esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We have proposed and numerically implemented a free space optical (FSO) communication scheme based on KK transceivers. Numerical simulations of ground-to-satellite (uplink) and satellite-to-ground (downlink) in the presence of weak, moderate, and strong turbulences were carried out. Our results show that the KK transceivers provide transmission performance comparable to coherent receivers, but at a significantly lower system cost and complexity. KK receiver uses a direct detection receiver and extracts the signal phase using Hilbert transform in the digital domain. Unlike the coherent receiver, KK receiver does not require a laser at the receiver. In addition, KK receiver is not sensitive to the transmitter laser phase noise and hence, conventional distributed feedback (DFB) lasers with relatively large linewidths can be used at the transmitter. In contrast, for coherent detection, expensive external cavity lasers with very small linewidths (<100 kHz) should be used. This smaller linewidth criterion becomes even more critical as the size of QAM constellation increases. KK transceiver requires only a single Mach-Zehnder modulator whereas the coherent transceiver requires a more complex optical I-Q modulator. Numerical simulation of 40 Gb/s, 35,000 km sat-to-sat link with 16-QAM showed that there is about 2.9 dB SNR penalty at a BER of \(10^{-3}\) for KK transceivers as compared to coherent receivers. However, in the presence of strong turbulence, the SNR penalty at a BER of \(10^{-3}\) for KK receivers becomes quite small (~0.4 dB).  
All authors contribute equally for writing this paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Council (NRC), Canada, and grant number HTSN-623.  
Not applicable.  
We would thank the National Research Council (NRC), Canada, SatCan, the Natural Sciences and Engineering Research Council (NSERC) and the Canada Research Chair Program (CRCP) for supporting this research work.  
The authors declare no conflict of interest.
###### Abstract
The expression for this correction parameter is  
\[\lambda_{\text{ng}}=f(\mu,\sigma,S_{k},K_{u},m) \tag{1}\]  
Existing non-Gaussian fatigue calculation methods are mostly based on Gaussian stress frequency-domain damage models, followed by non-Gaussian corrections. However, the accuracy of these correction methods is often unsatisfactory. Inspired by the Dririk method for Gaussian stress, this paper proposes a method to obtain the rainflow range PDF for non-Gaussian stress. With the advancement of computer science, AI and machine learning methods have been widely applied in engineering. Many scholars have introduced machine learning into fatigue calculations, such as predicting fatigue life through neural network models [13; 14; 15], predicting solder joint fatigue [16], predicting notch fatigue [17], and predicting crack propagation [18; 19]. These methods offer new approaches to solving fatigue problems. In this paper, a neural network is also used to establish a model for prediction of the rainflow range PDF of non-Gaussian stress. The focus of this study is on how to obtain the corresponding rainflow range PDF from assigned non-Gaussian stress PSD and kurtosis. This paper proposes a method to establish the relationship between stress PSD spectral parameters and kurtosis with a rainflow range PDF bi-Gamma model using a neural network. The proposed bi-Gamma model for the rainflow range PDF of non-Gaussian distributions will have significant value for theoretical analyses and calculations related to structural fatigue and reliability. This paper is organized as follows. Section 2 focuses on the definition of kurtosis, the definition of non-Gaussian stress, the simulation of the stress PSD, and the generation of time-domain stress signals based on the specified stress PSD and kurtosis values. Section 3 presents the frequency-domain method for calculating stress fatigue, as well as the non-Gaussian fatigue calculation method based on coefficient correction. It also introduces the bi-Gamma model for the non-Gaussian stress rainflow range PDF used in this paper. Section 4 provides a detailed explanation of the development of the stress PDF model and the neural network training process. Section 5 presents the neural network's prediction results, as well as the sensitivity coefficient test and robustness test for the neural network. Section 6 present the conclusion of this paper.
## 4 Results and Discussion
### Comparison of BET and BTT Agent Performances
#### 4.2.3 Utilization of Channels  
Tables 6 and 7 show the NOC used by BET and BTT in a selected time run. The BET met the rendezvous target at 225.2 s with 1104 iterations, whereas BTT converged at 100.8 s with 840 iterations. The NOC was recorded per 100 iterations to observe how the NOC was involved in communication between the agent and neighbors along the way until the agent reached the rendezvous point. BET showed a total of 71.09% NOC usage as compared to BTT, which utilized 100% of the channels. This was due to the effect of implementing event-triggering in the BET system, which resulted in the usage of channels which were not full for the first 500 iterations, as shown in Figure 11. As a result, there were at least 28.9% usage of channels reserved for the agent with the use of the BET controller as compared to none of the channels being reserved in the BTT system.  
The NOC of the first 10 iterations were recorded to observe the patterns of agent communication at each iteration. Referring to Table 8 and Figure 11, there were only a few agents that would send the information of the agent's state position at each iteration, which depended on the ETF condition concerning whether there were any violations. As a result of this, the channel utilization can be reduced in the BET system as compared to the BTT system, which fully utilized the channel, as shown in Table 9 and Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline Time (s) & 20.4 & 40.8 & 61.2 & 81.6 & 102 & 122.4 & 142.8 & 163.2 & 183.6 & 204 & 225.2 \\ \hline Iterations & 100 & 200 & 300 & 400 & 500 & 600 & 700 & 800 & 900 & 1000 & 1104 \\ \hline NOC & 248 & 181 & 177 & 255 & 436 & 500 & 500 & 500 & 500 & 520 \\ \hline \end{tabular}
\end{table}
Table 6: Number of channels per 100 iterations (BET).  
Figure 10: Agent state trajectories (BTT).
## 6 Non-Linear Methods
### Supply Chain and Inventory Management  
Supply chains in the food industry are characterized by non-linear dynamics, including variable demand patterns, perishable inventory, and transportation constraints. Non-linear methods are instrumental in optimizing these complex systems, ensuring efficiency and sustainability.  
A study on perishable food distribution utilized non-linear programming to optimize delivery schedules, reducing waste by 8% and transportation costs by 10%. Another application involved dynamic inventory management for a frozen food company, where non-linear models helped balance holding costs and spoilage rates, achieving a 12% cost reduction while minimizing product loss [38].
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 3 Results
### Real Routine Verification
#### 3.2.2 Validation of the Effectiveness of Personalized Planning
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Route Type** & **Related Road** & **Level** & **T** & **M** & **C** & **Personal-Value** & **Effective Length/km** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} literature \\ Reference Route \\ \end{tabular} } & Yun Qi Road & 4 & 0.49 & 0.35 & 0.53 & 0.45 & 3.77 \\  & South Second Ring & 4 & 0.49 & 0.35 & 0.53 & 0.40 & 8.77 \\  & East Second Ring Road & 3 & 0.37 & 0.22 & 0.38 & 0.29 & 8.78 \\  & 31 Avenue & 3 & 0.48 & 0.28 & 0.43 & 0.37 & 1.95 \\  & Wan Jialial viaduct & 2 & 0.67 & 0.65 & 0.59 & 0.58 & 2.50 \\  & Wan Jiali North & 6 & 0.37 & 0.32 & 0.47 & 0.33 & 2.54 \\  & Xianghu West Road & 7 & 0.28 & 0.30 & 0.34 & 0.27 & 1.13 \\ \hline \multirow{6}{*}{
\begin{tabular}{c} log trajectory \\ \end{tabular} } & Yun Qi Road & 4 & 0.49 & 0.35 & 0.53 & 0.40 & 3.77 \\  & South Second Ring & 4 & 0.49 & 0.35 & 0.53 & 0.40 & 8.77 \\  & Labor East Road & 3 & 0.35 & 0.20 & 0.39 & 0.27 & 1.37 \\  & Wan Jiali viaduct & 2 & 0.67 & 0.65 & 0.59 & 0.58 & 10.94 \\  & Fuyuan East Road & 6 & 0.45 & 0.35 & 0.41 & 0.38 & 1.22 \\  & Kaiyuan West Road & 6 & 0.45 & 0.35 & 0.41 & 0.38 & 1.29 \\  & Xi Xia Road & 6 & 0.49 & 0.36 & 0.47 & 0.41 & 2.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: _Cont_. characterize the matching degree between personalized paths. User driving habits \(\Lambda\) are satisfied in Formula (22). \[\Lambda=\frac{l_{\text{same}}}{l_{\text{total}}} \tag{22}\]  
Type \(l_{\text{same}}\) is the path length for the coincidence of planning path and log trajectory, \(l_{\text{total}}\) is the planning path length, and \(l_{\text{same}}\leq l_{\text{total}}\). The results of the above example are listed as follows: comfort + economic user A, time + economic user B, time + economic user C personalized path/literature reference path and user log trajectory matching the result shown in Table 15.  
From Table 15, the coincidence degree levels of the personalized planning paths that integrates users A, B, and C are 89.2%, 100%, and 84.5%, respectively, which are different from the corresponding user log paths. The coincidence degrees of the planning paths are 52.3%, 64.7%, and 48.7%. In comparison with the reference path in the literature, the coincidence rate of the personalized path that integrates user habits and users' log trajectory significantly increases by 36.9%, 35.3%, and 35.8%.
## 3 Results
### Effect of Two Types of Meshes on the Improvement of APF Application via NCP Pre-Treatment  
Among the various working elements of NCP, two types of meshes were investigated to identify the most important factor responsible for enhancing the APF effect due to NCP pre-treatment (Figure 4b). The F\({}^{-}\) content increased approximately fourfold in the group treated with APF after NCP pre-treatment compared to that in the group treated with APF alone. The F\({}^{-}\) content increased fourfold after NCP pre-treatment with a cotton mesh (DE). However, pre-treatment with NCP equipped with an electric grounded (EG) mesh significantly reduced the F\({}^{-}\) content compared to the other NCP treatment groups (\(p\) < 0.01).
## 3 Results
### The Analysis of the Conditions for Gamma-Decalactone Biosynthesis  
3.1.1. The Influence of Castor Oil Concentration on Gamma-Decalactone Production  
The influence of the substrate concentration in the culture medium on GDL biosynthesis was analyzed. Due to the fact that castor oil is used by microorganisms as both a carbon source (necessary for growth) and a substrate from which ribonucle acid is released as a result of lipase activity, relatively high concentrations of castor oil, from 10 g/L to 150 g/L, were analyzed (Figure 1).  
The results presented in Figure 1 showed that the concentration of castor oil in the medium influenced the amount of lactone produced by the yeast. When castor oil was used in the medium at a concentration of 10 g/L, the microorganisms produced a maximum of approximately 1.86 \(\pm\) 0.15 g/L of GDL. The highest biotransformation efficiency in this cultivation variant was observed on the 3 rd day. After that period, the GDL concentration decreased to values of 0.54 \(\pm\) 0.12 g/L (on the 5 th day of cultivation) and 0.088 g/L \(\pm\) 0.02 g/L (on the 9 th day of cultivation), respectively. It is assumed that the almost complete disappearance of lactone in the medium may have resulted from the yeast's ability to use it as a carbon source.  
The highest lactone concentration among the analyzed mediums was recorded for the oil content of 100 g/L. _Yarrovia lipolytica_ yeast produced approximately 3.06 \(\pm\) 0.20 g/L of the aroma compound and the maximum of the reaction occurred on the 7 th day of cultivation. For castor oil concentrations used in the range of 10-100 g/L, it was observed that higher oil concentrations delayed reaching the reaction maximum. At a concentration of 10 g/L, the reaction maximum was reached after 3 days of cultivation, at 50 g/L after 5 days of the reaction, and at 100 g of oil per liter on 7 th day of the reaction.  
In the above research, the highest concentration of castor oil, used at the level of 150 g/L, allowed for the production of only about 1.54 \(\pm\) 0.11 g of lactone per liter (during the analyzed 9 day reaction period). Such a large amount of oil in the medium resulted in much weaker cell growth (unpublished data), and thus a lower lactone concentration.  
In order to better visualize the influence of substrate concentration on the level of GDL biosynthesis, the curves of changes in GDL concentration over time were described using a five-parameter Pearson IV equation using TableCurve 2D v.5.01 software. High coefficients of determination from 0.9393 to 0.9939 were obtained. On this basis, the rates of change in GDL concentration depending on the substrate concentration in the biotransformation mediums were determined and described with the following equation:  
\[y^{-1}=1.1389798+0.060850278x+0.0064025173x^{1.5} \tag{1}\]  
where x is the concentration of castor oil (substrate) in g/L and y is the concentration of gamma-decalactone in g/L.
evolving oxygen, whereas oxygenic phototrophs, namely Cyanobacteria, produce oxygen during the photosynthetic process [8]. Carotenoid production in microorganisms can occur to mitigate oxidative damage as part of the microbial preventative and responsive defense mechanisms against reactive oxygen species (ROS). Various bacterial strains contain distinct pathways for carotenoid production. The final products of these pathways have different structures depending on the pathways, enzymes, and environmental conditions.  
Carotenoids generally possess a linear hydrocarbon backbone composed of C\({}_{5}\) isopenoid units present as six, eight, nine, and ten units, which are classified as C\({}_{30}\), C\({}_{40}\), C\({}_{45}\), and C\({}_{50}\), respectively. C\({}_{40}\) carotenoids are the most abundant in nature, and thus the most researched [9]. Carotenoids contain double bonds in their structures, which allow for numerous stereochemical arrangements, and account for their strong antioxidant potential [10]. The extent of the double bonds largely defines the carotenoid absorption of light and spectral properties, which typically fall between 400 and 550 nm, from violet to green light [11]. Within this range, carotenoid colors are detected from yellow to red to violet. However, unpigmented precursors are also present within carotenoid production pathways [12; 13]. Within the carotenoid groups, C\({}_{30}\) carotenoids are distributed among Euryarchaeota, Firmicutes, Cyanobacteria, Alphaproteobacteria, and Gammaproteobacteria [14]. Currently, few natural sources of C\({}_{30}\) carotenoids are known, and they are more commonly found in Gram-positive bacteria such as _Lactiplantiobacillus plantarum_[15], _Cytobacillus firms_ (originally named _Bacillus firms_) [16], and _Staphylococcus aureus_[17].  
The majority of carotenoid research is centered on the more naturally abundant C\({}_{40}\) carotenoids, resulting in a gap in the knowledge of other carotenoid classes. Disseminating information on the production of C\({}_{30}\) carotenoids with health benefits comparable to C\({}_{40}\) carotenoids is advantageous in expanding the current market share of carotenoids. The antioxidant properties of C\({}_{30}\) carotenoids, where similar or greater than their C\({}_{40}\) counterparts, provide alternative sources of natural carotenoids with varying applications in the food, feed, and health sectors. This article discusses the current knowledge on microbial production of C\({}_{30}\) carotenoids and their commercial applications considering their antioxidant content and potential in the food and pharmaceutical industries.
## 2 Materials and Methods
### Evaluation Index  
In this paper, the confusion matrix approach is used to evaluate the model, and the following equations are chosen as the evaluation indexes of semantic segmentation: overall accuracy (OA), precision (Precision), recall (Recall), and Mean Intersection over Union (MIoU):  
\[OA=\frac{(TP+TN)}{(TP+FP+TN+FN)}, \tag{4}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{5}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{6}\]  
\[MIoU=\frac{\frac{TP}{TP+FP+FN}+\frac{TN}{2}}{2}\frac{TN}{TN+FN+FP}. \tag{7}\]  
where TP denotes the real class, where the real class of the sample is positive and the model extracts it as positive; FN represents the class with false negative instances, in which the actual class of the sample is positive, yet the model classifies it as negative; FP is the false positive class, where the real class of the sample is negative but the model determines it to be positive; and TN is the real negative class, referring to the situation where the real class of the sample is negative and the model correctly classifies it as negative.
## 2 Related Works
### Multimodal Fusion Strategies: Early, Late, and Intermediate Fusion  
Multimodal fusion enables comprehensive crisis understanding by integrating heterogeneous signals. Fusion strategies are typically grouped into early, late, and intermediate fusion.  
Early fusion concatenates shallow features across modalities. [PERSON] et al. [19] integrated FastText and VGG-16 embeddings but suffered a 12% F1 drop due to semantic mismatch.  
Late fusion aggregates predictions from modality-specific models. [PERSON] et al. [20] used CNNs and logistic regression independently for text and images, achieving only 83.29% accuracy due to a lack of cross-modal interaction.  
Intermediate fusion has gained prominence. [PERSON] et al. [21] laid its technical foundation with the attention mechanism, enabling dynamic focus on critical cross-modal features (e.g., linking text descriptions to visual regions). [PERSON] et al. [22] contributed to this direction by proposing a multimodal data analysis approach for social media during natural disasters, demonstrating how text-image integration can enhance crisis-related information extraction--a finding that aligns with the effectiveness of intermediate fusion in capturing cross-modal semantics. Beyond feature fusion, [PERSON] et al. [23] highlighted the practical value of social media multimodal data in disaster response by developing methods for sub-event detection during disasters, showing that fine-grained event parsing (e.g., distinguishing \"flood impact\" from \"rescue initiation\") relies on effective cross-modal alignment. [PERSON] and [PERSON] [24] applied bidirectional attention over RoBERTa and ViT, reaching 98% accuracy. [PERSON] et al. [25] proposed CrisisMatch with few-shot learning and TextMixUp, reducing annotation needs by 60% while preserving accuracy. [PERSON] and [PERSON] [26] introduced cross-attention to resolve ambiguous tweets. These intermediate fusion advancements directly translate to real-world impact: [PERSON] and [PERSON] [27] validated this by applying multimodal data fusion to rapid housing and shelter needs assessment during disasters, proving that cross-modal integration enhances the practical utility of crisis response systems.  
Notably, [PERSON] et al. [28] demonstrated that fine-tuned Vision Transformers outperform CNNs under degraded image conditions. These findings justify our choice of intermediate fusion as the architectural base.
# Shallow Water Waves and Conservation Laws with Dispersion Triplet
## 1 Introduction  
One of the several forms of pre-existing models for shallow water waves is the Korteweg-de Vries (KdV) equation, which has been extensively investigated [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. A deluge of results has appeared from a variety of sources, ranging from its integrability, numerical schemes, and vector forms of the model for addressing multi-layered flow. The KdV equation was handled with various forms of nonlinearity, including arbitrary power-law. The model was also addressed with logarithmic law of nonlinearity, which yielded Gaussian solitary waves [3]. Later, multi-layered fluid flow along lake-shares and beaches was studied with the Gear-Grimshaw model as well as the Zareamoghaddam model, which were successfully handled both analytically and numerically together with the recovery of solitary wave solutions and their conservation laws [2, 3, 6]. Subsequently, the KdV equation was extended/generalized to incorporate dual nonlinear effects. This is [PERSON]'s equation (GE), which was later studied extensively to address shallow water wave effects. It is not out of place to point out the fact that apart from the KdV equation and [PERSON]'s equation, there are several other forms of nonlinear wave equations that gave way to N-solitonsolutions. The method of integrability for such models is the Riemann-Hilbert approach. although there is yet another well-known and well-established scheme to locate N-soliton solutions including soliton radiation [11; 12; 13]. This is the Inverse Scattering Transform (IST).  
The current paper will revisit both models with the inclusion of triple dispersive effects that stem from triple spatial dispersion, spatio-temporal dispersion and dual-temporal-spatial dispersion. This gives an extension to the familiar couple of models that have been studied with third-order dispersion only. While the spatio-temporal dispersion is addressed in shallow water waves with Benjamin-Bona-Mahoney equation, the dual-temporal-spatial dispersive effect was first proposed in 1977 by [PERSON] and [PERSON] and was never seen again after its first appearance [4]. The current work incorporates all three dispersive effects, and the solitary waves for both models are recovered. The relation between the inverse width and the soliton velocity are also analyzed. The simulations finally led to the visual effect of behind-the-scenes, mathematical analysis of the models. The conservation laws for all models are exhibited as well.
# Estimation of BER from Error Vector Magnitude for Optical Coherent Systems
###### Abstract  
This paper presents the estimation of bit error ratio (BER) from error vector magnitude (EVM) for \(M\)-ary quadrature amplitude modulation (QAM) formats in optical coherent systems employing carrier phase recovery with differential decoding to compensate for laser phase noise. Simulation results show that the relationship to estimate BER from EVM analysis for data-aided reception can also be applied to nondata-aided reception with a correction factor for different combined linewidth symbol duration product at the target BER of \(10^{-3}\). It is demonstrated that the calibrated BER, which would otherwise be underestimated without the correction factor, can reliably monitor the performance of optical coherent systems near the target BER for quadrature phase shift keying (QPSK), 16-QAM, and 64-QAM.  
error vector magnitude (EVM); quadrature amplitude modulation (QAM); carrier phase recovery +
Footnote †: journal: _photonics_
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 1 Introduction
I will clarify what high-resolution facies mapping means using seismic data and how we, as interpreters with geologic backgrounds, can achieve better facies and reservoir resolution with currently available data and without the heavy involvement of staff geophysicists. I will propose a workflow to promote the high-resolution interpretation of sedimentary lithofacies and reservoirs with seismic sedimentology, as promoted by this Special Issue. While this discussion may not cover every detail, I hope to present valuable concepts and methods to benefit our geologic colleagues.