esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Variational Modal Decomposition  
Variational modal decomposition (VMD) is an adaptive signal decomposition method [21] that can effectively suppress the modal aliasing phenomenon that occurs in empirical modal decomposition (EMD) [22]. Unlike EMD, VMD determines each component's center frequency and bandwidth by iteratively searching for the optimal solution. VMD is based on a non-recursive method to process the original signal. The resulting IMFs are mutually distinct, preventing modal aliasing and resulting in better decomposition and a higher robustness. The decomposition process of VMD is the construction and solution process of a variational problem.  
Assuming that the original power series is \(f(t)\), decomposing the power into \(k\) components, the corresponding constrained variational model is  
\[\left\{\begin{array}{l}\min\limits_{\{u_{k}\},\{\omega_{k}\}} \sum\limits_{k=1}^{k}\left\|\partial_{t}[(\delta(t)+\frac{j}{\pi t})*u_{k}(k) ]e^{-j\omega_{k}t}\right\|_{2}^{2}\\ s.t.\sum\limits_{k=1}^{K}u_{k}=f(t)\end{array}\right. \tag{1}\]where \(\{u_{k}\}\), \(\{\omega_{k}\}\) is the set of central frequencies corresponding to the subsignals, \(k\) is the number of modes in the decomposition, \(t\) is the sampling moment, \(\partial_{t}\) is the Dirac function, \(*\) is the convolution operation, and \(f(t)\) is the original power sequence.  
To reconstruct the constrained variational problem as an unconstrained variational problem, the expression of the augmented Lagrangian function is  
\[\begin{split} L(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum \limits_{k=1}^{k}\left\|\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t} \Big{)}u_{k}(t)\Big{]}e^{-j\omega_{k}t}\right\|_{2}^{2}\\ +\|f(t)-\sum\limits_{k=1}^{k}u_{k}(t)\|_{2}^{2}+\lambda(t),f(t)- \sum\limits_{k=1}^{k}u_{k}(t)\end{split} \tag{2}\]  
where \(\lambda(t)\) is the Lagrange multiplier operator and \(\alpha\) is the quadratic penalty factor.  
By searching for the saddle points of the increasing Lagrangian function, it is alternatively possible to find the iterated \(u_{k}^{n+1}\), \(\omega_{k}^{n+1}\), and \(\lambda_{k}^{n+1}\), whose expressions are as follows:  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum\limits_{i\
eq k}\hat{u}_ {i}(\omega)+\hat{\lambda}(\omega)/2}{1+2\alpha(\omega-\omega_{k})^{2}} \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}\text{ d}\omega}{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}\text{d}\omega} \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\text{T}\Bigg{(}\hat{f} (\omega)-\sum\limits_{k}\hat{u}_{k}^{n+1}(\omega)\Bigg{)} \tag{5}\]  
where \(\hat{u}_{k}^{n+1}\) is the Wiener filter for each modal component and \(\omega_{k}^{n+1}\) is the frequency center of the corresponding modal component.  
\(k\) modal components are obtained when the iterative stop of Equation (6) is satisfied.  
\[\sum\limits_{k=1}^{k}\Big{(}\|u_{k}^{n+1}-u_{k}^{n}\|_{2}^{2}/\|u_{k}^{n}\|_{ 2}^{2}\Big{)}<\epsilon \tag{6}\]  
where \(\epsilon\) is the convergence progress.
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
## 3 Proposed Method
### Query for Information on the Location of Vehicle Traffic Accidents  
The location of a traffic accident can be extracted from the _\"Address\"_, _\"Latitude\"_, and _\"Longitude\"_ fields. The _\"Address\"_ field contains textual address information about the actual location of the traffic accident. The _\"Latitude\"_ and _\"Longitude\"_ fields include direct coordinates information \((x_{acc},y_{acc})\) about the location of the traffic accident. Geocoding is required to convert the address information in the _\"Address\"_ field into coordinates. Geocoding is a crucial part of text geoparsing, which involves several tasks. Specifically, it refers to converting the textual value of identified places into latitude and longitude coordinates on a map [28]. The following steps are performed to obtain coordinate values using geocoding: First, a search is made of various address databases of cities and buildings to find the most similar address. Then, the geocoding engine converts that address to latitude and longitude coordinate values. Therefore, in this paper, the traffic accident location \((x_{acc},y_{acc})\) was defined by directly using the _\"Latitude\"_ and _\"Longitude\"_ fields, which can be used directly without the need for a separate conversion process.
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
###### Abstract  
A general greening trend in the Arctic tundra biome has been indicated by satellite remote sensing data over recent decades. However, since 2011, there have been signs of browning trends in many parts of the region. Previous research on tundra greenness across the Arctic region has relied on the satellite-derived normalized difference vegetation index (NDVI). In this research, we initially used spatially downscaled solar-induced fluorescence (SIF) data to analyze the spatiotemporal variation of Arctic tundra greenness (2007-2013). The results derived from the SIF data were also compared with those from two NDVIs (the Global Inventory Modeling and Mapping Studies NDVI3g and MOD13Q1 NDVI), and the eddy-covariance (EC) observed gross primary production (GPP). It was found that most parts of the Arctic tundra below 75\({}^{\circ}\) N were browning (-0.0098 mW/m\({}^{2}\)/sr/nm/year, where sr is steradian and nm is nanometer) using SIF, whereas spatially and temporally heterogeneous trends (greening or browning) were obtained based on the two NDVI products. This research has further demonstrated that SIF data can provide an alternative direct proxy for Arctic tundra greenness.  
Article tundra; solar-induced fluorescence data; greenness +
Footnote †: journal:
## 4 Results and Discussion
Since the blue-shift of wavelength, together with the broadening of FWHM, originates from the carrier filling effect of localized states [16; 30], the wider and faster increase of FWHM with current for green LEDs indicates a stronger carrier localization effect as compared to NUV and blue LEDs. Therefore, the wavelength-dependent efficiency droop phenomenon in nitride LEDs can be well interpreted in this way: For InGaN QWs with a low average indium composition the localization potential is relatively shallow and the probability of carriers escaping out of the local potential minima is expected to be high, resulting in a relatively larger effective active volume. On the contrary, for high indium content InGaN wells, the indium compositional spatial fluctuation becomes more prominent and carriers are strongly localized at the deep potential minima, leading to a more reduced effective active volume. Then, the corresponding higher carrier density increases the relative contribution of the Auger recombination rate and carrier leakage over radiative recombination rates, resulting in a lower onset-droop current density as well as an aggravation of the efficiency droop. Figure 5a,b shows the simulation results of normalized IQE versus current density with \(\beta\) varying from 0.05 to 1.0 and well thickness ranging from 3 to 15 nm. The values of parameters A, B, and C were set to be \(7\times 10^{6}\) s\({}^{-1}\), \(3\times 10^{-11}\) cm\({}^{3}\) s\({}^{-1}\), and \(2\times 10^{-30}\) cm\({}^{6}\) s\({}^{-1}\) respectively [16]. The IQE droop is obviously alleviated and the onset-droop moves towards high current density through increasing \(\beta\) or well thickness, which is due to a reduction of carrier density. This result is consistent with the previous report about maximum quantum efficiency above 200 A/cm\({}^{2}\) with a thick double heterostructure (DH) active region by [PERSON] et al. [21]. Nevertheless, the growth of a thick InGaN well is challenging for \(c\)-plane InGaN LEDs. With a thick well layer, the piezoelectric field strengthens, resulting in a stronger quantum confinement stark effect (QCSE), as well as a lower overlap of electron-hole wave-functions [31]. One of the biggest advantages of InGaN LEDs grown on semi- and non-polar planes is that the polarization-related electric fields can be significantly reduced, whereby thick InGaN wells can be achieved [32; 33]. By employing a thick well of 12 nm, InGaN blue LEDs with a high quantum efficiency and low efficiency droop at a high current density of 400 A/cm\({}^{2}\) on a semipolar (20-2-1) plane, this is realized by [PERSON] et al. [32]. These results indicate that manipulating active region volume and carrier density are promising methods to alleviate the efficiency droop. Figure 3: (**a**) QW In composition-dependent \(\beta\) and (**b**) carrier density at various current densities varied from 5 to 100 A/cm\({}^{2}\).
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
## 4 Verification of 3D Defocusing Coupling Model
### Data Analysis
Comparing the curves shown in Figure 12a,b, whether these samples are tested with Class B jumpers or Class C jumpers, the changing of optical power with 3D defocusing coupling is significantly smaller than that with 2D coupling. From the analysis and verification, it shows that the distribution of light in the core and cladding of fiber is decided by the coupling model of the TOSA, which eventually affects the performance of the TOSA. The 3D defocusing coupling model can effectively improve the performance of a TOSA. Figure 11: (**a**) is the distribution of four-direction data with 2D coupling model. (**b**) is the distribution of four-direction data with 3D defocusing coupling model. The horizontal axis represents the serial number of samples, and the vertical axis represents the power changing.
## 1 Introduction
The contributions of our work are summarized as follows:  
1. We provide a metaheuristic approach for optic disc segmentation in fundus images that uses the Grey Wolf Optimization (GWO) algorithm, which has not previously been deployed in literature for this purpose. Thanks to this approach, we achieved a high performance of 99.39% accuracy in optical disc segmentation;  
Figure 1: (**a**) Healthy image (normal optic nerve). (**b**) Glaucoma image (large optic cup). * Vision transformers, including the Swin Transformer, show robustness to variations in image quality and noise, which are common challenges in medical imaging. Because of this, we used a new application of vision transformers, specifically the state-of-the-art Swin Transformer, for glaucoma image classification, which has received little attention in previous research. Thanks to this method, we achieved high classification performance;
* We provide a thorough evaluation and comparison of two-stage and one-stage approaches for glaucoma classification. This evaluation provides valuable guidance for researchers working on glaucoma classification. These two approaches represent different strategies for glaucoma classification. The one-stage approach considers the entire image, potentially capturing broader contextual information but also including irrelevant features. On the other hand, the two-stage approach focuses specifically on the optic disc region, which is directly relevant to glaucoma diagnosis;
* By effectively addressing the problem of class imbalance in ORIGA and Drishti-GS, our experimental results were able to achieve good results in glaucoma detection and classification.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 3 The Proposed Analytical Model and Simulation Tool
### Formulated Analytical Models  
The collision probability and the success probability are analytically derived below, as a function of the actual number of users accessing an RAO, \(N\), and the number of available NPRACH subcarriers, \(s\). Let the _Collision Probability_, \(\mathcal{P}_{N,s}\), be the probability that, in a given RAO, a preamble collision happens. First, if no users access the channel, no collisions will occur. Instead, the probability \(P_{k}\) that \(k\) among \(N\) users choose the same subcarrier follows a binomial distribution. Thus, \(P_{k}=\binom{N}{k}p^{k}(1-p)^{N-k}\), where \(p\) is the probability of choosing a specific subcarrier. According to NB-IoT specification, each subcarrier is selected with the same probability, hence \(p=\frac{1}{s}\). If a preamble is chosen only once, that is \(k=1\), the probability that only one of the \(N\) users chooses the specific subcarrier is equal to:  
\[P_{1}=N\frac{1}{s}\left(1-\frac{1}{s}\right)^{N-1}. \tag{1}\]  
Let \(\widehat{N}\) be the number of users not colliding. It is indeed equal to the average number of preambles chosen only once. In fact, given Equation (1), \(\widehat{N}\) is computed as the product of \(P_{1}\) and the number of different preambles, \(s\):  
\[\widehat{N}=sP_{1}=N\left(1-\frac{1}{s}\right)^{N-1}. \tag{2}\]  
As a result, the average number of collided users, that is \(\widetilde{N}\), corresponds to the total amount of users, i.e., \(N\), except the users whose preambles not collide, i.e., \(\widehat{N}\), that is:  
\[\widetilde{N}=N-\widehat{N}=N\left[1-\left(1-\frac{1}{s}\right)^{N-1}\right]. \tag{3}\]  
Thus, the Collision Probability can be expressed as follows:  
\[\mathcal{P}_{N,s}=\frac{\widetilde{N}}{N}=1-\left(1-\frac{1}{s}\right)^{N-1}, \hskip 28.452756 ptN>0. \tag{4}\]  
Similarly, let the _Success Probability_ be the probability that, in a given RAO, a user successfully completes a preamble transmission. As previously stated, \(\widehat{N}\) is the number of users not colliding. Since \(\widehat{N}=N(1-1/s)^{N-1}\), as Equation (2) reports, \((1-1/s)^{N-1}\) clearly represents this success probability. Conversely, the success probability \(\mathcal{S}_{N,s}\) can be intuitively modeled as:  
\[\mathcal{S}_{N,s}=1-\mathcal{P}_{N,s}=\left(1-\frac{1}{s}\right)^{N-1}, \hskip 28.452756 ptN>0. \tag{5}\]  
Accordingly, it is worth noting that \(\mathcal{S}_{1,s}=1,\forall s>0\), as if a single user accesses an RAO, it will certainly not collide.  
It is important to emphasize that no stringent assumption on the traffic model has been made in the formulated analytical model. In fact, the probability of a device attempting a preamble transmission in a given RAO depends on both its traffic arrival distribution and NPRACH structure in time. These aspects can be easily managed by the simulation platform.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 3 Generative AI for Threat Detection
### Generative Diffusion Models (GDMs)  
Generative Diffusion Models are a class of probabilistic generative models that learn to model complex data distributions by simulating a two-step process inspired by non-equilibrium thermodynamics. First introduced by [PERSON] et al. in Ref. [65], GDMs involve gradually adding noise to data through a forward diffusion process until the data becomes pure noise and then learning a reverse diffusion process that denoises this noise step by step to reconstruct the original data. This framework allows for both high model flexibility and tractability, offering exact sampling and likelihood evaluation, which are significant advantages over traditional generative models.  
The architecture of GDMs is based on two main components: a forward diffusion process and a reverse generative process. The forward process incrementally adds noise to a data sample through a Markov chain, transforming it into a simple, known distribution such as Gaussian noise. The reverse process is then learned to invert this transformation, reconstructing data from noise. This is achieved by estimating small perturbations needed to reverse each diffusion step. The entire model becomes a sequence of denoising steps that, together, generate data samples [65]. A visual representation of this structure and workflow is shown in Figure 8.  
To better understand their operation, GDMs can be interpreted as models that learn to reverse a structured noise-injection process. Initially, data is gradually corrupted by noise until it becomes indistinguishable from pure randomness. The model is then trained to denoise this input incrementally, step by step, in order to reconstruct the original data. This forward-reverse dynamic enables GDMs to generate high-quality samples that closely follow the distribution of real data. Owing to their systematic design, GDMs are particularly effective at modeling intricate data patterns and producing diverse, realistic outputs.  
GDMs have emerged as a valuable approach for enhancing intrusion detection systems, particularly in identifying malicious activities or anomalies in network traffic. Their ability to model complex, high-dimensional data distributions makes them well suited for learning the normal behavior of network or system data. Once trained on clean (attack-free) data, the reverse diffusion process can be used to detect discrepancies between the generated(expected) data and real-time observations. If the input data significantly deviates from the learned distribution, it may indicate an attack or anomaly. Moreover, GDMs' probabilistic nature allows for the quantification of uncertainty, enabling robust detection even in the presence of noise or adversarial manipulations. As a result, GDMs serve as a powerful tool for modern security applications, including detecting novel or stealthy cyberattacks in dynamic and complex environments.
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes
### rpoB and rpoC1  
_rpoB_ (RNA polymerase subunit B) and _rpoC1_ (RNA polymerase subunit C1) are plastid genes, encoding subunits of the plastid-encoded plastid RNA polymerase that have been used for the identification at the family level but, due to their slow evolution rate, they cannot be used for species discrimination in many plant families [37; 45]. Both can be efficiently amplified with a limited range of PCR conditions and primer sets [37]. _rpoB_, _rpoC1_, _rpoC2_ encode three out of four subunits of the chloroplast RNA polymerase [49] and are suitable markers for bryophyte identification [50].
###### Abstract  
A novel modeling approach is developed for investigating the effectiveness of buckypaper (BP), a porous membrane made of a highly cross-linked network of carbon nanotubes, in improving the electromagnetic interference (EMI) shielding properties of carbon fiber-reinforced polymer (CFRP) composites. The methodology uses quantum tunneling-based equivalent electrical circuits and Monte Carlo simulations to predict the frequency-dependent electrical conductivity and EMI shielding effectiveness (SE) of the hybrid BP/CFRP composites. The study examines a signal frequency range of 50 MHz to 12 GHz that includes the very high and X-band. The results show that at a frequency of 12 GHz, the transverse conductivity increases to approximately 12.67 S/m, while the longitudinal conductivity decreases to about 3300 S/m from an initial value of 40,000 S/m. These results are then integrated into the ANSYS High-Frequency Structure Simulator to predict SE by simulating the propagation of electromagnetic waves through a semi-infinite composite shield element. The numerical simulations illustrate that incorporating BP significantly improves the SE of CFRP composites beyond 2 GHz owing to its high conductivity in that frequency range. For instance, at 12 GHz signal frequency, adding a single BP interleaf enhances the SE of a [90, 0] laminate by up to ~64%.  
carbon nanotubes; buckypaper; EMI shielding electrical conductivity; quantum tunneling; CFRPs; polymer composites 202148960. [[https://doi.org/10.3390/app1419960](https://doi.org/10.3390/app1419960)]([https://doi.org/10.3390/app1419960](https://doi.org/10.3390/app1419960))  
Article Electromagnetic Interference Shielding Analysis of Hybrid Buckypaper-Reinforced Polymer Matrix Composites: A Quantum Tunneling-Informed Equivalent Circuit Approach. 202148960. [[https://doi.org/10.3390/app1419960](https://doi.org/10.3390/app1419960)]([https://doi.org/10.3390/app1419960](https://doi.org/10.3390/app1419960))
## 2 Topology of the Survey Area  
The survey region is Ny-Alesund (\(11^{\circ}54^{\prime}41.23\"\) E, \(78^{\circ}55^{\prime}31.65\"\) N), in the northwest of Spitsbergen Island, in the Svalbard Archipelago, Norway, located approximately 1000 km from the North Pole (Figure 1). This region consists of a peninsula to the northwest and a bay to the east, and the mean altitude is 8 m. The mean annual precipitation and air temperature are 364.6 mm and \(-4.1~{}^{\circ}\)C, respectively. Temperate summers can be experienced due to the northward passage of the warm Gulf Stream, but the winters are very cold. The Ny-Alesund region used to be a coal-mining region, and the major rock types consist of sandstone, shale, and coal [22]. In order to assess freeze-thaw characteristics of rocks in the Arctic Circle, specimens were collected from Locations A and B in Figure 1, using rocks that had fallen from rocky outcrops protruding over the shore or rock block specimens taken from the outcrops.  
Freeze-thaw actions are most active at cold regions, such as high latitude areas including Norway, Alaska, or Canada. Therefore, the study area is selected based on the literature by [PERSON] [22] and [PERSON] et al. [23]. It was reported that, at the region of Ny-Alesund, dolomite and quartzite are widely distributed at ground surfaces and are easily sampled minimizing destroy of nature environments. Dolomite and quartzite are formed at the geographical age of middle and upper carboniferous period [23]. There is limited research on these rocks.