esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## 4 Discussion  
### SOS and EOS Determination  
#### 4.1.1 Derivative Analysis vs. TIMESAT  
As evident from matching the SOS and EOS dates from derivative analysis with those from TIMESAT, both the AG and DL smoothing filters performed similarly. This was not unexpected, since previous studies have shown similar behaviors for these two methods [82]. Further, the 5% threshold for the SOS agreed the most with the SOS dates that were derived from derivative analysis, while the 20% threshold displayed the highest agreement with the derivative analysis derived EOS dates. Although seasonality parameters are usually derived using symmetric thresholds, asymmetric thresholds may not be uncommon for AG or DL smoothing filters, as both smoothing functions demonstrated skewness.
## 4 Topology Optimization in Weight Reduction
### Optimization Results  
This study used a total of 12 different global mesh sizes, and the results are shown in Figure 14. The optimized mesh size and weight are shown in Table 3.  
Figure 14: Topology optimization results with different global mesh sizes.  
The optimization results obtained from the software show that with the increase in the number of meshes, the proportion of material that could be removed by the optimization increased. In the 0.3 mm mesh size, the optimized weight percentage was 50.26%; that is to say, 49.74% of the material was removed compared to the original weight of the component. With the refinement of the mesh during the topology optimization of the simulation, it could be seen that the region removed first was the red region shown in Figure 15. Then the blue region occurred and gradually expanded and merged to the red region. In further refinement, some of the material in the green region also started to be removed. It can be seen that the strength of the component with the safest region was the red region.
## 1 Introduction
Therefore, the use of remote sensing images in these models requires the subdivision of the original image into several square patches that require post-treatment to eliminate the mosaic effect [6; 13], which makes it difficult to develop automated detection and counting frameworks. To the best of our knowledge, few studies have addressed any methodology for aggregating the final result in a single scene. Through a modification of the Non-Maximum Suppression (NMS) method, [PERSON]. [28] proposed an approach to study with irrigated pivots, which differ from trees in agricultural plots as they have extremely regular shapes and no overlap. To integrate the results of identification of forest tree canopies, [PERSON]. [29] adopted their own method. Without much detail about the procedure, the authors suggest the union of any two crowns that overlap and are located at the ends of the clippings (patches), disregarding the possibility of real overlap between the trees. In the existing literature, few studies have reported the evaluation of instance segmentation in remote sensing images using only RGB imagery and photogrammetry DEM-based data for detection and delineation of dense treetops (touching/overlapping). The analysis carried out in this study aims (I) to contribute to the identification and counting of plants and the delimitation of their canopies, as well as (II) to increase the feasibility of the annotation process in studies involving canopies of contiguous trees in agricultural orchards, and also (III) to propose a method for automating the segmentation process of canopies with different planting densities for large scenes by mosaicking the results.
# Next-Generation Blockchain Technology:
## 4 Generating a Set of \(m\)-Blocks  
We already established that a new set constructed using \(m\)-blocks from a given set of \(N\) single characters, containing \(U\) distinct characters, will have \(\Omega=U^{m}\) distinct \(m\)-blocks/characters. We are now working out how many \(m\)-block elements will be in the newly formed set of \(m\)-blocks. Just as the \"windows\" segmentation of the set of \(N\) characters resulted in a new set containing \(N_{W}\) windows, the same set could be transformed into a new set of \(m\)-blocks containing \(N_{m}\) elements. The set of \(m\)-blocks is constructed using the same procedure used for generating the set of windows. However, instead of sliding the segment of WS from left to right, in step size (SS), where \(1<\text{SS}\leq\text{WS}\), we now slide the \(m\)-block segment from left to right in terms of SS, where the SS condition is now \(1<\text{ss}\leq m\). When applied to each window, the newly formed set of \(m\)-block elements within a window of size (WS) contains a number of characters given by  
\[N_{m}=1+\frac{\text{WS}-m}{\text{ss}}. \tag{10}\]  
It is important to observe that the values of \(m\) and ss are selected so that the sliding procedure produces a set of \(N_{m}\) integer elements. To clarify this procedure, let us again observe a few examples. Let us assume a random set of \(N=16\) single characters (this could be a window with \(\text{WS}=16\)) and U = 2:  
\[\{0110101101011100\}.\]  
Taking \(\text{ss}=1\) and \(m=2\), we generate the new set of \(m\)-block characters by sliding the \(m\)-block segment of two single characters from left to right in steps of 1. This results in the following set, containing \(N_{m}=15\) elements, as dictated by (10):  
\[\{01,11,10,01,10,01,11,10,01,10,01,11,10,00\}.\]  
Figure 1: Illustration of splitting a binary dataset into a set of windows using a given window step size (SS).  
Constructing another \(m\)-block set with \(\mathrm{ss}=1\) and \(m=3\), according to (10), we obtain the following set of \(N_{m}=14\) elements:  
\[\{011,110,101,010,101,011,110,101,010,101,011,111,110,100\}.\]  
Using this procedure, a new set of any \(m\)-block size, with \(m\leq\mathrm{WS}\), can be generated. For sets of single characters, the window size has to satisfy the condition \(\mathrm{WS}\geq U\) in order to ensure that the IE per window can take all possible values between zero and the maximum value theoretically permitted, \(\log_{b}(U)\). In the case of sets of \(m\)-blocks, to ensure that the IE per set can take all possible values between zero and the maximum value theoretically permitted, \(\log_{b}(U^{m})\), the new imposed condition is \(N_{m}\geq U^{m}\). Using (10) and solving for WS, we obtain the following:  
\[\mathrm{WS}\geq\mathrm{ss}\cdot U^{m}+m-1. \tag{11}\]
# Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques
## 1 Introduction  
Solar energy is an inexhaustible and less-polluting energy resource that has received widespread attention in renewable energy production. Photovoltaic power generation is an effective method for using solar energy [1]. In recent years, most research works have concentrated on extracting more power, which can be achieved effectively from PV cells [2]. In comparison to other renewable energy sources, solar PV is a natural energy source that is more efficient because it is free, clean, and abundant [3]. The energy consumption of the PV system has been reduced by effectively designing MPPT [4]. The nonlinear characteristic of PV cell output is not only affected by the temperature, but also by the load when the light intensity is fixed [5]. This paper presents some efficient ideas to improve the performance of MPPT. In the past few years, several MPPT techniques have been implemented, but still, some enhancements are required in MPPT to improve the PQ features.  
PV cells allow energy to be carried by electromagnetic waves, which have to be converted into electricity [6]. The MPPT technique implemented in the PV system creates full utilization of the PV array output power and it tracks the efficient MPP from the PV array input. The major challenge of the MPPT technique is automatically detecting the voltage or current MPP [7, 8]. Changes in the output voltage can impact MPPT's output characteristics [9]. However, the existing inverter levels provide some inaccuracy, because they require additional functionalities. The time required to predict GMPP strictly depends on the number of PV arrays and the complexity of the system design [9, 10].  
To overcome the above-discussed issues, the currently exploited novel optimization is required. Hence, a hybrid BPSO-GWO-based PV system that is connected to the grid is introduced to enhance the power quality (PQ) features. Maximum power transfer from the PV array to the grid is ensured using BPSO. The proposed MPPT technique provides highly efficient performance with a quick response under variable climate conditions, and in the case of a sudden variation in irradiance level. This research investigates a hybrid control scheme to grant multiple functions to a grid-connected PV inverter. This strategy guarantees a constant energy supply independently of the intermittent nature of solar energy. The main advantages of this proposed method are fast MPPT performance and less complexity than the Gauss-Newton, because double derivative terms are not present in the hybrid algorithm.  
The organization of this paper is as follows: Section 2 provides a literature review relevant to this work. The problem statement and its objectives are explained in Sections 3 and 4, respectively. Section 6 describes the workflow of the proposed method. Section 7 provides the results and discussions of the BPSO-GWO method. Finally, the conclusion is explained in Section 8.
## 2 Literature Review  
Many studies have focused on enhancing the hatching rate of fertilized eggs, including exploring the factors that affect the hatching rate and how to inspect the egg effectively by thermal imaging technology.  
First, regarding temperature, [PERSON] et al. [1] attempted to control machine temperature to maintain the eggshell temperature during incubation; they found that the eggshell temperature could vary independently from the machine temperature. [PERSON] et al. [2] further studied the effects of different eggshell temperature settings in one incubation (e.g., the low eggshell temperature at the start of incubation, and high eggshell temperature at the end of incubation) on hatchability. [PERSON] also suggested that maintaining the eggshell temperature at 37.8 \({}^{\circ}\)C from days 0 to 10 leads to the best hatchability. [PERSON] [6] noted that embryo development is affected by small changes in the eggshell temperature.  
Many scholars have attempted to explore other possible factors on hatchability, i.e., other than temperature. [PERSON] et al. [3] suggested that oxygen determines the embryo development (via heat production in the egg) rather than eggshell temperature. [PERSON] and [PERSON] [4; 5] found that low relative humidity or low O\({}_{2}\) concentration leads to the highest embryo mortality. In [10; 11; 12; 13], the researchers designed an automatic sprinkler cooling egg system to maintain a specific temperature, humidity, and concentration of CO\({}_{2}\) settings. In [14], the authors discuss the possibility of controlling the velocity and temperature of the airflow field in an incubator to increase hatchability.  
Many studies apply thermal imaging as a non-invasive and non-contact method of measuring the surface temperature of eggshells in order to eliminate the effect of human factors on egg incubation. [PERSON] [15] demonstrated several ways to use thermal imagers in poultry science, including physiological status monitoring, behavior research, and field survey on poultry. [PERSON] et al. [16] employed thermal imagery technology to filter fertilized chicken eggs. [PERSON] et al. [17] used thermal imaging to label the eggs and trace their development cycle.  
Figure 1: Black Muscovy duck (captured by this study).  
The works discussed above provide valuable knowledge on how to evaluate the egg, with what tools or technology, and explore factors that may affect the hatching rate. However, they do not provide a sufficient hatching prediction model to demonstrate what key factors affect the hatching rate and how to apply these factors to predict the hatching rate of fertilized eggs.
# Maize Replacement for Controlling Invasive _Eupatorium adenophorum_
###### Abstract  
Replacement control technology is a sustainable strategy for the control of invasive weeds. Two consecutive years of field experiments were conducted in Xichang city to assess the ecological and economical possibility of replacement control of _Eupatorium adenophorum_ (Spreng.) King & H.Rob. with maize. Four treatment groups were planted with maize at different densities after cutting _E. adenophorum_. Two reference groups were set by not treating and only cutting the aerial parts of _E. adenophorum_. All maize replacements in the \"after tillage\" treatments provided control effects of up to 100% and significantly reduced water and fertilizer use by _E. adenophorum_. Maize replacement provided a substantial economic benefit of up to 20,273.50 and 23,633.22 yuan/hm2 in two consecutive years and increased incomes. Evaluated in terms of niche theory, the large leaves as well as high plant cover assisted in effectively occupying the available niche and reduced light interception, seed germination and growth of _E. adenophorum_. This study provided a scientific basis for the sustainable and eco-friendly control of weeds by ecological methods.  
E. adenophorum; maize; replacement control; competitive effect; ecological method Article
## 2 Materials and Methods
### Spin Trapping Measurements Coupled with EPR Spectroscopy  
The evaluation of ROS generation under US stimulation was performed by Electron Paramagnetic Resonance (EPR) Spectroscopy through the EMX Nano X-Band Spectrometer from Brucker. The EPR measurements were assisted by a spin-trapping technique and the ROS production due to US stimulation was provided by a commercial apparatus (LipoZero G39 from GLOBUS).  
The sonication was performed by placing 1 mL of sample (200 ug/mL NPs concentration) in a 24-well plate from Thermo Scientific which was positioned in contact with the LipoZero transducer. A thin layer of water-based gel (Stosswellen Gel Bestelle from ELvation Medical GmbH) was added to maximize the coupling between the transducer and the plate. The conditions used to stimulate all samples were the following: sonication time 1 min, working frequency 1 MHz, duty cycle 100%, and US output powers of 0.3, 0.6, 0.9, and 1.2 W/cm\({}^{2}\) (corresponding to 10%, 20%, 30%, and 40% of the maximum output power of the device). After the US stimulation, the sample was immediately transferred into a quartz microcapillary tube and placed in the EPR spectrometer cavity. The production of hydroxyl and superoxide anion radicals was detected in bidistilled water and 5,5-dimethyl-L-pyrroline-N-oxide (DMPO, from Sigma), was used as a spin trap. Before each measurement, DMPO (from a stock solution of 10 mM) was added to each tested sample. The spin trap is essential due to its ability to trap both radicals (hydroxyl and superoxide anion) increasing their lifetime and enabling their measurement by the EPR spectroscopy.  
The parameters to collect the EPR spectra were the following: center field 3426 G, sweep time 100.0 s, sample g-factor 2.000, and number of scans 10.  
After the acquisition, the spectra were processed through the Bruker Xenon software for baseline correction. Analysis of the recorded data was made using the Bruker SpinFit software.  
All EPR experiments were carried out under daylight conditions, except the assays designed to evaluate the eventual synergistic effect between visible light and US stimuli. In this case, output powers of 0.9 and 1.2 W/cm\({}^{2}\) of US irradiation were used.  
Note that no UV stimulation was performed to any of the a-TiO\({}_{2}\)-based NPs before any experiment. All EPR experiments were carried out in triplicate.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 2 Modelling the Perovskite Crystal Structure  
In perovskite solar cells MAPbI3, FAPbBr3, and CsPbCl3 are standard ABX3 light-absorbing materials [31]. The arrangement of ions in the perovskite crystal structure varies in space around a unit cell depending on temperature (T), pressure (P), and tolerance factor (t). The perovskite crystal structure ranges from orthorhombic, tetragonal, and cubic in order of increasing crystal perfection, respectively [32]. The tolerance factor (t) predicts stability in perovskite crystal systems. In a perfect perovskite cubic structure, the (t) factor equals 1.0, whereas in a non-ideal case, the cubic structure is within 0.8 to 1.0 (t) factor. Less ordered perovskite crystal structures, such as orthorhombic and tetragonal, are obtained at low (t) factor [19; 26]. FA+ containing perovskite materials have a high (t) factor. Alloys of MA+ and Cs+ multi-cation perovskites result in materials with properties comparable to FA+ alone. This concludes that FA+ containing perovskites are more stable than the individual MA+ and Cs+ containing perovskite materials [33]. Figure 2 shows MAPbI3 phase transitions with an increase in temperature. The (t) factor equation is given as follows.  
\[\mathrm{t}=\frac{\mathrm{r}_{\mathrm{A}}+\mathrm{r}_{\mathrm{X}}}{\sqrt{2 \times(\mathrm{r}_{\mathrm{B}}-\mathrm{r}_{\mathrm{X}})}} \tag{1}\]  
where \(\mathrm{r}_{\mathrm{A}}\), \(\mathrm{r}_{\mathrm{B}}\), and \(\mathrm{r}_{\mathrm{X}}\) represent the ionic radii for the organic cation A, inorganic cation B, and the halide anion X, respectively, from the ABX3 perovskite crystal structure [31].  
Figure 1: (**a**) Schematic representation of a solid-state perovskite sensitized device structure. SEM cross-sectional images of PSCs based on mesoporous (**b**) TiO2 and (**c**) Al2O3. Progress in PSC efficiency with the cell structure: (**d**) sensitized PSC \(\eta\) = 9.7%, (**e**) mesoporous PSC \(\eta\) = 10.9%, (**f**) meso-superstructure PSC \(\eta\) = 15%, and (**g**) planar heterojunction thin-film PSC \(\eta\) = 15.4% [9; 10; 17; 21].
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Catalysts as Supported Nanoparticles
#### 3.4.1 Oxide-Supported Nanoparticles  
Metallic nanoparticles dispersed on oxides are widely used for catalytic applications, and their characterization remains challenging. Due to a heterogeneous field of evaporation, the analysis of interfacial regions leads to trajectory aberrations, local magnifications and reconstruction artifacts [143]. Furthermore, it is quite common to observe a thin layer of oxide at the metal/metal-oxide interface due to field-induced oxygen migration: oxygen reacts with the metal and forms an additional interfacial oxide layer [144]. Correlative approaches are thus required to improve the compositional and spatial resolution of such samples [145].  
Model metallic nanoparticles dispersed in a metal oxide were studied, consisting of Au/MgO and Ag/MgO [146; 147]. The samples were synthesized by ion implantation and annealing [148], and APT specimens were prepared by the conventional lift-out method [149]. As expected, the analysis reveals the presence of Au nanoclusters, but diluted, i.e., with a maximum of \(\sim\)20% of Au within the cluster. Correlative approaches were thus needed to improve the compositional and spatial resolution. APT, STEM and finite element field evaporation modeling were used to analyze the Au/MgO system in detail [150]. APT-STEM reveals that the number of nanoparticles within the MgO matrix is similar between the two methods, but their shape appears distorted in the APT reconstruction (Figure 10a-c).  
Moreover, APT analysis yielded 1D concentration profiles in the vicinity of the nanoparticles in which the apparent extent of Au/MgO interdiffusion scaled with the size of the Au-rich regions, even though STEM results exhibit an abrupt Au/MgO interface (Figure 10d). The difference in fields of evaporation of Au and MgO leads to a preferential evaporation of MgO, which results in Au nanoparticles protruding from the hemispherical surface of the evaporating specimen. The local magnification (Figure 10e) due to the protruding Au generates trajectory overlaps and results in inaccurate composition and incorrect interface width measurements. Experimental parameters, such as the laser pulse energy, have to be optimized to obtain reliable data, for example detecting stoichiometric MgO ions rather than complex Mg\({}_{x}\)O\({}_{y}\), as well as to decrease background noise and to improve the sensitivity [146; 151].
## 2 Methods
### Electrophysiology Studies (Mapping and Arrhythmia Test)  
The day following the imaging studies at 9 weeks post-DOX, catheter-based electro-anatomic mapping (EAM) and an arrhythmia inducibility test were performed in pigs #3 and #4, respectively. An additional EAM study and arrhythmia test was performed in a healthy (control) pig. All animals were intubated and sedated, and anesthesia was maintained throughout these EAM procedures using the same combination of medications as those administered prior to the MR imaging scans. However, amiodarone was not administered, since one objectives of the EP study was to evaluate arrhythmia inducibility.  
All three interventional electrophysiology EP studies were carried out under X-ray guidance, using a C-arm Toshiba INFINIX VF-I/SP-S (Figure 4a). catheter-based EAM of the left ventricular (LV) endocardial surface was performed in these animals by employing a conventional CARTO3 electrophysiology system (Biosense Webster Inc, Diamond Bar, CA, USA). For the endocardial mapping procedures, we used a PentaRay(r) catheter (Biosense Webster Inc., Irvine, CA, USA) inserted into the LV cavity, via femoral access.  
The EAMs were primarily acquired for the purpose of constructing detailed bipolar voltage maps (i.e., more than one thousand points per map, in sinus rhythm and under pacing conditions). The bipolar voltages characterized by low amplitude values (0.1-1.5 mV) were attributed to patches of fibrotic tissue, whereas the areas with an amplitude voltage > 1.5 mV were considered normal, in accordance with the clinical threshold typically used to define scarred tissue [19]. The low bipolar voltage areas were qualitatively compared to those defined by LGE using ADAS 3D software, version 2.11.1-beta.2 (www.adas3D.com, accessed on 31 October 2022). Several representative points that had low voltage values (denoting fibrosis) were selected for a qualitative evaluation of the QRS and QRS-T intervals.  
For the arrhythmia inducibility test, we inserted an SF Thermocool catheter (Biosense Webster Inc., Irvine, CA, USA) into the right ventricle (RV) and performed rapid pacing of the heart. Figure 4b shows an exemplary X-ray image of the heart during the EP study (in pig #3), with the pacing catheter inside the RV and the mapping catheter (with 5 prongs  
Figure 4: (**a**) Experimental set-up of the EP study (electro-anatomic mapping and arrhythmia study) shown during the intervention in pig #3, at week 9 following the completion of DOX injections; and (**b**) X-ray image of the mapping and pacing catheters inside the heart during the EP study.  
and multiple sensors on each prong) inside the LV. The external ECGs placed onto the animal torso for reference, are also visible.