esa-sceva/satcom-chunk-collection huggingface dataset:
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 3 Results
### Fire Simulation and Validation  
The simulation model exhibits robust predictive capabilities regarding the shape, location, and directional progression of burned areas, meeting all established threshold criteria (Table 3, Figure 4a,b). Specifically, the simulated major and minor axes of the fitted ellipse are measured at 449 m and 305 m, respectively, surpassing the actual dimensions of 331 m and 246 m by 118 m and 59 m. These deviations result in proportional errors of 0.356 and 0.24, respectively, both remaining within the established threshold limits (0.445 and 0.404), indicating slight overestimations of area size. Additionally, the simulated centroid coordinates (99.7, 27.8) differ from the actual centroid position (99.7, 27.8) by only 22 m, producing a proportional discrepancy of 0.0229, significantly below the allowable threshold of 0.0747, thus underscoring the model's precision in spatial localization. Furthermore, the simulated fire spread direction, recorded as SW22.199\({}^{\circ}\), demonstrates a minor divergence of 4.317\({}^{\circ}\) from the actual observed direction of SW17.882\({}^{\circ}\), considerably lower than the permitted threshold of 19.579\({}^{\circ}\), confirming the model's efficacy in capturing dynamic fire progression. Overall, these results substantiate the simulation model's substantial reliability and practical applicability for accurately forecasting characteristics of burned regions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Metric** & **Actual** & **Simulated** & **Difference** & **Difference Ratio** & **Threshold** \\ \hline Fitted Ellipse Major Axis (Burn Area) & 331 m & 449 m & 118 m & 35.60\% & 44.50\% \\ Fitted Ellipse Minor Axis (Burn Area) & 246 m & 305 m & 59 m & 24.00\% & 40.40\% \\ Centroid Point (Coordinates) & 99.71, 27.81 & 99.71, 27.81 & 22 m & 2.29\% & 7.47\% \\ Fire Spread Direction & SW17.882\({}^{\circ}\) & SW22.199\({}^{\circ}\) & 4.317\({}^{\circ}\) & - & 19.579\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model verification.
# Facility Layout Problem with Alternative Facility Variants
## 5 Experiments
### Goals of Empirical Investigation  
The following three experiments were carried out to evaluate the performance of the single- and multi-objective evolutionary algorithms on the FLP with alternatives:  
* Experiment 1--The goal of this experiment was to analyze and compare the performance of the algorithms on the FLP with and without alternatives. Note that the search space in the case of the FLP with alternative shapes is much bigger than the search space of the single-shape FLP. Thus, the question was whether the algorithms could converge to solutions that were at least as good as the solutions found on the single-shape FLP. Experiments with both the EA and MOEA were carried out. In the former case, the maxFreeSpace optimization objective was used. In the latter one, the maxFreeSpace and minConnDist objectives were used. Note that, in this case, both optimization objectives were of the implicit type, as the workstation cost was not considered here.
* Experiment 2--The goal of this experiment was to investigate the effect of using the cooling scheme on the quality of solutions. Again, experiments with the EA and MOEA were carried out while optimizing the same objectives as in Experiment 1.
* Experiment 3--The goal of this experiment was to investigate the performance of the MOEA, given the optimization objectives were a mixture of explicit and implicit ones. The MOEA was run with the implicit maxFreeSpace and the explicit minCost objectives. As mentioned in Section 4.4, the minCost objective can easily be improved just by choosing the cheaper variant for each workstation. On the contrary, improving the maxFreeSpace objective requires finding a better placement for the given selection of the workstation variants. So, it is likely that the algorithm may prematurely converge to the set of solutions composed of low-cost/high-size workstation variants, thus exhibiting low maxFreeSpace values. Thus, the question was whether the MOEA could efficiently search for a diverse set of high-quality solutions.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 3 Datasets and Experimental Settings
### Experimental Setup  
#### 3.2.1 Implementation Details  
The method proposed is implemented on an Intel (R) Core (TM) i7-8750H CPU @ 3.20 GHz computer with 32.0 GB RAM, utilizing an NVIDIA GeForce GTX 2070 super 16 G graphical processing unit (GPU). The programming language used for the code is Python, and Pytorch is chosen as the deep learning framework.  
In order to improve the convergence speed and generalization ability of the model, we adopt transfer learning for training. The model obtained by training the Swin Transformer on the ImageNet-1K dataset is used for the pretraining weights in the experiments, and the model proposed in this paper is directly trained on the basis of its initial weights, which can not only shorten the training time but also avoid overfitting. The Adaptive Momentum Estimation and Weight Decay (AdamW) [56] optimizer is used for optimization, which adds  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Class** & **Training** & **Testing** & **Total** \\ \hline
1 & Ac & 2276 & 300 & 2576 \\
2 & Cb & 3210 & 300 & 3510 \\
3 & Ci & 2424 & 300 & 2724 \\
4 & C1 & 2718 & 300 & 3018 \\
5 & Cu & 2350 & 300 & 2650 \\
6 & Mi & 1826 & 300 & 2126 \\
7 & Sc & 2096 & 300 & 2396 \\  & Total & 16,900 & 2100 & 19,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and testing samples for the GCD dataset.  
Figure 10: Example images from the ASGC dataset: (**a**) altocumulus (Ac); (**b**) cumulonimbus (Cb); (**c**) cirrus (Ci); (**d**) clear (CI); (**e**) cumulus (Cu); (**f**) mixed (Mi); (**g**) stratocumulus (Sc).  
the L2 regular term to the Adam optimizer, thus solving the problem of parameter overfitting, which has the advantage of fast gradient descent. In addition, the model uses a scheduling technique called Cosine AnnealingLR [56] and sets the initial learning rate to 0.0016. This method causes the learning rate to fall rapidly and then rise steeply during the training process to avoid falling into the local minimum and to find the true global minimum.
# Intelligent Traffic Management in Next-Generation Networks
### Related Work  
Driven by the recent advances in ML/DL in network traffic management, many studies have been conducted to survey the works on this domain. For example, [PERSON] et al. [4] have provided a comprehensive survey of ML techniques applied to SDN. Different types of ML/DL algorithms and their applications in the SDN environment are presented. Nevertheless, they did not review the traffic prediction application. Additionally, the paper lacks several recent approaches as well as a dataset in the literature review.  
[PERSON] and [PERSON] [5] briefly reviewed the application of AI with SDN. Then, in [6], they presented an extensive overview of AI techniques that have been used in the context of SDN. Additionally, [PERSON] et al. [7] surveyed ML/DL algorithms and their applications in the SDN environment. However, these papers categorized the reviewed contribution based on the ML model (e.g., supervised, unsupervised) and not by the use case.  
A review on the approaches of traffic classification in software-defined wireless sensor networks (SDWSN) using ML has been presented in [8]. [PERSON] et al. [9] briefly surveyed traffic classification and traffic prediction using ML in SDN. Moreover, [PERSON] et al. [10] proposed a survey of the ML-based methods applied to fundamental problems in networking. In addition, a survey of SDN-based network intrusion detection systems using ML/DL approaches is presented in [11]. [PERSON] et al. [12] proposed a survey on different security challenges related to the application of ML/DL in an SDN environment. As the main difference, these papers did not cover all the network traffic management applications in one paper. Furthermore, the research challenges in our paper are significantly different than those papers.  
Table 1 summarizes existing survey articles with similar topics as ours. We categorize these papers into (i) overviews of deep learning, (ii) overviews of other machine learning algorithms, (iii) reviews of dimensionality reduction methods, and (iv) reviews of works at the intersection between DL/ML and SDN. The previous surveys have focused on ML/DL approaches in the SDN environment. However, they did not cover the traffic analysis applications like traffic classification, prediction, and anomaly detection. Additionally, they are lacking some new approaches. such as federated learning. In addition, while DL has the ability to learn features automatically, conventional ML algorithms try to extract knowledge from a set of features or attributes, but they do not have the capability to extract these features automatically. Therefore, choosing the appropriate features to be used with ML models is a predominant challenge. In this context, one important objective of this paper is to provide an overview ranging from dimensionality reduction and machine learning techniques to deep learning used in the SDN environment. Then, we explore the network traffic management/analysis that has benefited from ML/DL algorithms and SDN architecture.
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 3 Plasmonic Nanomaterials for MNPs Detection
### Advantages and Disadvantages of Plasmonic Nanomaterials  
The employment of plasmonic nanomaterials for MNPs detection is a completely novel topic, accounting for a limited number of papers (cited in the previous paragraphs) published in the last 2-3 years. All of these papers are proof-of-principle studies, opening up interesting perspectives for a wider use of these materials.  
Colorimetric methods can provide an easy to use technique that can be applied in the field without the use of expensive instrumentation. On the other hand, these methods are intrinsically unable to provide reliable quantitative information and will hardly be able to compete with an instrumental method. Colorimetric assays, however, are useful in many fields for fast and qualitative screening, and could also find employment in the analysis of MNPs.  
SPR sensing can rely on a solid base of instruments and chips already available in the market for other purposes, mainly for biological assays. This could offer a good starting point for the development of specific methods for MNPs detection and quantification. To date, however, only a couple of proof-of-principle papers have been published on this topic.  
PEF also looks promising, exploiting the good S/N ratio of enhanced fluorescence. The employment of PEF, however, has only been reported in one paper in the literature to our knowledge.  
Some of the aforementioned papers showed promising preliminary data on the discrimination of plastics with different sizes and different composition. The methods, however, still need to be tested on complex and real samples. The main issue that needs to be addressed is the performance of this methods on real samples and matrices.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 1 Introduction
For the inter-system differencing with different frequencies, due to the different wavelengths, the inter-system DD ambiguities still cannot be fixed to integers even if the DISBs are precisely known. [PERSON] et al. [17] proposed using triple-frequency carrier phase linear combinations to limit the effect of different frequencies on the ambiguity resolution in inter-system differencing. However, the influence of different frequencies still cannot be eliminated completely, and the model can only be applied to the triple- or quad-frequency case. [PERSON] et al. [18] proposed a GPS and BDS inter-system mix RTK model for short baselines, where the single-difference (SD) ambiguity of the reference satellite is corrected firstly with pseudorange. They verified that the inter-system model can improve the single-epoch AR performance with high cut-off elevation. However, the benefit of inter-system model to the positioning reliability needs to be further developed since the precision of DISB estimators can be improved with fixed ambiguities. The precise DISB estimators will become useful information to strengthen the positioning model for the subsequent epochs. In this paper, the time-variant characteristics of L1-B1 and L2-B2 code and phase DISBs will be analyzed in detail. Using the stability of the DISBs, an inter-system differencing model between GPS and BDS for medium-baseline RTK positioning is proposed. The dual-frequency ionosphere-free model is derived with real-time estimation of the code and phase DISBs. The positioning performance with the proposed model and its comparison with the classical differencing model will be tested using two sets of medium baselines. The rest of this paper is organized as follows: In Section 2, the models for combined GPS and BDS observations are introduced, including the classical differencing and the inter-system differencing models. In Section 3, the stability of L1-B1 and L2-B2 code and phase DISBs in time domain is analyzed with several zero and ultra-short baselines. In Section 4, the positioning performance for medium baselines and the comparison between the two models are tested. Some discussions and conclusions will be given in Sections 5 and 6, respectively.
###### Abstract
In this study, we propose an integrated approach combining the modified Akaike information criterion (AIC) method and the Simplex method to localize AE sources in the PRF. We performed a series of experiments on a scaled PRF model at a ratio of 1:50, which was composed of one raft and 25 piles, and explored the feasibility of using a fully automated three-dimensional source localization method on the PRF model. The AE signals were artificially generated by using Hsu-Nielsen source at three different locations--raft top surface, raft bottom surface, and middle of a pile. We compared the accuracy of the source localization between the conventional AIC method and the modified two-stage AIC method. The conventional single-stage AIC method requires the setting of a time window to capture the onset time, but it is difficult to use a pre-determined window size when the signal-to-noise ratio is low and the rising of the wave is not clear [21]. To overcome this limitation, the two-stage AIC method was applied at the predetermined time window based on the result of the single-stage AIC method. The location of AE sources was computed based on the onset time with the Simplex method. The discussion on the effect of signal quality and field implementation follows.
## 3 Results and Discussion  
### Convergence of Random Samples  
In order to evaluate the random sampling effect (i.e., variability in the input space), 1000 random combinations of data index were generated following a uniform distribution (to obtain the training and testing datasets with a ratio of 70/30). For each considered configuration of the ANN model (i.e., training function, activation function, number of neurons, etc.), 1000 Monte Carlo simulations were performed using these 1000 datasets. Therefore, 1000 values of R\({}^{2}\), RMSE, MAE and Slope criteria were obtained by computing the deviation between the testing target and the corresponding output for each configuration of ANN model. The statistical convergence of error measurement criteria R\({}^{2}\), Slope RMSE and MAE is analyzed in this section (also see Equation (3) for the calculation of that convergence). Figure 5a,b show the convergence estimation for (R\({}^{2}\), Slope) and (RMSE, MAE) over 1000 Monte Carlo random sampling runs, respectively. In Figure 5, various curves are associated with the configurations of the parametric study. Figure 5 shows that R\({}^{2}\) and Slope exhibit a lower order of fluctuation than do RMSE and MAE. This could be because RMSE and MAE measure the standard deviation of the prediction errors, whereas R\({}^{2}\) and Slope indicate the degree of linear correlation between the target and predicted LBC. Consequently, random sampling was necessary, in order to estimate the statistical fluctuation with respect to all error measurement criteria, especially in terms of RMSE and MAE. It can be concluded that 1000 Monte Carlo random sampling runs is sufficient to obtain a representative convergence estimation, ready for subsequent statistical analysis, as seen in Figure 5b (a fluctuation of 1% around the mean value was reached from 900 simulations).
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 2 Production of 3 HP from Glycerol
### Engineering of E. coli Cells for the Conversion of Glycerol to 3 HP
This tool was employed by [PERSON] et al. [PERSON] et al. (2016) in their effort to prevent the accumulation of 3-hydroxypropionaldehyde by fine-tuning the expression levels of aldehyde dehydrogenase and glycerol dehydatase. Moreover, they deleted the by-product formation genes, _yghD_ and _ackA-pta_, in order to improve metabolic flow towards the formation of 3 HP. During flask culture, 3 HP formation reached 17.9 g/L with a yield of 0.61 g/g. Following on from these results, the authors tried a fed-batchcultyation with the addition of glucose together with the glycerol. Under these conditions, the production of 3 HP increased to 40.5 g/L with a yield of 0.97 g/g. This increase in the yield of 3 HP formation could be explained by the better carbon flow of glycerol towards 3 HP formation, as glucose could cover the needs of cell growth. A different approach for balancing the pathway enzymes to prevent 3-hydroxypropionaldehyde accumulation, and in turn, improve the 3 HP production yields, was used by [PERSON] et al. [35]. In their work, they used a synthetic regulatory cassette comprised of varying-strength promoters and bicistronic ribosome-binding sites (RBSs) to control the expression of the genes. Fine-tuning of the levels of expression between the two genes could result in no secretion of 3-hydroxypropionaldehyde, which was achieved when aldehyde dehydrogenase had an expression that was 8-fold higher than that of glycerol dehydatase. This strategy resulted in the engineering of an _E. coli_ strain capable of producing up to 56.4 g/L 3 HP in a fed-batch bioreactor, with the addition of glucose together with glycerol. The addition of glucose as a co-substrate was also found to improve the activity of the aldehyde dehydrogenase gene, and in turn, improve the 3 HP production yields [36]. [PERSON] et al. [36] reported a 3.5-fold increase in the activity of the enzyme, which improved the 3 HP production in flasks from 3.39 g/L (control--no addition of glucose) to 6.80 g/L. Optimization of the glucose concentration and feeding strategy improved the 3 HP production to up to 17.2 g/L during fed-batch cultivation. The authors also suggested that addition of glucose reduced the imbalance between the activities of glycerol dehydratase and aldehyde dehydrogenase, as it led to an increase in the activity of aldehyde dehydrogenase. Not only the presence of 3-hydroxypropionaldehyde, but also high concentrations of 3 HP can inhibit the growth of the host microorganisms, thus hindering the production of 3 HP. Aiming to identify a 3 HP-tolerant _E. coli_ strain, [PERSON] et al. [37] studied nine acid-tolerant strains that efficiently produced various organic acids at high titers. Construction of the 3 HP producing strains was achieved by expressing the _dha_ operon, and an ald from _Azospirillum brasilense_. Of all the strains
## 3 Bending Test Results and Discussion  
### Tensile Strength before and after Cracking  
According to the ASTM C 1609 [35], the stress \(f\) can be calculated by  
\[f\ =\ \frac{PL}{bd^{2}} \tag{1}\]  
where \(P\) is the applied load, \(L\) is the span length, and \(b\) and \(d\) are the width and depth of the beam, respectively. Figure 3 shows the relationship between the cracking stress \(f_{cr}\) and ultimate stress \(f_{u}\) and the steel fiber content. The \(f_{cr}\) corresponds to the first peak stress defined in the ASTM C 1609 [35] and the stress at the limit of proportionality defined in the BS EN 14,651 [37]. \(f_{cr}\) is equal to \(f_{u}\) in the non-reinforced plain concrete beam, which exhibited no strength recovery greater than \(f_{cr}\) after cracking. The linear lines in Figure 3 were obtained from the linear regression analysis of the stress with respect to the steel fiber volume percentages from zero to 1.00%. \(f_{cr}\) is almost constant, while \(f_{u}\) increases in proportion to the steel fiber content. \(f_{u}\) was approximately 7.40 MPa for the reinforced concrete beam with a fiber volume percentage of 1.00%, which was 2.21 times that of the non-reinforced plain beam.  
The residual tensile strength is defined as the stress at the deflection of L/600 (=0.75 mm) and L/150 (=3.0 mm) according to the ASTM C 1609 [35]. JSCE SF-4 [38] defines the residual strength as the mean stress from the beginning (=zero deflection) to the deflection of L/150. [PERSON] et al. [14] excluded the uncracked region to calculate the residual strength up to a certain deflection. In this study, the method proposed by [PERSON] et al. [14] is used to define the residual strengths of \(f_{600}^{\alpha}\) and \(f_{150}^{\alpha}\), which are the mean stresses from the crack initiation to the deflection of L/600 and L/150, respectively.  
Figure 4 shows \(f_{600}^{\alpha}\) and \(f_{150}^{\alpha}\) with respect to the steel fiber volume fractions. The linear lines shown in Figure 4 were obtained from the linear regression analysis, except for the residual strength of the non-reinforced concrete beam, which was zero. The results of the linear regression analysis show  
Figure 3: Cracking and ultimate strengths with increasing fiber volume percentages from zero to 1.00%.  
that \(f_{600}^{eq}\) and \(f_{150}^{eq}\) increase in proportion to the content of steel fiber. \(f_{150}^{eq}\) is slightly greater than \(f_{600}^{eq}\), which means that the steel fibers play an effective role in increasing the residual strength. The \(f_{150}^{eq}\) of the reinforced beams with fiber volume percentages of 0.33, 0.67, and 1.00% was 3.96, 4.83, and 6.66 MPa, which are approximately 110, 136, and 170% of \(f_{cr}\), respectively.