esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### System Architecture  
The goal of this research was to test the chosen data link in a real-life scenario with a UAV and to determine if this hardware can be used for the real-time streaming of high-resolution aerial images. Flight beyond line of sight (BLOS) was not tested. Although this is a likely scenario when searching for people in a mountainous terrain, a simple scenario for testing the data rate with a line of sight between the UAV and antenna was considered to be a necessary step towards presenting a solution that would not have such limitations (Figure 4). Possible architecture types for BLOS include multi-hop communication and multiple unmanned aerial vehicles [52; 53; 54; 55; 56].  
Another possibility that could be considered for more flat terrain is using a tethered UAV equipped with a data link modem at a location close to the IPP [57]. Tethered UAVs can be lifted up to 100 m altitude and maintain the position for an unlimited time since it is powered with an energy cable from the supply on the ground.  
Figure 4: The tested data link solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **UAV—Transmitter** & **GS—Receiver** \\ \hline P\({}_{TX}\) (dBm) & 30 & - \\ Antenna (dBi) & 2 & 14 \\ Attenuation in cables and connectors (dB) & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data for power budget link calculations.  
Other than the Microhard pMDDL2450 data link, the UAV was equipped with a 2\(\times\) omnidirectional 2-dbi antenna, and the antenna on the GS side was a directed panel 14-dbi MIMO 2.4-GHz antenna (Figure 5). A directional panel was used because directional antennas provide a much better performance in terms of communication range than omnidirectional antennas since they can concentrate radio signals in desired directions, enhancing the range of our end-to-end communication [58; 59]. The gain pattern chart of the directional antenna [60] is given in Figure 6.
## 4 Discussion
Depending on the metal chelated, the resultant complexes may display higher [27] or lower [28] antioxidant activity compared to quercetin itself, or may have even demonstrated prooxidant activity as in copper complexes [29]. Considering that colorectal adenocarcinoma cells are subjected to high oxidative stress and accumulate copper levels up to seven times higher than the surrounding non-tumorous epithelial cells, compounds capable of chelating this metal, including phenolic compounds, are being explored as selective agents for inducing cancer cell death through a copper-dependent pro-oxidant pathway [9]. Conversely, Cu\({}^{2+}\) serves as a potent stimulator of blood vessel proliferation, which can promote malignant angiogenesis and tumor growth [30]. Consequently, reducing copper levels through chelation may represent a therapeutic strategy through naturally occurring flavonoids. This study not only advances the scientific understanding of the biological effects of saffron leaf polyphenols but also introduces an innovative and efficient methodology for their extraction and characterization. It thus lays the fundamental foundation for transforming agricultural waste into high-value biomedical resources, thereby promoting sustainability and circularity within the saffron industry. This insight represents a pioneering contribution to the integration of circular bioeconomy principles into saffron production, opening new avenues for the development of complementary and environmentally friendly treatments for colorectal cancer with broad biotechnological applications. However, it is important to note that this is an initial exploratory and preliminary study with several limitations. First, the results are derived exclusively from in vitro experiments with a single colorectal cancer cell line (Caco-2), which do not reflect the biological complexity of actual tumor behavior. Second, the underlying mechanisms of action and the bioavailability of individual phenolic components remain unclear, requiring further purification, elucidation and investigation; as does the chemical variability among different saffron samples (heterogeneity, age, phenological changes, chemical biodegradability, etc.). Third, the lack of in vivo studies limits the ability to assess systemic effects, potential cytotoxic effects on healthy cells and the therapeutic relevance of the extracts. Finally, rigorous human clinical trials are essential to validate both their efficacy and safety before considering their incorporation into cancer prevention and treatment strategies.
## 1 Introduction  
The topic for the Alpbach Summer School 2010 in Austria was \"New Space Missions for Understanding Climate Change\". Early career scientists and engineers from many countries formed working groups to devise new space missions to tackle this challenging subject. Following the summer school, one mission concept was chosen for further development at a subsequent workshop in Obergurgl, the outcome of which is described in this paper. At the core of the mission chosen for further study was a novel active limb-sounding instrument, used as part of a multi-instrument measurement approach to observing three key climate change variables: water vapour, clouds and aerosols.  
Water vapour in the upper troposphere-lower stratosphere (UTLS) region has an important role in determining the atmospheric temperature profile and tropopause structure [1,2]. Observations are particularly challenging because of the low concentrations, and the poor vertical resolution of conventional passive nadir instruments in this region. However, this information is vital in general circulation models (GCMs) for the understanding and future prediction of the Earth's climate system. Aerosols and clouds, and their interactions, have also been identified as key climate change uncertainties, and observations of these in the UTLS are also limited [3].  
Currently, several ground-based systems use differential optical absorption spectroscopy (DOAS) to detect low concentrations of trace gases by comparing path-integrated measurements over a continuous range of wavelengths. We propose an analogous spaceborne system, looking through the limb of the atmosphere at a few discrete wavelengths to detect differential absorption due to water vapour. By combining such a limb-looking LiDAR with a nadir-looking range-resolved LiDAR, this mission would provide water vapour and aerosol measurements in the UTLS and above of unprecedented accuracy and resolution. The addition of an imaging spectrometer operating in the visible and infrared range allows cloud microphysical properties to be retrieved in the same location, enabling process studies of cloud-aerosol-climate interactions and the reduction of key uncertainties in climate prediction.  
A nadir water vapour LiDAR was proposed to ESA in 2002 as the WALES candidate Earth Explorer mission [4]. The instrument concept presented in this paper builds on the laser and nadir LiDAR system development begun for the WALES mission.  
Section 2 presents the scientific motivation and requirements for the new mission. Section 3 gives an overview of the mission concept, while Section 4 presents the preliminary performance calculations for the system. Section 5 presents the technical implementation in more detail and Section 6 outlines the main technical challenges associated with the concept.
# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
## 2 Multi-Algorithm Parallel Integration Model and Algorithm Prerequisites
### Algorithm Structure and Overall Idea Introduction  
There are two main things that affect the recognition effect of the iris recognition algorithm: the algorithm itself and iris quality. The interference factors for iris quality include the external environment of iris collection and the state of the collection person themselves. The external environment includes illumination, equipment parameters, whereas the state of the collection person includes the degree of head deflection, defocus, blinking, and constrained acquisition. Because the collection state at different times cannot be exactly the same, even in the same person the iris states at different shooting time experience a certain degree of change. Examples of eye images of the same person at different shooting times is shown in Figure 3.  
An example of the acquisition state change of the collection person being small and the external environment being stable is shown in Figure 4. The images with a steady change in constrained iris texture are taken of the same person.  
An example of the state of the collector changing (defocus, deflection) and the external environment being unsteady (illumination) is shown in Figure 5. The images with an unsteady change in constrained iris texture are taken of the same person.  
It can be seen from Figures 4 and 5 that, in the case of the same person's state being stable in the external environment, the iris texture image captured at different times is stable and the change is small; where conditions are opposite, the iris texture experiences a large change in state. The main influencing factors are defocus, deflection of human eyes, and external illumination.  
Therefore, the algorithm prerequisites set in this article are:  
1. Constrained iris (the iris of the recording instrument is unchanged and the shooting state is standard)  
2. The collection state of the collection person may change at different times (the appearance of the iris texture is affected by the defocus and positional offset)  
3. The external environment may change at different times (the presentation of the iris texture is affected by the intensity of the illumination)  
Figure 2: Multi-algorithm parallel integration model structure.
## 2 Methods and Materials
### Posterior Distribution and Estimation of the Worth Parameters  
By using the prior distributions given in Equations (8) and (9) along with likelihood function of Equation (7), the joint posterior distribution is deducted as below  
\[p(\theta_{1},\theta_{2},\ldots,\theta_{m}|\mathbf{w})=\frac{1}{k}\prod_{i<j=1}^{m} \theta_{i}^{d_{i}-1}\tau^{a_{1}-1}e^{-a_{2}\tau}p_{0,ij}^{w_{0,ij}}p_{i,ij}^{w _{ij}}p_{j,ij}^{n_{ij}-w_{1,ij}-w_{0,ij}}, \tag{10}\]  
where \(k=\int_{0}^{1}\int_{0}^{1-\theta_{1}}\ldots\int_{\theta_{m}=0}^{1-\sum_{i=1}^{ m-1}\theta_{i}}\prod_{i<j=1}^{m}\theta_{i}^{d_{i}-1}\tau^{a_{1}-1}e^{-a_{2} \tau}p_{0,ij}^{w_{0,ij}}p_{i,ij}^{w_{ij}}p_{j,ij}^{n_{ij}-w_{0,ij}-w_{0,ij}}\)\(d\theta_{m-1}\ldots d\theta_{2}d\theta_{1}\) and represents the normalizing constant. The deduction of marginal posterior distributions requires the resolve of complex integrations involved in the expression of joint posterior distribution. The objective is attained by the launch of Gibbs sampling--popular approach of Markov chain Monte Carlo methods [26]. In general, Gibbs sampling proceeds by assuming \(P(\underline{\theta},\underline{x})\) be the joint posterior density, where \(\underline{\theta}=(\theta_{1},\theta_{2},\ldots,\theta_{m})\). The conditional densities of worth parameters are then given by \(P(\theta_{1}\theta_{2},\theta_{3}\ldots,\theta_{m})\), \(P(\theta_{2}\theta_{1}\theta_{3}\ldots,\theta_{m})\ldots\)\(P(\theta_{m}\theta_{1},\theta_{2}\ldots,\theta_{m-1})\). The Gibbs sampler now initiates by assuming initial values upon worth parameters such as \(\left(\theta_{2}{}^{(0)},\theta_{3}{}^{(0)},\ldots,\theta_{m}{}^{(0)}\right)\) and conceptualizes the conditional distribution of \(\theta_{1}\) such that \(P\Big{(}\theta_{1}{}^{(1)}\mathrm{I}\theta_{2}{}^{(0)},\theta_{3}{}^{(0)}, \ldots,\theta_{m}{}^{(0)}\Big{)}\). The iterative procedure continues until the convergence occurs. For demonstration purposes, the expression for the marginal posterior distribution of worth parameter associated with \(m\prime th\) object, that is \(\theta_{m}\), is solved as follows  
\[p(\theta_{m}|\mathbf{w})=\frac{1}{k}\int_{\theta_{1}=0}^{1-\theta_{m}}\ldots\int_ {\theta_{m}=0}^{1-\sum_{i=1}^{m-2}\theta_{i}-\theta_{m}}\prod_{i<j=1}^{m} \theta_{i}^{d_{i}-1}\tau^{a_{1}-1}e^{-a_{2}\tau}p_{0,ij}^{w_{0,ij}}p_{i,ij}^{ w_{ij}}p_{j,ij}^{n_{ij}-w_{1,ij}-w_{0,ij}}d\theta_{m-1}\ldots d\theta_{1}, \tag{11}\]  
The \((1-\alpha)100\%\) credible intervals attached with \(\theta_{m}\), say \(C_{r}\), are obtained numerically by solving the given expression  
\[\int\ p(\theta_{k}:\mathbf{x},w,\tau)d\theta_{m}=1-\alpha, \tag{12}\]  
which is a subset of \(\theta_{m}\)/s parametric space. Figure 1 below presents the flow diagram summarizing the working of the proposed scheme along with algorithmic advancements.
## 1 Introduction
### Related Work
The motivations of the proposed method derive from the following three aspects:  
First and foremost, both patch-wise classification aiming to assign a label to a given region and pixel-wise classification aiming to provide a label for the central pixel of ROI neglect the shape and the size information. For instance, the micrinites are always small in size and the cutinites are very thin [18; 19]. In cases where the selected regions contain two or more groups/components of macerals, it directly affects the performance. The results of the previous methods based on fixed-size window are always observed with poor generalized ability. Second, due to the complex and heterogeneous nature of coal, the task for identifying macerals requires more parameters describing the shape, texture and morphology. Comprehensive features along with powerful machine learning techniques are required to detect the subtle differences between maceral groups/components. Last but not least, there is no publicly available software for identifying maceral groups/components, especially targeted for geologists without strong expertise in the machine learning and image processing domains. In addition, the existing methods focus on predicting the label for a given region or pixel, whereas they do not work in the identification of maceral components from the entire photomicrograph. To address the above-mentioned concerns, we propose a novel framework for autonomous coal macerals identification based on image segmentation and classification (MISC). A two-level coarse-to-fine clustering strategy is implemented for image segmentation, and random forest is employed to classify maceral components from the entire microscopic image. The main contributions of the proposed framework can primarily be broken down into three aspects. 1. Inspired by the distribution of maceral subgroups and the gray scale characteristics of maceral groups, we design a coarse-to-fine segmentation strategy to divide an entire photomicrograph into a number of discrete regions, providing the shape and the size information. Both coarse clustering and fine clustering are based on K-means, which is one of the most popular unsupervised image segmentation methods. 2. We extract the discriminative features from microscopic images, including geometric, grayscale and texture features, which are combined into a 172-element feature vector. A comprehensive feature combination for identification of maceral components, not limited to maceral groups, is proposed. In addition, we evaluate six kinds of machine learning classifiers, and the random forest provides the best performance with an average accuracy of 90.44%. 3. A publicly available tool, namely MISC, to identify macerals in microscopic images of coal is released. The software integrates the best segmentation and classification algorithms involved in this paper, and provides an AI-assisted autonomy algorithm for maceral components identification.
## 3 Results
### Comparison of Our Result with Previous Studies
#### 3.4.2 Gross Primary Productivity  
For a comparison of GPP, the GPP map of [PERSON] et al. [25], field data from [PERSON] et al. [32], and the GPP product from MODIS (MOD17A2 HGF, which are gap-filled 8 day sums of GPP acquired by the Terra satellite; here aggregated to 1 km\({}^{2}\) from annual averages for the period from the beginning of 2019 to the end of 2020 [35]) were analyzed (Figure 7). For all three studies, the GPP values between 20 and 30 occurred most frequently. However, the distribution from this study was closer to the MODIS-based distribution than the Rodig-based distribution. The values from the study of [PERSON] et al. [25] were mostly between 22 and 27 Mg C ha\({}^{-1}\) a\({}^{-1}\), those from MODIS ranged between 25 and 32 Mg C ha\({}^{-1}\) a\({}^{-1}\)  
Figure 6: Frequency distribution of aboveground biomass of this study, consisting of the combination of GEDI and FORMIND data and the aboveground biomass map from [PERSON] et al. [12], with a resolution of 1 km\({}^{2}\) for the Amazon Basin.  
and those from this study ranged between 22 and 30 Mg C ha\({}^{-1}\) a\({}^{-1}\). However, a wider range of GPP values was observed in this study (0-40 Mg C ha\({}^{-1}\) a\({}^{-1}\)) in comparison to MODIS product (values between 15 and 35). The data of [PERSON] et al. [32] showed higher values than the average GPP determined in this study, but this was reasonable because their amount of included forest inventory data was rather low, the forest plots were much smaller in size than the resolution of the our map (1 km\({}^{2}\)), and the locations of these forest plots were often in productive forests and not randomly selected [36].  
For a pixel-to-pixel comparison of GPP, the GPP map of this study was compared to those of [PERSON] et al. [25] and MODIS in the Amazon Basin (Figure S7a,b). It can be seen from both comparisons that the differences in the central Amazon Basin were rather small (\(\pm\)5 Mg C ha\({}^{-1}\) a\({}^{-1}\)). Only in the southern part and the Arc of Deforestation can larger deviations be observed (\(\pm\)10 Mg C ha\({}^{-1}\) a\({}^{-1}\)).
## 3 Predicted Results and Experimental Validation
### Chatter Stability Analysis and Experimental Validation  
According to the SDM presented in Section 2.5, the SLD for bull-nose end milling of a thin-walled cylinder is calculated, as demonstrated in Figure 10.  
The spindle speed varies within the interval from 2000 r/min to 8000 r/min. The axial depth of the cut varies within the interval from 0.0 mm to 2.0 mm. It can be found that there are three milling states: stable, critical, and unstable, which are distinguished by the stability boundary. The stable and unstable regions are below and above the stability boundary, respectively. The critical stable region is located near the stability boundary. For a depth of cut of 0.5 mm, the spindle speed within the intervals of 3204-4612 r/min and 5950-8000 r/min theoretically ensures stable milling. For a depth of cut of 1.0 mm, the spindle speed within the intervals of 3450-4372 r/min and 6369-8000 r/min theoretically ensures stable milling. However, the above ranges have to be contracted appropriately due to the critical stable region. In this paper, \(\pm\)100 r/min is used to determine the critical stable region. Then, the spindle speed intervals for stable milling with a 0.5 mm axial depth of cut are 3304-4512 r/min and 6050-8000 r/min; the spindle speed intervals for stable milling with a 1.0 mm axial depth of cut are 3550-4272 r/min and 6469-8000 r/min.  
For verifying the accuracy of the SLD, a group of bull-nose end milling tests are performed on a thin-walled cylinder, as demonstrated in Figure 9. The shared milling conditions are set as follows: the milling mode is down milling, the radial depth of cut is fixed at 6 mm, and the feed per tooth is fixed at 0.016 mm/tooth. Table 5 presents the remaining milling parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **Frequency** & **Damping** & **Stiffness** \\  & **(Hz)** & **(\%)** & **(N/m)** \\ \hline Cutter (\(x\)-axis) & 1768 & 2.6 & \(1.85\times 10^{8}\) \\ Cutter (\(y\)-axis) & 1753 & 2.3 & \(1.79\times 10^{8}\) \\ Workpiece (\(z\)-axis) & 794 & 3.6 & \(4.25\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modal parameters.  
Figure 10: Predicted SLD.  
Figure 12 illustrates the finished surfaces of the thin-walled cylinder that correspond to the milling parameters at points A, B, and C in Figure 10. It can be clearly seen that significant chatter marks appear in the unstable case; slight chatter waves appear in the critical stable case; while in the stable case, the finished surface is sleek and its roughness (Ra) is very low.  
Figure 11: Time-domain and frequency-domain analysis of acceleration signals at each point. (**a**) Acceleration at point A; (**b**) FFT for acceleration at point A; (**c**) Acceleration at point B; (**d**) FFT for acceleration at point B; (**e**) Acceleration at point C; (**f**) FFT for acceleration at point C.  
Figure 12: _Cont._
## 2 Methodologies for Parallel Numerical Computation
### Explicit Finite Element Time Integration  
The mathematical representation of the specific nonlinear coupling system under seismic excitation can be described as:  
\[M\ddot{S}+C\dot{S}+P^{int}=P^{ext} \tag{6}\]  
\[P^{\text{int}}=KS \tag{7}\]  
where \(\ddot{S}\), \(\dot{S}\) and \(S\) signify the generalized acceleration, velocity, and displacement vectors, respectively; \(K\), \(C\) and \(M\) mean the matrices of stiffness, damping and mass separately; \(P^{\text{int}}\) means the vector of internal resisting forces, and \(P^{ext}\) represents the vector of the external forces.  
The explicit central difference approach has been used to deal with the system equations. Assuming that \(\Delta t_{n-1}\) and \(\Delta t_{n}\) are variable time intervals, the variants updating at time \(t_{n}\) are given by:  
\[\ddot{S}\left(t_{n}\right)=M^{-1}\left[P^{ext}\left(t_{n}\right)-P^{int}\left( t_{n}\right)-C\dot{S}\left(t_{n-1/2}\right)\right] \tag{8}\]  
\[\dot{S}\left(t_{n+1/2}\right)=\dot{S}\left(t_{n-1/2}\right)+\frac{1}{2}\left( \Delta t_{n-1}+\Delta t_{n}\right)\ddot{S}\left(t_{n}\right) \tag{9}\]  
\[S\left(t_{n+1}\right)=S\left(t_{n}\right)+\Delta t_{n}\dot{S}\left(t_{n+1/2}\right) \tag{10}\]  
The explicit central difference algorithm is only stable under the Courant conditions [28]. This should be taken into consideration during the simulation.