X-rays. The drawback of this recording medium is the need for sufficiently high X-ray fluence values for color formation to obtain high-quality (i.e., high resolution) SXCM images. The advantage is that the LiF does not require a development procedure; however, the digitization is carried out with a fluorescence microscope, which is much more limited by its spatial resolution than SEM or AFM microscopes.
# A Comprehensive Review of Micro UAV Charging Techniques
## 9 Future Research Directions  
Due to their diverse applications in military, remote sensing, traffic monitoring, disaster management, search and rescue, precision agriculture and parcel delivery, UAVs are currently receiving significant attention from both academic researchers and industrial experts. Because of these applications, and to meet extended mission requirements, UAV flight time, battery capacity and payload carrying capability must be improved. Among these, charging the UAV's battery is critical. Several wired and wireless charging technologies are currently being used to recharge UAVs. Still, the research community is looking for cost-effective, secure and quick ways to recharge UAVs. Contact-based charging, battery swapping, fuel cells and super capacitors are all employed. However, most of these techniques have operational difficulties, high requirements, physical assistance, serious concerns about human injury and electrical hazards during operation. Contactless charging mechanisms, such as wireless power transfer, LPT, DLC, SWIPT and SLIPT, have emerged as promising solutions in this scenario, ensuring the safety of both the human operator and the device equipment. AI, machine learning-based computer vision, smart sensors, Internet of Things (IoT) and integration with novel technologies such as intelligent reflecting surfaces (IRSs), will make UAV technology more efficient, reliable, secure and intelligent. UAV performance can be improved by using smart control mechanisms, self-learning algorithms, tracking and aiming techniques and energy and trajectory optimization. Future research should focus on cooperative algorithms based on multiple UAVs, secure transmission, power allocation design, physical layer (PHY) security in SWIPT-based UAVs, coverage gain via directional antennas and IRS-aided WPT for UAVs, among other topics. Future research will focus on mitigating solutions for attacks such as monitoring attacks, software attacks, interference attacks, safety attacks, spoofing attacks and jamming attacks [6]. In the future, researchers should focus on multi-UAV swarms, UAV-aided secure relaying with cooperative jamming, blockchain and mobile edge computing aided UAVs, cooperative communication with power splitting and MS-aided resource allocation for UAV swarms. In terms of charging, research should focus on pointing and tracking, misalignment, efficient battery management, dynamic charging, fuel cell-based UAVs, supercapacitor-based rapid charging, drone-to-drone charging, efficiency enhancement of PV cells, human safety concerns for laser-powered UAVs and novel charging coil designs.
## 2 Zinc Binding Groups in Chelation Therapy  
Zinc plays an essential role in living systems. It is one of the most abundant metals in the human body and it is common in the brain. Free zinc ions localize in glutamatergic nerve terminals where they are released upon terminal activation, besides they can act as antagonists of NMDA (N-methyl-D-aspartate) receptor [15]. Bound zinc has various roles, functioning as a cofactor in several proteins and enzymes. According to the classification as a \"borderline\" acid, Zn\({}^{2+}\) coordinates nitrogen, oxygen or sulfur of amino acid residues in protein zinc binding sites: nitrogen of histidine is the most common one, followed by sulfur of cysteine, oxygen of aspartate/glutamate, but also carbonyl oxygen of peptide bond, carbonyl oxygen of asparagine/glutamine, and hydroxyl of tyrosine [16]. Zinc is also  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acids** & **Bases** \\ \hline Hard & H\({}^{+}\), Li\({}^{+}\), Na\({}^{+}\), K\({}^{+}\), Be\({}^{2+}\), Mg\({}^{2+}\), Fe\({}^{3+}\), Ca\({}^{2+}\), Cr\({}^{2+}\), & F\({}^{-}\), OH\({}^{-}\), H\({}_{2}\)O, NH\({}_{3}\), CO\({}_{3}\)\({}^{2-}\), \\  & Cr\({}^{3+}\), Al\({}^{3+}\), SO\({}_{3}\), BF\({}_{3}\) & NO\({}_{3}\)\({}^{-}\), O\({}^{2-}\), SO\({}_{4}\)\({}^{-}\), CO\({}_{4}\)\({}^{-}\) \\ \hline Borderline & Fe\({}^{2+}\), Co\({}^{2+}\), Ni\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), Pb\({}^{2+}\), SO\({}_{2}\), BB\({}_{3}\) & NO\({}_{2}\)\({}^{-}\), SO\({}_{3}\)\({}^{2-}\), Br\({}^{-}\), N\({}_{3}\)\({}^{-}\), N\({}_{2}\), \\  & & C\({}_{6}\)H\({}_{5}\)N, SCN\({}^{-}\) \\ \hline Soft & Cu\({}^{+}\), Au\({}^{+}\), Ag\({}^{+}\), Tl\({}^{+}\), Hg\({}_{2}\)\({}^{2+}\), Pd\({}^{2+}\), Cd\({}^{2+}\), Pd\({}^{2+}\), & H\({}^{-}\), R\({}^{-}\), CN\({}^{-}\), CO, I\({}^{-}\), SCN\({}^{-}\), R\({}_{3}\)P, \\  & Hg\({}^{2+}\), BH\({}_{3}\) & C\({}_{6}\)H\({}_{5}\), R\({}_{2}\)S \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of Lewis acid and bases according to the Hard and Soft Acid and Bases (HSAB) theory.  
required in functions such as cellular proliferation, differentiation and apoptosis, and immunity and reproduction [17]. Considering the importance of zinc, the perturbation of its homeostasis because of genetic alterations or diet deficiency causes a series of diseases [18].  
Drugs containing a zinc binding group (ZBG) represent a way to treat disorders linked to zinc dyshomeostasis. This review focuses attention on recent ZBGs potentially useful for the treatment of Alzheimer's disease (AD), a neurodegenerative disorder, and for the inhibition of two zinc-enzymes, matrix metalloproteinases (MMPs) and carbonic anhydrases (CAs), involved in several pathologies.
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 1 Introduction
[17] tested the effects of different dry-wet duration ratios on concrete performance under sulfate or chloride attack, by taking the mechanical properties of concrete as evaluation indices. The results indicate that the degradation of concrete induced by attack increases and then decreases as the dry-wet ratio increases. Another research focus is simulating the actual environment to determine dry-wet cycle parameters. [PERSON]. [PERSON] et al. designed dry-wet cycles of 12, 24, and 48 h to imitate coastal environments, such as in Iran [18]. [PERSON] et al. simulated the sulfate attack environment in Western China with a dry-wet cycle of 24 h and dry-wet ratio of 1:1 [19]. In order to simulate the marine tidal environment, [PERSON] et al. used a 24-h representation of a dry-wet cycle, soaking samples for 18 h and then drying them in air for 0.5 h, followed by drying the samples at 60 \({}^{\circ}\)C for 5 h, before finally cooling them at room temperature for 0.5 h [20]. [PERSON] et al. used a 72-h dry-wet cycle and dry-wet ratios of 6:1, 9:1, and 14:1 to simulate alternating drying and wetting by the irregular, shallow, semi-diurnal tide at Lianyungang Port [21]. Comprehensive analysis showed that the main factors that need to be considered when testing dry-wet cycles are the drying and wetting duration, solution concentration, and temperature. In previous studies, most of the dry-wet cycle parameters were regarded as degradation conditions rather than objects of research. However, parameters such as the dry-wet cycle period have great influence on the deterioration of concrete under sulfate attack. To date, studies on the influence of the dry-wet cycle period on the long-term performance of concrete are insufficient for establishing a unified dry-wet cycle system standard, limiting the development and focus of research into concrete under sulfate attack. It is therefore crucial to take the dry-wet cycle period as the main factor in establishing a reasonable dry-wet cycle method. This should facilitate systematic experimental studies on concrete's degradation in performance under sulfate attack in different dry-wet cycle systems and on the mechanism underlying it. Using different dry-wet cycle periods, this study evaluated the characteristics of concrete damage through sulfate attack based on several aspects such as the flexural strength, relative dynamic elastic modulus, and mass change. Considering that sulfate attack products might decompose when the temperature is high, this study adopted the natural drying approach to avoid negative impacts from temperature on the dry-wet cycles [22, 23]. The microstructural characteristics of concrete before and after sulfate attack and the crystal formation and morphology of the erosive products were analyzed by scanning electron microscopy (SEM) to reveal the mechanisms of damage under different dry-wet cycle periods and sulfate attack.
# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 2 Theory
### Air Pollutants of Absorption and Scattering  
The air quality standards were created by the Environmental Protection Administration, [PERSON] through order no. 1010038913 on 14 May 2012, listing fine suspended particulates (PM10), suspended particulates (PM2.5), sulfur dioxide (SO2), nitrogen oxides (NO), carbon monoxide (CO), and ozone (o3) as the main pollutants [35].  
1. [label=1., ref=0]
2. Suspended particles (PM10): Suspended particles with a particle size <10 \(\upmu\)m can all be called PM10, and their optical effect is mainly Mie scattering, which is independent of the wavelength--that is, the same proportion of radiation absorption occurs at different wavelengths [36];
3. Fine suspended particles (PM2.5): The fine suspended particles among the suspended particles, with a particle size of 2.5 \(\upmu\)m, are referred to as PM2.5. The optical effect is Mie scattering, which is not affected by the wavelength, and the proportion of radiation absorption at different wavelengths is the same [36]:
4. Sulfur dioxide (SO2): SO2 has absorption characteristics in the infrared band, and the primary characteristic wavelengths are 18.9 \(\upmu\)m, 8.8 \(\upmu\)m and 7.6 \(\upmu\)m [37; 38];
5. Nitrogen oxides (NOx): NOx mainly includes nitrogen oxide (NO) and nitrogen dioxide (NO2), which also have absorption characteristics in the infrared band. The primary characteristic wavelength of NO is 5.3 \(\upmu\)m, while the primary characteristic wavelength of NO2 is 6.2 \(\upmu\)m and the secondary characteristic wavelengths are 7.5 \(\upmu\)m and 13.3 \(\upmu\)m, respectively [39; 40];Carbon monoxide (CO) has absorption characteristics only in the mid-infrared band in the wavelength of 4.6 \(\upmu\)m [37; 41];
* Ozone (O\({}_{3}\)): The primary absorption characteristic falls in the far-infrared band with a wavelength range between 9.3 \(\upmu\)m and 10.4 \(\upmu\)m. This band has two sets of absorption peaks at 9.5 \(\upmu\)m and 9.7 \(\upmu\)m. It also has absorption characteristics in the ultraviolet band with a wavelength of 254 nm [39; 41].
# Carbon Microstructures Synthesis in Low Temperature Plasma Generated by Microdischarges
## 4 Conclusions  
The experimental results of the synthesis of carbon microstructures in low temperature plasma generated by microdischarges were presented. The experiments were carried out in a plasma reactor, between needle and plate electrodes, a configuration typical for corona discharge. As an effect of the decomposition of cyclohexane in plasma, thin, 50-100 \(\upmu\)m in diameter, carbon microfibers were built at the tip of the needle electrode. Carbon nanowalls, carbon powder, soot particles and other structures were deposited onto these fibers. These various carbon structures, produced from cyclohexane vapors in high voltage, low-current electrical discharges at atmospheric pressure, were synthesized for different discharge currents, different electrode polarities, inter-electrode distances and cyclohexane concentrations. For 1% cyclohexane, the growth rate of the carbon deposit was very low compared to a higher concentration of this hydrocarbon, for the same discharge polarity and inter-electrode distance. The growth rate obtained for 3 and 5% cyclohexane, for 1.8 mA was similar, about 0.5 mm/s, but SEM investigations showed that the morphology of these deposits was different; they were semi grained and smooth surfaced, respectively. For an inter-electrode distance equal to and smaller than 4.55 mm, and a discharge current in the range of 1.4 to 1.8 mA, the growth rate was lower than for 15 mm, and the surface of the carbon deposits was covered by carbon nanowalls.  
The process of deposition can, however, be controlled by the discharge current, the residence time in the reactor (flow rate), the gas composition and the geometry of electrode. The optimal conditions can only be determined experimentally.  
Due to its porous structures, the produced carbon micro- and nanostructures have potential applications, for example, in the synthesis of electrodes for ion batteries, supercapacitors, fuel cells, gas sensor or catalyst carriers.  
Conceptualization, A.T.S. and A.J.; methodology, A.T.S.; validation, A.T.S. and A.J.; investigation, A.T.S.; writing--original draft preparation, A.T.S.; writing--review and editing, A.J.; supervision, A.J. Both authors have read and agreed to the published version of the manuscript.  
This research was funded internally by the Institute of Fluid Flow Machinery, Polish Academy of Sciences within the project No. O2/T3/Z4.  
Not applicable.  
Not supporting data.  
The authors wish to thank [PERSON] for her technical help and for carrying out the Raman measurements.  
The authors declare no conflict of interest.
# Shape Optimization of an Integrated Mast for RCS Reduction of a Stealth Naval Vessel
## 3 Analysis Results
(Unit: dBsm)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & **Elevation** & \multicolumn{2}{c}{**Initial Mast**} & \multicolumn{2}{c}{**Optimized Mast**} \\  & **Angle** & **HH** & **VV** & **HH** & **VV** \\ \hline \multirow{6}{*}{L-band} & 0\({}^{\circ}\) & 5.11 & 4.30 & 4.20 & \(-\)1.55 \\  & 3\({}^{\circ}\) & 11.78 & 10.52 & 3.34 & 0.12 \\  & 5\({}^{\circ}\) & 16.24 & 16.58 & 3.77 & 0.42 \\  & 7\({}^{\circ}\) & 18.33 & 17.73 & 7.58 & 6.74 \\  & 10\({}^{\  
same manner as the representative RCS value of the integrated mass, the geometric mean and the arithmetic mean of the naval ship equipped with each mast were calculated and are listed in Tables 3 and 4, respectively. The geometric mean and the arithmetic mean of the optimized mast decreased by a maximum of 24.05 dBsm and 34.83 dBsm and by an average of 10.42 dBsm and 9.97 dBsm, respectively, compared with those of the initial mast. As for the result of the integrated mast itself, there were a couple of cases where the representative RCS values of the optimized mast were larger than those of the initial mast but it could be seen that the RCS values of the optimized mast improved overall. From these results, it could be seen that the optimized mast had low RCS properties and minimally affected the RCS patterns of the naval vessel. As the geometric mean and arithmetic mean also might have had different results, we confirmed that the RCS characteristics should be evaluated comprehensively. Figure 10: RCS pattern of a naval vessel equipped with the integrated mast in the azimuth plane in the L-band. (**a**) HH polarization at an elevation angle of 0\({}^{\circ}\), (**b**) VV polarization at an elevation angle of 0\({}^{\circ}\), (**c**) HH polarization at an elevation angle of 10\({}^{\circ}\), (**d**) VV polarization at an elevation angle of 10\({}^{\circ}\). Figure 9: Geometry and dimension of the naval vessel. Figure 11: RCS pattern of a naval vessel equipped with the integrated mast in the azimuth plane in the S-band. (**a**) HH polarization at an elevation angle of 0\({}^{\circ}\), (**b**) VV polarization at an elevation angle of 0\({}^{\circ}\), (**c**) HH polarization at an elevation angle of 10\({}^{\circ}\), (**d**) VV polarization at an elevation angle of 10\({}^{\circ}\). Figure 12: RCS pattern of a naval vessel equipped with the integrated mast in the azimuth plane in the X-band. (**a**) HH polarization at an elevation angle of 0\({}^{\circ}\), (**b**) VV polarization at an elevation angle of 0\({}^{\circ}\), (**c**) HH polarization at an elevation angle of 10\({}^{\circ}\).
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 4 Experiments and Analysis  
### Datasets  
In this experiment, all the data used are real-world datasets. To evaluate the performance of the model, the study conducted validation on two datasets: TaxiNYC and BikeNYC.  
TaxiNYC dataset consists of taxi trip records in New York City, provided by the New York City Taxi and Limousine Commission. The experiment used a subset of the dataset from 1 July 2017 to 30 September 2017. It includes detailed information such as the pickup time and location, drop-off time and location, fare, passenger count, payment method, and more for each taxi trip.  
BikeNYC dataset is an open dataset provided by the NYC Transportation Department, containing data from a bike-sharing system. The experiment used a subset of the dataset from 1 October 2014 to 31 December 2014. The dataset includes information such as the start time, start and end locations, trip duration, distance, user type, and additional derived data such as latitude and longitude of each station, station names, capacity, and usage information.  
Since both datasets are collected in New York City, it is possible to use the same irregular spatial partitioning and Points of Interest (POI) [11] for both datasets. The total number of irregular partitions in this case is 82. As shown in Figure 4, the 82 irregular partitioned areas of New York City are illustrated.  
We mapped the mobility data from both datasets to the irregular regions representing the crowd movement. The sampling interval was set to 1 h, resulting in OD (Origin-Destination) flow as shown in Figure 1. There were a total of 26,202 Points of Interest (POIs) categorized into 9 types: Food, Residential, Shop and Service, University, Nightlife, Tourist and Transport, Arts and Entertainment, Professional Places, and Outdoor & Recreation.  
Figure 4: The irregular partitioning of New York City.  
Detecting and handling outliers is crucial to improve the quality and reliability of crowd flow data and reduce errors and uncertainties caused by outliers, thus providing better support for relevant decisions. In this study, we employed the 3-Sigma criterion to detect outliers in both datasets. According to the 3-Sigma criterion, a data point is considered an outlier if its value exceeds the mean plus or minus three times the standard deviation. The experimental results are presented in Figure 5a,b, showing the presence of outliers in both datasets. There are noticeable deviations from the average value in OD flow during certain periods such as severe weather conditions or weekends and holidays. For example, during severe weather, people tend to take taxis instead of walking or cycling, while on weekends and holidays, people prefer to ride bicycles or walk instead of taking taxis.