esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CGST Codec Simulation for Processing Images
### PNG Image Compression  
The field of effective image compression is a current area of research, especially in satellite remote sensing systems [48]. These systems face the challenge of providing energy efficiency. Therefore, we tested the effectiveness of the CGST performance on images from the satellite remote sensing system. For this, we obtained data using the free navigation program SASGIS. This program enables the download of satellite images of the Earth's surface from various online mapping services. We chose Yandex Maps, which acquires images from Ikonos, QuickBird, and WorldView2 satellites. We compiled a dataset of 200 PNG images each with a resolution of 128 \(\times\) 128 pixels that covered Ufa city (Russia, Republic of Bashkortostan) [49].  
In CGST image compression, the input signals can be formed using the color channels to ensure their correlation. The group codec processes the original images to reduce the signal's dynamic range, and the receiver restores the images using the inverse transformation. In our simulation, we chose a coefficient value of k to reduce the dynamic range of signals by two times, or by 3 dB. This reduces the number of bits required for coding each image pixel from 8 bits to 7 bits, providing an additional compression of about 14%. Figure 3 illustrates the image compression results at each step for this case.  
According to the results, when using the CGST method to reduce the dynamic range of RGB channels by 3 dB or more, distortions were introduced in the transmitted information, mainly affecting the brightness. During compression, some pixels experienced a reduction in brightness level to zero, which led to a loss of information after the inverse transformation in the receiver, and the resulting image was not identical to the original. Furthermore, the extrapolation process resulted in undesirable image smoothing, which can be observed in Figure 3 in the output of the extrapolation block. As a consequence, the image produced by the codec demonstrated distorted color rendering due to the loss of brightness and blurring in the object's contours. This happened because the brightness values of the RGB channels were restored based on the signal passed through the extrapolation block.
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
###### Abstract  
Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation rely on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we applied Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach were made for the forward and adjoint simulations. Results demonstrate that the FNO network generated comparable simulations with small errors and was orders of magnitude faster than the pseudo-spectral time domain methods (~26\(\times\) faster on a 64 \(\times\) 64 computational grid and ~15\(\times\) faster on a 128 \(\times\) 128 computational grid). Moreover, the FNO network was generalizable to the unseen out-of-domain test set with a root-mean-square error of 9.5 \(\times\) 10\({}^{-3}\) in Shepp-Logan, 1.5 \(\times\) 10\({}^{-2}\) in synthetic vasculature, 1.1 \(\times\) 10\({}^{-2}\) in tumor and 1.9 \(\times\) 10\({}^{-2}\) in Mason-M phantoms on a 64 \(\times\) 64 computational grid and a root mean squared of 6.9 \(\pm\) 5.5 \(\times\) 10\({}^{-3}\) in the AWA2 dataset on a 128 \(\times\) 128 computational grid.  
photoacoustic imaging; image processing; computer vision; simulation; reconstruction; deep learning 2023
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 1 Introduction
By selecting a subset of representative high-precision data from the entire historical dataset, assessing the similarity in geographic configuration attributes between known and unknown points, and inferring the feature classification of unknownpoints, the method aims to decrease the effects of data noise and address the high precision requirements of historical data in earlier geographic similarity model evaluations. This approach has significant potential for refining landslide susceptibility assessments [24]. The southern Anhui mountainous area is one of the regions most susceptible to landslides in China. Here, landslide-related disasters have frequently occurred in recent years because of extreme weather, adversely affecting regional economic development [2]. This research selected Huangshan City in southern Anhui as the study region to conduct landslide susceptibility assessment based on the IV model, combining geographical environment similarity and landslide sample balance considerations [25]. This investigation brings forward a landslide susceptibility assessment method that integrates the IV and GOS models. It performs landslide susceptibility evaluation by choosing non-landslide points based on topological rules and using the geographic similarity features between landslides. The primary contributions of this study are detailed as follows:  
1. The paper thoroughly considered the linear correlations among several geographical attribute factors, using the IV model to establish linear relationships between landslides and each geographical attribute. Non-landslide points were uniformly and randomly selected, constructing a landslide sample set with geographic environment consistency constraints to optimise the spatial distribution of the landslide sample set. 2. Concurrently, an optimised GOS model was established using a geographically consistent landslide sample set, integrating remote sensing-derived feature factor imagery to create a probabilistic landslide susceptibility map for the study area. The map was subjected to uncertainty analysis of the prediction results, ensuring a reliable landslide susceptibility assessment. 3. The comparison of the susceptibility assessment observations with those from models such as RF, LR, RBFC, and the basic-configuration-similarity (BCS) model confirms the compelling advantages and superior applicability of the GOS model in landslide susceptibility prediction.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 3 Research Methodology
### Training of Generative Models
#### 3.1.2 Minutiae Map Generation  
The most crucial step in pix2 pix-based fingerprint reconstruction is the transformation of a minutiae template to an image depicting minutiae. We call such an image a minutiae map. This process is further referred to as minutiae encoding and visualized in Figure 2. Let\(M\) denote a minutiae map and \(Enc\) the minutiae encoding function; then, \(M=Enc(L)=Enc(MinExt(1))\).  
In the initial work on application of pix2 pix to fingerprints [43], minutiae are encoded by gray squares. Formally, we encode every minutiae \(L_{i}\) in the minutiae list \(L\) by drawing a gray square of a fixed size and centered at \((x_{i},y_{i})\) in an image of the same size as the original fingerprint image. The intensity of a gray value encodes the minutiae orientation \(\theta_{i}\). The distribution of colors and the size of a square may vary depending on an implementation. However, shades of gray used as an orientation encoding are not optimal because the slight difference in colors may dilute during convolutions.  
In [7], we focused on two alternative minutiae encoding approaches: \"directed lines\", also referred to as DL, and \"pointing minutiae\", also referred to as PM.  
**Directed Lines (DL).** Each minutiae \(L_{i}\) from the minutiae list \(L\) is encoded by a directed line, which starts at \((x_{i},y_{i})\) and is pulled in the direction defined by the angle \(\theta_{i}\). Bifurcations are represented by white lines (intensity value of 255) and endings by black lines (intensity value of 0). The intensity value for the background of a minutiae map is 128. This selection of intensity values is not random. The idea is to underscore the dualism of bifurcations and endings. It is claimed that directed line encoding outperforms gray square encoding for our purpose. For 500 ppi fingerprint images, we use a line with the length of 15 pixels and the width of 4 pixels.  
**Pointing Minutiae (PM).** Pointing minutiae encoding can be seen as a combination of squares and directed lines. For a minutiae \(L_{i}\) from the minutiae list \(L\), there is a square centered at \((x_{i},y_{i})\) and a line pulled in the direction defined by the angle \(\theta_{i}\). As in directed line encoding, bifurcations are represented by white lines (intensity value of 255) and endings by black lines (intensity value of 0). The intensity value for the background of a minutiae map is 128. As for directed line encoding, pointing minutiae encoding underscore the dualism of bifurcations and endings resulting in invariance to color inversion. For 500 ppi fingerprint images, we use a line with a length of 15 pixels and a width of 4 pixels. The square size is 7 \(\times\) 7 pixels.
## 3 Results and Discussion  
### Promotion of Terpene Biosynthesis by Light Supplements  
After two weeks of light supplementation, the increase of the cyclic monoterpene content in the treated leaves was observed. Especially, BL increased all four monoterenes (Figure 3): pulegone (362% increase compared to the WL control), mentofluran (285%), menthone (223%), and menthol (389%). RL slightly promoted pulegone (256%), menthofuran (178%), and menthol (197%). These results are consistent with a previous study that showed an increase in fresh weight and essential oils in several mint species (_Mentha piperita, Mentha spicata_, etc.) cultivated in a red-blue LED incubator [15]. Another previous report also observed the increase of terpene contents in _Cannabis_ plants under RL-BL sub-canopy LED lighting [21]. The precursors of all higher plant monoterpene biosynthesis, isopentenyl diphosphate (IPP) or dimethylallyl pyrophosphate (DMAPP), were proposed to be increased by the RL-BL supplement [21]. It is likely to explain our result showing the increase of cyclic monoterpene with either BL or RL supplement in Japanese mint (Figure 3). Interestingly, the accumulation of menthone (229%) or mentofuran (339%) was observed with FR light treatment. Our result is the first report that showed that FR light treatment increased terpen in mint plants. In natural conditions, long-day treatment tomint plants showed the accumulation of menthofuran [14]. It indicates that the content of menthofuran is associated with the life cycle of the mint plant. Our result showed that the FR treatment disturbed the plant response to the photoperiod, affecting the metabolism of menthofuran. Furthermore, the reaction to FR indicates that the biosynthesis of these compounds is possibly under the control of phytochromes. In our study, no significant promotion of plant growth was observed during two weeks of LED light supplementation at the fluence rate used. Also, the contents of cyclic monoterepenes shown in Figure 3 were normalized with the weight of fresh leaves. This suggests that the increase of cyclic monoterepenes was not due to the leaf area's expansion. In many _Lamiaceae_ plants, GTs in the aerial part of the plant body influence the biosynthesis of secondary metabolites, including terpen, and store them in a cavity space surrounded by a cuticle. Here, we hypothesized why the terpenes increased after LED treatments as follows: (1) BL treatment increased the density of GTs, and (2) light stimulated the processes of terpene biosynthesis.
## 4 Experiments
### Ablation Study
#### 4.5.2 Comparison of Residual Structure and Attention Layers  
In order to study the effectiveness of improved multi-layer residual connections and multiple attention mechanisms in enhancing news-feature extraction, each part is evaluated by disabling another part. Firstly, a single multi-head attention mechanism is used to preprocess the news. This is then compared with the performance of a single-layer model after the addition of residual structures, as shown in the left panel of Figure 2. Next, the performance of the model with added residual structures and a multi-layer multi-head attention mechanism is compared, as illustrated in the middle panel of Figure 2. Finally, the model with two-layer residual connections and two rounds of multi-head attention mechanisms is compared with the other models, as depicted in the right panel of Figure 2.  
From the left panel of Figure 2, it can be observed that the model with residual structures demonstrates better performance in extracting features from news data. Therefore, in subsequent experiments, models with residual structures are used. We believe that this result can be attributed to the fact that the residual structures improve the overall model fitting, ultimately enhancing the model's effectiveness. Since the addition of residual structures represents a significant change from no residual structures, the performance improvement is notable. From the middle panel of Figure 2, it is evident that there is a significant difference in the effectiveness of multi-head attention mechanisms with different numbers of layers for extracting news features. According to the experimental results, using a module with two rounds of multi-head attention mechanisms and internal residuals yields better results in extracting features from news data. We believe that this outcome is due to the limitations of a single round of multi-head attention mechanisms, while three rounds of multi-head attention could lead to overfitting. Therefore, the two-round multi-head attention mechanism proves to be the most effective. From the right panel of Figure 2, it can be observed that the presence or absence of a dual-layer residual structure, both internally and externally, also has an impact on the effectiveness of news-feature extraction. Based on the experimental results, it can be concluded that the model with both internal and external dual-layer residual structures performs better in extracting features from news data. We believe that this is due to the improved overall model fitting achieved by incorporating dual-layer residuals, thereby enhancing the training effectiveness of the model.
## 1 Introduction  
In the herbal markets and food industries of Korea and other East Asian countries, _Panax ginseng C.A. [PERSON]_ is one of the most important herbal plants. Its roots have been used as a main constituent in oriental medicine [1; 2]. _P. ginseng_ root contains many bioactive compounds including ginsenosides, peptides, polyacetylenic alcohols, fatty acids, and essential oils [3]. Among these compounds, ginsenosides are considered critical ingredients, having various pharmacological effects, such as enhancing the immune system, and anti-tumor and anti-diabetes activities [4; 5; 6; 7]. Each ginsenoside has specific activities, and the efficacy of ginseng is determined by the ginsenoside contents. Due to its biological utility, the production and use of ginsenosides has been receiving increased attention. More than 150 ginsenosides have been isolated and identified from the _P. ginseng_ plant [8; 9]. In particular, its distinct parts including the roots, leaves, stems, and berries are considered as good sources of ginsenosides [10; 11; 12]. The qualitative and quantitative ginsenoside contents may differ depending on the plant part of _P. ginseng_[13; 14]. Hence, to evaluate the efficacy of different ginseng parts and their effective production of ginsenosides, profiling the various ginsenosides from the distinct parts of _P. ginseng_ is required.  
To assess the levels of various ginsenosides from a complex _P. ginseng_ extract, a sensitive and reliable analytical method is necessary. To analyze ginsenosides, high performance liquid chromatography (HPLC) coupled with a UV detector [15; 16], or an evaporative light scattering detector (ELSD) [17; 18] are typically used. However, due to the limited availability of standard samples, only a few ginsenosides have been quantified. Liquid chromatography (LC) coupled with electrospray ionization/mass spectrometry (ESI/MS) has emerged as a robust tool for the comprehensive profiling of various ginsenosides [19; 20; 21]. Within MS, a full mass scan based on quadrupole time of flight (QTOF)/MS is a sensitive detector used to measure the exact mass values of target and non-target compounds [22; 23]. The ultra-performance LC (UPLC) system provides high resolution separation using a small particle size column, and is effective for analyzing various compounds in a complex mixture [24].  
In this study, UPLC-QTOF/MS with an in-house library was used to perform a comprehensive profiling of ginsenosides from the roots, leave, stems, and berries of _P. ginseng_. To construct an in-house library and the quantification of ginsenosides, we used a total of 58 standard samples, including malonyl-ginsenosides (MGRs), isolated in our laboratory. The qualitative and quantitative contents of ginsenosides were characterized in the distinct parts of _P. ginseng_.
# A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks
## 5 Simulation Results
### Comparative Analysis of QoS Parameters  
Figures 3-7 show the performance analysis of the proposed outlier detection mechanism over five MANET routing protocols using various QoS parameters in a network of 200-1000 nodes. The QoS parameters taken for performance analysis were jitter, end-to-end delay, throughput, and sender and receiver energy consumption. The five MANET routing protocols selected for analysis were destination-sequenced distance vector (DSDV) routing, ad hoc on-demand distance vector (AODV), dynamic source routing (DSR), optimized link-state routing (OLSR), and the zone-based routing protocol (ZRP). The observations of QoS parameters were as follows:  
Figure 3 shows the comparative analysis of jitter in 200-1000-node networks using the proposed outlier detection mechanism over five MANET routing protocols. The findings reveal the lowest jitter value for the ZRP--a hybrid protocol that acclimatizes to dynamic settings of the network. As the number of nodes rises, the jitter value also increases. The reason for the increase in jitter value is the rise in traffic, which results in increased overhead in the network.  
Figure 4 shows the comparative analysis of end-to-end delay in 200-1000-node networks using the proposed outlier detection mechanism over five MANET routing protocols. For end-to-end delay, the ZRP has the lowest value, while DSR has the highest value; with the rise in node numbers, the value of end-to-end delay rises; this is because of processing and propagation delays due to an increase in traffic that necessitates more processing time for the large number of requests received.  
Figure 5 shows the comparative analysis of throughput in 200-1000-node network using the proposed outlier detection mechanism over five MANET routing protocols. With the rising number of nodes, throughput also increases; this is because there are several paths available to connect to the destination, and this, in turn, increases the probability of successful transmission. Among the five studied protocols, the ZRP breaks unidentified communication by creating different zones that become accustomed to dynamic settings in the network. Hence, the ZRP performs better in comparison to AODV, DSDV, DSR, and OLSR.  
Figure 6 shows that average sender energy consumption is lowest for the ZRP and highest for the DSR protocol. This is because the ZRP makes a structured, connected, semi-hierarchical network that facilitates well-timed message delivery. In other protocols, the senders involved packet retransmission due to packet loss or discard in the unstructured network.  
Figure 7 shows the comparative analysis of receiver energy consumption in 200-1000-node networks using the proposed outlier detection mechanism over five MANET routing protocols. The ZRP has the lowest value for average receiver consumption, as the network is structured--unlike other protocols, where it is unstructured. Unstructured networks have a higher probability of retransmission or loss of packet acknowledgment.
## 2 Materials and Methods
### Artificial Neural Network Model
#### 2.2.3 Statistical Validation  
To determine the fit of the predicted values of the ANN to the actual measured values, the statistical validation suggested by the World Meteorological Organization (WMO) was used. The method involves the sum square error (SSE; Equation (2)) to represent the mean relative error, the root mean square error (RMSE; Equation (3)) to represent the mean absolute error, and \(R^{2}\) (coefficient of determination; Equation (4)) as the criteria for accuracy.  
\[\text{Sum Square Error }(\text{SSE})=\frac{1}{N}\sum_{i=1}^{N}(X_{i}-Y_{i})^{2} \tag{2}\]  
\[\text{Root Mean Square Error }(\text{RMSE})=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(X_{i}-Y_{i })^{2}} \tag{3}\]  
\[\text{Coefficient of determination }(\text{R}^{2})=1-\frac{\sum_{i=1}^{N}(X_{i}-Y_{i })^{2}}{\sum_{i=1}^{N}(X_{i}-X_{im})^{2}} \tag{4}\]  
where  
\(N\): number of samples  
\(X_{i}\): measurement  
\(X_{im}\): mean of measurement  
\(Y_{i}\): output.
## 2 Methodology
### Robustness Computation Method
#### 2.4.3 Computation of Robustness Index  
After the function \(P\) has been introduced, the robustness index \(R(\textit{Cropping},\,S,\,L,\,N)\) can be calculated in the theory. According to Equation (4), the formula of \(E_{total}\) is given as:  
\[E_{total}=C_{L\times N}^{S\times L\times N}. \tag{8}\]  
\(E_{suc}\) can then be calculated as the enumerations where ruined watermark bits are smaller than the threshold value \(r\) with the help of auxiliary function \(P\). The number of minimum correct watermark bits (denoted as \(X\)) is calculated by:  
\[X=\lceil L\times(1-r)\rceil. \tag{9}\]  
The operation \(\lceil\ \rceil\) rounds the number up to an integer. By changing the count of correct watermark bits from \(X\) to \(L\), the robustness index \(R(\textit{Cropping},\,S,\,L,\,N)\) is calculated based on Equation (7):  
\[R(\textit{Cropping},\,S,\,L,\,N)=\sum_{i=X}^{L}C_{L}^{i}\times P(i,N,S\times L \times N-N\times(L-i))/C_{L\times N}^{S\times L\times N}. \tag{10}\]  
Hence, the robustness index can be acquired based on the proposed formulae in numerical calculations.