esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this research, we have reported for the first time the room temperature rate coefficient of the reaction between Cl and _trans_-2-methyl-2-butenal at 760 \(\pm\) 5 Torr. Although this rate coefficient is quite high, with a similar value as the previously reported rate coefficients for other saturated and unsaturated aldehydes, the contribution of the Cl reaction to the homogeneous loss of T2M2B in the troposphere is less than 42%. This research also presents the first detailed product study for the titled reaction, using three complementary detection techniques. In summary, the main gaseous products identified were  
* Acetaldehyde, methylglyoxal, acetic acid, and 2,3-butanedione, detected by the three techniques;
* 3-chloro-2-butanone, detected by SPME/GC-MS and FTIR;
* Formaldehyde, methanol, and ketene, detected by PTR-ToF-MS and FTIR;
* HCl, CO, and ClC(O)H, detected only by FTIR.  
The major reaction products are HCl ((92.8 \(\pm\) 12.9)%), CO ((97.3 \(\pm\) 10.0)%), 3-chloro-2-butanone ((52.5 \(\pm\) 7.3)%), and acetaldehyde ((40.8 \(\pm\) 0.6)%), indicating that the Cl-addition to C-3 and the H-abstraction from the aldehyde group of T2M2B are the major reaction pathways with around 53% and 40% contribution, respectively. Formation of ultrafine particles (5.6-560 nm) was investigated for the first time and we concluded that the SOA yield is very low. In conclusion, the impact of the observed degradation products, especially secondary pollutants (HCl, CO, carbonyl compounds, and aerosols) on air quality or acid rain episodes is expected to be low at the expected mission levels of T2M2B.  
Conceptualization, M.A. ([PERSON]) and E.J.; formal analysis, M.A. ([PERSON]); funding acquisition, J.A. and E.J.; investigation, M.A. ([PERSON]) and M.A. ([PERSON]); methodology, M.A. ([PERSON]) and E.J.; project administration, J.A. and E.J.; supervision, J.A. and E.J.; writing--original draft, M.A. ([PERSON]); writing--review & editing, J.A. and E.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Castilla-La Mancha (UCLM) through the project 2019-GRIN-27175 (_Audas para la franciacion de actividades de investigacion dirigidas a grupos_). Authors would like to thank the Spanish Ministry of Science, Innovation and Universities and the European Union for funding through the _European Regional Development Fund_ project (FEDER, Ref. EQC2018-004157-P). M.A. ([PERSON]) also acknowledges the NANOCOSMOS Synergy grant (SyG-610256) awarded by the European Research Council and M.A. ([PERSON]) acknowledges UCLM _Plan Propio de Investigacion_ for funding their research contracts.  
The authors declare no conflict of interest.
## 5 Conclusions  
This paper proposed a novel NIR colorization method using ACD and pixel-level fusion. Our method can generate high-quality RGB images in different natural scenes. We compare classical colorization methods based on a common dataset and the experimental results show that the proposed method provides better images in enhancing textures and coloring naturally. It is worth mentioning that our model is more suitable for training and colorization of large-size NIR images, so it has greater practical application value. Furthermore, we conducted ablation studies to demonstrate the contributions of the three modules proposed in this paper. From the result, we can conclude that ACD enhances the colorization accuracy and texture sharpness, GLFFNet improves the color overflow and uneven color, and BFWLS fusion enables us to obtain more delicate coloring results. Under the condition of ensuring the coloring quality, future work will focus on the lightweight colorization of unpaired NIR-RGB images to address the lack of existing paired NIR-RGB datasets.  
Conceptualization, X.M. and W.H.; methodology, X.M. and W.H.; software, X.M.; validation, X.M. and R.H.; project administration, X.L.; resources, X.L.; writing--original draft preparation, X.M.; writing--review and editing, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation Action Plan of Shanghai Science and Technology Commission in 2022, grant number 21511102605.  
Not applicable.  
Figure 11: Color performance comparison of different modules: (**a**) ColorNet; (**b**) ColorNet combined with BFWLS.
## 4 Experiments and Discussions
### Attack Performance
#### 4.4.2 ImageNet Dataset  
More perturbed pixels are needful for attacking large-size images, such as ImageNet. As shown in Table 4, when the number of modified pixels is 3, 10, and 50, \(APR\) is 100%, 99.63%, and 87.72%, respectively. Multi-pixel attacks are more robust when sufficient hiding capacity is available. More perturbed pixels can help large-size images change their overall structures, and mislead the models to make classification errors.  
APR does not necessarily increase with the increment of the number of perturbed pixels. The differential evolution algorithm optimizes a solution vector composed of allowable perturbed pixels. Regardless of the number of allowable perturbed pixels, the ultimate goal is to achieve a certain level of overall perturbation. Meanwhile, an increase in the number of perturbed pixels also increases the amount of auxiliary information that needs to be embedded in the AE. Therefore, the number of perturbed pixels should be appropriate to satisfy the sufficient hiding capacity condition as much as possible, which can achieve a higher APR.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Pixels** & \(ASR_{A}\) & \(ASR_{RA}\uparrow\) & \(APR\uparrow\) \\ \hline \multirow{3}{*}{MobileNet} & 3 & 25.30\% & 25.30\% & **100.0\%** \\  & 10 & 32.60\% & 31.50\% & 99.63\% \\ \cline{1-1}  & 50 & 39.10\% & **34.40\%** & 87.72\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Attack performances with different numbers of perturbed pixels on ImageNet.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Method** & \(ASR_{A}\) & \(ASR_{RA}\uparrow\) & \(APR\uparrow\) \\ \hline \multirow{2}{*}{LeNet} & o & 66.50\% & 44.04\% & 63.60\% \\  & w & & **57.02\%** & **81.89\%** \\ \hline \multirow{2}{*}{ResNet} & o & 41.02\% & 24.10\% & 51.29\% \\  & w & & **35.10\%** & **75.32\%** \\ \hline \multirow{2}{*}{DenseNet} & o & 24.02\% & 12.80\% & 50.29\% \\  & w & & **19.08\%** & **72.93\%** \\ \hline \hline \end{tabular} \(\uparrow\) indicates that higher values are better. The same applies to the tables that follow.  
\end{table}
Table 3: Attack performances on different models; “o” and “w” represent without and with D-CSDPP on CIFAR-10.
## 2 Related Work
The use of artificial intelligence (AI) and machine learning (ML) to optimize the routing and charging of FEVs was investigated, highlighting how AI can enhance the efficiency and effectiveness of FEV operations by leveraging real-time data and predictive analytics [35; 36]. Another study examined the integration of blockchain technology with AI to develop a decentralized platform for managing FEV data, ensuring transparency and security in data transactions [37; 38; 39; 40]. The interoperability of different systems and devices poses a significant challenge for the integration of FEVs into smart cities. The need for standardized protocols and data formats to ensure seamless communication between FEVs and other smart city components has been emphasized, with studies highlighting the importance of establishing common standards to facilitate interoperability and enhance overall efficiency [41]. International collaborations have also been explored to develop and implement standardization frameworks for FEVs, suggesting that global cooperation is essential to harmonize standards and promote the widespread adoption of FEVs [42]. The need for more robust and scalable energy management solutions to accommodate the growing number of FEVs has been identified, with recommendations for the development of advanced energy storage technologies and smart grid systems to enhance the resilience and efficiency of energy infrastructures [43]. In the realm of transportation, research has emphasized the need for more studies on the integration of autonomous FEVs with existing public transit systems, suggesting that a hybrid approach combining autonomous and traditional vehicles could optimize urban mobility and reduce congestion [44]. A comprehensive review of the literature on FEV integration into smart cities revealed several key areas where further research is needed [45; 46]. These include the development of more efficient energy storage systems, the implementation of advanced AI-driven traffic management solutions, and the creation of secure and interoperable data sharing platforms [47]. Addressing these challenges will require a collaborative effort from researchers, policymakers, and industry stakeholders to develop innovative solutions and drive the widespread adoption of FEVs. Despite significant progress, several challenges remain. The interoperability of different systems and devices is a major concern, as is the need for standardized protocols and data formats. Security and privacy issues also pose significant risks, particularly in data-intensive applications like traffic management and energy distribution. This study aims to contribute to this body of knowledge by presenting a comprehensive model for integrating FEVs into smart cities, optimizing energy management, and enhancing urban mobility. This model incorporates advanced data analytics, AI-driven optimization, and standardized protocols to address the existing challenges and pave the way for a more sustainable and efficient urban future.
## 4 Conclusions  
In situ doping of 5% Co or Ni into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays was performed by a facile aqueous chemical growth method followed by annealing at 500 \({}^{\circ}\)C. The doping did not change the crystal structure, morphology, energy gap and flat band potential of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorod arrays, but enhanced its PEC activity. At 0.2 V vs. SCE, the Co-doped and the Ni-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) exhibited a photocurrent density of 1.28 mA/cm\({}^{2}\) and 0.79 mA/cm\({}^{2}\), respectively, which were 2.1 times and 1.3 times that of the undoped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). The charge carrier concentration of the Co-doped and the Ni-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) were 2.3 times and 1.5 times that of the undoped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). Therefore, the increase in the photocurrent density of the doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) was likely attributed to the enhanced conductivity. In the future, the following work will be conducted. (1) Materials characterization such as energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) or inductively coupled plasma-mass spectrometry (ICP-MS) will be performed in order to provide direct evidence of the incorporation of Co or Ni. (2) The influence of dopant concentrations on the PEC performance will be investigated and the optimal concentration will be obtained. (3) The effects of Co doping and Ni doping will be compared by synthesizing samples with the same dopant concentration and distribution.  
Conceptualization, X.L. and F.C.; methodology, F.C.; validation, X.L. and F.C.; formal analysis, F.C.; investigation, F.C.; resources, X.L.; data curation, F.C.; writing--original draft preparation, F.C.; writing--review and editing, X.L.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51676098 and by Natural Science Foundation of Jiangsu Province, grant numbers BK20170095 and BK20160822. The APC was funded by Natural Science Foundation of Jiangsu Province.  
The authors also would like to give thanks to the Makerspace of Energy Saving and Emission Reduction which provides the place for experiments.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
This study assessed leaf fluxes of CO\({}_{2}\), CH\({}_{4}\) and biogenic volatile organic compounds (BVOC) for two common urban tree species, _Platanus \(\times\) acerifolia_ (exotic) and _Schinus molle_ (native), widely distributed in Santiago, Chile. The emission factors (EF) and the Photochemical Ozone Creation Index (POCI) for _S. molle_ and _P. \(\times\) acerifolia_ were estimated. The global EF was 6.4 times higher for _P. \(\times\) acerifolia_ compared with _S. molle_, with similar rates of photosynthesis for both species. Isoprene represented more than 86% of the total BVOCs leaf fluxes being 7.6 times greater for _P. \(\times\) acerifolia_ than _S. molle_. For _P. \(\times\) acerifolia_, BVOCs represented 2% of total carbon fixation while representing 0.24% for _S. molle_. These results may suggest that plant species growing outside their ecological range may exhibit greater BVOCs leaf fluxes, proportional to photosynthesis, compared to well-adapted ones. The results found may contribute to better urban forest planning.  
CO\({}_{2}\) and CH\({}_{4}\) fluxes; biogenic gases emissions; carbon fixation; Photochemical Ozone Creation Index; semi-arid mediterranean climate
## 3 Experimental Analysis
### Nonlinear Time Series Analysis of Datasets  
In order to analyze the nonlinear and chaotic characteristics of the time series data of metro passenger flow data, this study delves into their autocorrelation function plot, lag relationship, Lyapunov exponent, etc., and comes up with the following conclusions. We analyze the autocorrelation function (ACF) of the metro passenger flow data, as shown in Figure 7. The ACF plot shows a high correlation at the initial lag, which gradually decays as the lag increases and tends to zero around lag 30, indicating strong short-term dependencies but an absence of long-term persistence. This observation suggests that the system's dynamics are primarily governed by short-term influences, which aligns with the characteristics of chaotic systems where predictability declines rapidly over time. To quantify the chaotic nature of the system, we computed the largest Lyapunov exponent, which was found to be 2.18. A positive Lyapunov exponent indicates the presence of chaos, as it signifies sensitive dependence on initial conditions. The value of 2.18 suggests moderate chaotic behavior, implying that while there are complex, unpredictable dynamics, the system retains some structure that can be used for short-term predictions. The lag plot (Figure 8) demonstrates the relationship between the current value of the data and the previous value, forming a pseudo-attractor structure, further supporting the nonlinear and deterministic chaotic character of the data. The presence of pseudo-attractors indicates that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Size** & **Mean** & **Variance** \\ \hline Training & 4652 & \(-\)0.0057 & 1.0114 \\ Testing & 1164 & 0.0229 & 0.9535 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset division information.  
the system is governed by complex interactions, and traditional linear models struggle to adequately portray these characteristics. The above results reveal the chaotic nature of the metro passenger flow dataset. Therefore, it is necessary to construct a nonlinear, chaotic prediction model around the metro passenger flow datasets.
# Research on Improved GRU-Based Stock Price Prediction Method
## 4 Results
### Comparison of Experimental Results  
In this section, we analyze the performance of the proposed approach using the three evaluation parameters; namely, the RMSE, MAE, and MAPE, as presented in Section 3.6.  
We compared our proposed model with four baseline models; namely, a GRU, LSTM, an RNN, and a DNN. All six models were provided with input consisting of stock index data from the same four industries over the same time period. Three evaluation metrics--namely, the RMSE, MAE, and MAPE--were used to validate the models' output against the actual prices. The comparison results are depicted in Tables 3-5 and in Figures 15-17.  
Figure 16: The MAEs for the four industries with six different experimental methods.  
Figure 17: The MAPEs for the four industries with six different experimental methods.  
Figure 15: The RMSEs for the four industries with six different experimental methods.  
As depicted in the figures and tables, this study introduced two methods that exhibited superior performance compared to the other four methods across all three metrics (the RMSE, MAE, and MAPE). Particularly in terms of the RMSE, both StockAugNet-f and StockAugNet-c achieved error reductions of more than twofold compared to the GRU model, which was the best-performing model among the remaining four models. Furthermore, in terms of the MAE and MAPE, StockAugNet-c demonstrated the best performance across all four industries. Specifically, for the banking industry, StockAugNet-c exhibited reductions in the MAE of 15.14, 20.35, 93.23, and 151.6 percentage points compared to the other four models, while the corresponding reductions in the MAPE were 0.144, 0.203, 0.811, and 1.451. Although StockAugNet-f performed slightly worse than StockAugNet-c, it still showed significant improvements compared to the other four models.  
Based on the experimental findings, it is evident that the stock prediction model proposed in this paper, along with the data reconstitution method, achieved significant decreases in the loss function values across multiple industry-specific training and validation sets. Notably, even in the pharmaceutics sector, where slight fluctuations were observed, the performance of the model remained commendable. These results underscore the effectiveness of the experimental methodology in mitigating overfitting, thereby demonstrating its exceptional generalization capacity and robustness.
## 4 Proposed Multi-Channel Packet Scheduling and Forwarding
### Dual-IPT  
In this scheme, uplink and downlink traffic assigned to Ch.2 are accommodated via Bi-IPT function. As stated in Section 3.1, its non-negligible overheads may limit forwarding efficiency in compensation for the packet loss reduction effect. Although employing directional antennas can alleviate such limitations [25], antenna switching overhead cannot also be negligible. This paper investigates this problem based on omnidirectional antenna condition from aspects of the practicality.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Audio Degradations  
For this study, the audio component of the source sequences was impaired with four audio degradations: background noise, clipping, echo, and chop. The TCD-VoIP dataset [30] was used as a reference to reproduce this set of distortions. As stated in [30], these four audio degradations are platform-independent, i.e., they are not attached to a particular codec, network or hardware. As commented before, these distortions were selected with the purpose of studying their impact on the audio-visual quality independently of a particular use case scenario.
# Responses of Water Use Efficiency to Drought in Southwest China
## 2 Materials and Methods
### Analysis  
For each grid cell in the study region, we calculated annual GPP and ET from the monthly GPP and ET, respectively, for each year over the period 2000-2017. We then calculated annual WUE for each grid cell over the 18-year period from annual GPP and ET. The ecosystem WUE (gC kg\({}^{-1}\)H\({}_{2}\)O) of an ecosystem can be expressed as follows [45]:  
\[\text{WUE}=\text{GPP/ET} \tag{1}\]  
where WUE, GPP, and ET are the annual WUE, GPP, and ET, respectively.  
In this study, the proportion of the pixels affected by drought in Southwest China was used to measure the spatial extent of drought across the region. To assess the temporal variations in WUE and scPDSI, the linear trends of per-pixel annual WUE and scPDSI over the period 2000-2017 were calculated by using the ordinary least square regression (OLSR) method. For each grid cell, the trend was considered to be statistically significant if the \(p\) value was less than 0.05. The grid cells with missing values were excluded in our analysis.  
In order to compare and analyze the effects of drought on WUE, we calculated the difference of annual WUE (\(\Delta\)WUE) between the drought year and the normal year in the same region. If \(\Delta\)WUE values are greater than 0, WUE exhibits positive anomalies and drought likely increases WUE; if \(\Delta\)WUE values are less than 0, WUE shows negative anomalies and drought likely decreases WUE. The nonlinear correlation analysis was used to study the responses of annual WUE to drought.  
The trend of scPDSI from 2000 to 2017 was calculated with the Mann-Kendall (M-K) method, following [46; 47]. The M-K test does not require that samples follow a certain distribution, and it is also insensitive to outliers. This method is suitable for data with a non-normal distribution (e.g., hydrological and meteorological data). Many previous studies used the M-K method to analyze the trend in time series of precipitation, temperature, runoff, and water quality [48; 49].
## 2 Background and Motivation
### Category  
Table 2 illustrates the comparison between EDIJP and two other state-of-the-art edge-distributed inference frameworks. CoEdge [37] achieves parallel computing by partitioning data for each network layer. After the execution of each network layer, a synchronization wait is performed, and then each device transmits padding data to one adjacent device to ensure the correctness of the computation. EdgeFlow [48] also performs data partitioning on each network layer to achieve parallel computing. Intending to shorten inference latency, it determines the data partitioning scheme layer by layer based on the partitioning results of the previous network layer. Both of these frameworks do not use model partitioning, and after each network layer; there is data interaction between devices. Additionally, CoEdge also performs synchronous waiting. Although our solution has a small amount of overlapping computing, it greatly avoids frequent data transmission.
# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
###### Abstract  
As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.  
Storm surge prediction; long short-term memory; neural network