esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise Infrared and Visible Image Fusion Using Spatial Adaptive Weights
## 4 Experiments
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **CC** & \(MSSSIM\) & **SCD [33]** & \(FMI_{det}\) [34] & \(SSIM_{a}\) & **PSNR** \\ \hline ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.2675 & 0.8655 & 1.6188 & 0.2691 & 0.7381 & 16.0222 \\ ours (\(L_{D_{i}}^{*}\), \(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.5\) & 1.2549 & 0.8785 & 1.6598 & 0.2501 & 0.7319 & 15.8065 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=5\) & 1.2379 & 0.8601 & 1.6986 & 0.2346 & 0.7139 & 15.2591 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.1\) & 1.2587 & 0.8625 & 1.5609 & 0.2736 & 0.7396 & 16.0723 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=0.5\) & 1.2567 & 0.8764 & 1.6566 & 0.2569 & 0.7326 & 15.8283 \\ ours (\(L_{D_{i}}^{data*}\)) & \(\lambda=0.5\), \(\gamma=5\) & 1.2165 & 0.8547 & 1.6916 & 0.24 & 0.6998 & 14.7879 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average values of quality metrics for 20 fused images of our RoadScene dataset. Different values of \(\lambda\) and \(\gamma\) are used in the loss function. The best values are indicated in red and the second best values are indicated in blue. There are two ways to construct the weight maps equal to the size of the output of discriminator, average pooling and maximum pooling. In Table 5, we analyze the effect of using both pooling strategies for the construction of the weights.The settings for this analysis include the weighted loss used in both \(D_{i}\) and \(G\) with \(\lambda=0.5\) and \(\gamma=0.1\). Based on these results, it is quite evident that average pooling performs better than maximum pooling.
## 4 Results
### Land Requirement Estimation and LULC Prediction under Scenario II
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{8}{c}{**Area (in km\({}^{2}\))**} \\ \cline{2-11}  & **UR** & **PD** & **FC** & **RB** & **PO** & **FO** & **WA** & **RL** & **WL** & **ML** \\ \hline
2019 & 33.10 & 220.74 & 21.84 & 19.78 & 79.22 & 436.91 & 33.37 & 27.26 & 16.41 & 2.71 \\
2020 & 33.48 & 219.75 & 21.84 & 20.65 & 81.18 & 434.86 & 33.37 & 25.90 & 17.75 & 2.58 \\
2021 & 33.86 & 218.77 & 21.84 & 21.52 & 83.13 & 432.80 & 33.37 & 24.54 & 19.10 & 2.44 \\
2022 & 34.24 & 217.78 & 21.84 & 22.38 & 85.08 & 430.74 & 33.37 & 23.17 & 20.44 & 2.31 \\
2023 & 34.63 & 216.79 & 21.84 & 23.25 & 87.03 & 428.68 & 33.37 & 21.81 & 21.79 & 2.17 \\
2024 & 35.01 & 215.80 & 21.84 & 24.12 & 88.98 & 426.62 & 33.37 & 20.45 & 23.13 & 2.03 \\
2025 & 35.39 & 214.82 & 21.84 & 24.98 & 90.93 & 424.56 & 33.37 & 19.08 & 24.48 & 1.90 \\
2026 & 35.77 & 213.83 & 21.84 & 25.85 & 92.89 & 422.50 & 33.37 & 17.72 & 25.82 & 1.76 \\
2027 & 36.16 & 212.84 & 21.84 & 26.71 & 94.84 & 420.44 & 33.37 & 16.36 & 27.17 & 1.63 \\
2028 & 36.54 & 211.85 & 21.84 & 27.58 & 96.79 & 418.38 & 33.37 & 14.99 & 28.51 & 1.49 \\
2029 & 36.92 & 210.86 & 21.84 & 28.45 & 98.74 & 416.32 & 33.37 & 13.63 & 29.85 & 1.36 \\ \hline
**Annual Change rate** & **0.38** & **-0.99** & **0.00** & **0.87** & **1.95** & **-2.06** & **0.00** & **-1.36** & **1.34** & **-0.14** \\ \hline \hline \end{tabular} Note: Annual change rate was calculated based on the classified LULC data in 2010 by SVM and allocated LULC area to maximize ecosystem service by LP.  
\end{table} 
Table 15: Annual land requirements under Scenario II for each LULC type. As a result, the significant LULC types with increasing area were urban and built-up area, para rubber, perennial trees/orchards, water body, and wetlands, but the several LULC types with decreased area were paddy fields, field crops, forest land, rangeland, and miscellaneous land. In particular, wetland--which provides the highest coefficient value for ecosystem services--is expected to increase in the future. Wetland areas increased from 16.41 km\({}^{2}\) in 2019 to 29.72 km\({}^{2}\) in 2029, coming from paddy fields (9.50 km\({}^{2}\)), rangeland (2.83 km\({}^{2}\)), and miscellaneous land (0.98 km\({}^{2}\)) in 2019. This result implies the efficacy of linear programming for determining reclaimed and other areas changing into the wetland. Like Scenario I, there was a slight difference between the required land area and the predicted area of each LULC type in 2029 under Scenario II. The deviation values varied from \(-\)0.0013 to 0.0033%; nevertheless, the summation of deviation values was 0.00%. (See Table A6 in Appendix B). Hence, the LULC prediction under Scenario II can be considered acceptable for estimating sediment and nutrient export in the study area.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description
### Channel Model  
For each node, the channel state \(h\) can be expressed as shown in [6] by:  
\[h_{k}=h_{s}h_{e}h_{l}, \tag{1}\]  
where \(k\) is the number of users, \(h_{s}\) is a random variable representing the intensity fluctuation due to the atmospheric turbulence, \(h_{e}\) is the misalignment fading due to the pointing error loss and \(h_{l}\) is the channel loss coefficient.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 6 Conclusions  
The characteristics of sandalwood essential oil depend on the species of origin, the seasonal variation, and the extraction methods. Furthermore, the amount, composition, and physiochemical characteristics of sandalwood essential oil will influence its various activities. With the increasing consumption of precious natural resources, the biosynthesis of important components of sandalwood oil in a microbial host is becoming increasingly urgent.  
Significant strides have been made in santalene and santalol biosynthesis within microorganisms. Research efforts have primarily concentrated on manipulating microbial strains to boost production levels. These endeavors have involved creating biosynthetic pathways in _S. cerevisiae_, optimizing P450-CPR redox systems, and suppressing the ERG9 gene to enhance the yields of santalene and santalol [67]. Moreover, successful outcomes have been achieved by adjusting the metabolic flux towards sesquiterpenoid synthesis by reducing ERG9 expression and amplifying the expression of santalol biosynthesis genes under GAL promoters in yeast [12]. In the case of _E. coli_, the enhancement of precursor flux and the engineering of santalene synthase have led to a substantial increase in \(\alpha\)-santalene production [68]. Additionally, introducing genetic modifications and implementing optimal feeding strategies in _Y. lipolytica_ have enabled the heterologous synthesis of \(\alpha\)-santalene, resulting in enhanced yields devoid of undesired by-products [91]. These advancements underscore the promising prospects for the efficient biosynthesis of santalene and santalol in microorganisms, paving the way for diverse industrial applications in the future.  
Despite this remarkable progress in reconstructing santalene and santalol biosynthesis pathways in microorganisms, some challenges remain. For example, P450-mediated metabolic engineering is often complex due to the limited expression of P450 enzymes. Approximately 40% of plant P450s are poorly expressed or not expressed in yeast and other microbes, which hinders pathway reconstruction in microorganisms [74]. Hence, optimizing the P450/CPR redox system in _S. cerevisiae_ for high conversion of \(\alpha\)-santalene to \(\alpha\)-santalol is considered a bottleneck to santalol heterologous biosynthesis.  
Investigation, X.Y., S.D.D. and Y.L.; Project administration, C.W., Q.C. and Y.L.; Supervision, J.Q. and Q.C.; Validation, D.L., M.G., S.N. and J.Q.; Writing--original draft, X.Y., S.D.D. and D.L.; Writing--review & editing, X.Y., W.L., D.L., G.D. and S.D.D.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2020 YFA0906800), the China Postdoctoral Science Foundation (2023M742602), and the National Natural Science Foundation of China (32300055).  
Not applicable.  
The authors declare no conflicts of interest.
# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 4 Deep Learning Techniques for Load/Generation Forecasting
### LSTM-RNN Based Framework for Time-Series Forecasting  
It has been proven that RNN methods are a good choice for forecasting time depending data such as load/generation curves for MG systems [32]. An RNN operates in a similar way to other neural networks except it has connections to the previous time-steps. In other words, each cell receives its previous output as an additional input as well as the regular input of the current time step [33]. Although, conventional RNN algorithms perform well for short-term temporal data, extended sequences cause severe delays and memory capacity problems at the learning stage [34].  
LSTM method was introduced to address these problems and gradually became the dominant RNN method for time-series forecasting [35]. The basic idea of LSTM is that at each time sequence the NN can store and trash some memory data which is performed by using input, output, and forget gates in the algorithm structure [9; 35]. An LSTM cell receives the cell state (\(C_{t-1}\)) and the output value from the previous time step which is also called hidden state (\(h_{t-1}\)). In addition, the cell receives a current input for each time step (\(x_{t}\)). The cell output for each timestep are current cell and hidden states, (\(C_{t}\)) and (\(h_{t}\)). It is useful to add, LSTM algorithm utilizes three sigmoid and two tanh non-linear activation functions [35]. An illustration of LSTM architecture is provided in Figure 5.
## 4 POD with Ambiguity Resolution
### Results  
One year of data in 2019 collected by GRACE-FO satellites were analyzed; 38 days were excluded due to large satellite maneuvers or data gaps. The method introduced in Section 4.1.2 is used to resolve the SD WL and NL ambiguities, and the method in Section 4.1.3 is adopted to fix the DD ambiguities. Firstly, the residuals and fixing rates of WL and NL ambiguities are presented. Then the absolute orbit accuracy of each satellite and baseline accuracy between two satellites are evaluated.
## 4 Experimental Results and Analysis
### Comparative Experiment with Other Algorithms  
This paper designs lightweight networks for edge devices based on U-Net. Therefore, this section compares it with other lightweight algorithms, including using common lightweight networks such as MobileNet and ShuffleNet to replace the encoder backbone, and using neural networks such as ENet that are specifically designed for image segmentation. In addition, a comparison was made between the non-lightweight segmentation model U-Net++ and TransBTS. UNet++ represents a pure convolutional neural network, whereas TransBTS belongs to the Transformer series of architectures. The experimental results are shown in Table 2:  
As can be seen from the above figure, ENet has the fastest detection speed and the smallest model size, but its detection accuracy is very poor. EDPNet not only leads  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **mIoU/\%** & **F1-Score/\%** & **Precision/\%** & **Recall/\%** & **Parameter/M** & **GFLOPS** & **Time/ms** \\ \hline MobileNet & 90.2 & 94.7 & 95.1 & 94.3 & 0.82 & 7.85 & 256 \\ ShuffleNet & 89.9 & 94.5 & 94.0 & 95.0 & 0.66 & 7.41 & 250 \\ SqueezeNet & 87.5 & 93.1 & 93.5 & 92.6 & 0.58 & 7.27 & 125 \\ ENet & 88.5 & 93.7 & 93.2 & 94.3 & 0.36 & 2.35 & 90 \\ DABNet & 91.7 & 95.6 & 95.8 & 95.4 & 0.75 & 7.94 & 176 \\ U-Net & 90.6 & 95.0 & 94.6 & 95.3 & 4.32 & 40.37 & 564 \\ U-Net++ & 94.2 & 96.8 & 97.1 & 96.5 & 11.48 & 142.85 & 1519 \\ TransBTS & 94.7 & 97.2 & 97.5 & 96.9 & 13.73 & 66.54 & 1672 \\
**EDPNet** & **93.4** & **96.5** & **96.7** & **96.2** & **0.60** & **6.50** & **96** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results with other algorithms.  
Figure 7: Loss stage experimental results.  
other lightweight models in segmentation accuracy, but also has a much faster detection speed. Compared to non-lightweight segmentation networks, EDPNet lags behind by 1% in segmentation accuracy, but its detection speed significantly outpaces that of other such networks. Figure 8 is a schematic diagram of segmentation comparison of different algorithms.  
As shown in Figure 8, the left side is a comparison of the self-made dataset, and the right side is a comparison of the network collected images. As can be seen from the figure, lightweight networks such as MobileNet and ENet can identify the ice-covered area, but the details are not processed finely enough, and a small number of false detections and missed detections occur. EDPNet performs well on the self-made data set and can perfectly identify the ice-covered area, but in the ice-covered images of network transmission lines with more complex backgrounds, there are also some areas that are misidentified. Overall, its recognition effect is better than other lightweight networks.
## 4 Results and Discussion
### Isotherm, Kinetic, and Thermodynamic Studies  
#### 4.4.1 Isotherm Models  
The isotherms were assessed to gain a better insight into the interactions between the adsorbents (CPW, APWH, APWN, APWP) and the pollutants (Cr(VI) and MB). Two models, the Langmuir isotherm, which is based on the uptake taking place on an adsorbent with a homogeneous surface, and the Freundlich isotherm, which states that adsorption occurs on an adsorbent with a heterogeneous surface, were used. Table 2 shows the calculated parameters for the Langmuir and Freundlich isotherms. The values of the non-linear correlation coefficients (R\({}^{2}\)) were used as the determining factors, and the values closer to unity (1) were considered. From the results in Table 2, the adsorption of MB and Cr(VI) was better fitted to the Freundlich isotherm. The Freundlich constant (N) indicates the process of favorable sorption. If the values are between 1 and 10, the uptake of the pollutants was favorable. From the results in Table 2, it can be concluded that the interaction between the adsorbents and the pollutants was favored since the N value for all adsorbents was greater than 1 and less than 10. The Freundlich constant (K\({}_{\rm F}\)) showed a strong affinity that  
Figure 6: pH effect for (**a**) Cr(VI) and (**b**) MB.  
Figure 7: Temperature effect for (**a**) Cr(VI) and (**b**) MB.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 3 Challenges Faced by Firmware Emulation and Its Application in Fuzzing
### Fuzzing Application Enhancement  
Fuzzing refers to an automated software testing technique that providing invalid, unexpected, or random data as inputs to a computer program to test whether the program perform as expected, which has been widely used in vulnerability detections. In general software fuzzing, the process includes input generation, delivery, execution, feedback collection, and feedback-guided input mutation. It is divided into a front-end (input generation and mutation) and a back-end (execution environment and feedback). In firmware fuzzing within an emulation environment (see Figure 4), the emulator serves as the back-end, facilitating (1) test case reception, (2) emulated execution, (3) feedback collection, and (4) crash detection. The emulated execution environment relies on CPU and peripheral emulation. Inputs should be delivered when the firmware reads data from peripherals, as fuzzing aims to uncover software bugs, which are more exploitable than hardware bugs. Unlike general software with defined data I/O interfaces (as discussed in F2 and F3 in Section 2), identifying external input/output interfaces in black-box firmware is challenging. For feedback, fuzzers mainly use code coverage, requiring the emulator to support instruction emulation at the basic block level and collect path and basic block feedback [15]. For bug detection, general fuzzers detect bugs by using existing security mechanisms within the target system or an integrated sanitizer. The sanitizer identifies bugs by detecting undefined or suspicious behavior through instrumentation code inserted by the compiler at runtime. However, MCU firmware, often using RTOS or bare-metal resources, lacks these mechanisms and source code for instrumentation, leading to undetected memory corruptions, also known as \"silent\" bugs [16]. Therefore, the emulator needs to support more fine-grained memory monitoring for bug detection. An ideal fuzzing application on an emulator should have low false-positive and -negative rates for bug detection and testing at high speed.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 10 Conclusions  
This paper proposes the architectural components of a smart farming monitoring system, based on modern IoT communication technologies and WSN capabilities, in cooperation with energy-saving protocol schemes. A use case study based on the DIAS project is also presented, enhancing the reliability and contribution of our model. The IoT agricultural applications enable farmers to collect and analyze meaningful data. Large landowners and small farmers should welcome the potential of IoT market for agriculture by installing smart technologies to increase competitiveness and sustainability in their productions. The rapid growth of population forces farmers to meet the demand by implementing agricultural IoT solutions in a prosperous manner. In the future, an in-depth study will be carried out regarding the real-time performance of the proposed model in the DIAS project.  
\begin{table}
\begin{tabular}{c c} \hline \hline DIAS platform hardware and software equipment cost & 45,000 euros \\ \hline Average increase in production results in euros & 10\% \\ \hline Average increase in produced yield in Kgs per 1000 m\({}^{2}\) & 20\% \\ \hline Average increase in safron quality per 1000 m\({}^{2}\) & 25\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Economic estimation for Greek saffron cultivation via using the DIAS platform in one year.  
**Author Contributions:** Conceptualization, A.T., P.S. and S.B.; Methodology, A.T., P.S. and S.B.; Writing--original draft, A.T.; Writing--review & editing, P.S. and S.B.  
**Funding:** This research was co-funded by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, grant number T1 EDK-04873, project \"Drone innovation in Saffron Agriculture,\" DIAS.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Fourier Transform Infrared Spectroscopy  
The FTIR spectrum of solids obtained reacting with Sr(OH)\({}_{2}\) at different reaction times are shown in Figure 8. The typical spectrum associated with carbonates has three absorption bands in the regions 1440-1452, 854, and 705 cm\({}^{-1}\). At 20 min of reaction, the presence of these three absorption bands confirms the formation of carbonate during CO\({}_{2}\) capture with the Sr(OH)\({}_{2}\) solution, obtaining strontium carbonate (SrCO\({}_{3}\)), which is characteristic of the aragonite group (absorption band at 854 cm\({}^{-1}\)).  
The FTIR spectrum of solids formed after reaction with Ba(OH)\({}_{2}\) is shown in Figure 9. The spectrum shows the three main absorption bands for carbonates at 1417, 854, and 700 cm\({}^{-1}\). In this case, the intensity of the absorption bands at 20 and 40 min are very similar. The absorption bands present in the spectrum confirmed obtaining barium carbonate (BaCO\({}_{3}\)). In summary, SrCO\({}_{3}\) and BaCO\({}_{3}\) correspond to the aragonite group of carbonates (absorption band at 854 cm\({}^{-1}\)).  
Figure 8: FTIR spectrum of solids formed during carbonation reactions with Sr(OH)\({}_{2}\) in the 1700 to 500 cm\({}^{-1}\) region.  
Figure 7: Micrographs and analysis SEM/EDS of solids recovered after carbonation reactions with Ba(OH)\({}_{2}\).  
Table 1 presents the wavelengths at which the characteristic absorption frequencies for carbonates occurred; these bands correspond with the characteristic vibrations of the CO3\({}^{-2}\) group [18; 19].  
The carbonates of the aragonite group display vibrations within 840-860 cm\({}^{-1}\), as observed in the FTIR spectra of strontium and barium carbonates. Some authors have reported the intensity of the absorption bands in the FTIR spectrum at 1429-1492, 879, and 706 cm\({}^{-1}\) of carbonates obtained after carbonation reactions from CO\({}_{2}\) capture [18; 20].
## 4 Applications of WSN in Urban Areas
### Gully Pot Monitoring
This methodology helps in optimizing maintenance efforts and minimizing the risk of sewer flooding in urban environments. Table 4 compares various methodologies for managing sediment-related issues and their objectives, sensor types, network configurations, results, and limitations. Each method utilizes acoustic or temperature sensors within distributed or centralized networks with mesh, star, or tree topologies. The methods aim to inform sediment accumulation, reduce the impacts of increased rainfall, quantify tire wear particles in gully pot sediments, and adjust maintenance policies to minimize surface flooding risk. While effective in improving certain aspects such as EC, NL, E, Delay, R, and Cost, the limitations include not improving throughput levels, reliability, or computational complexity and failing to minimize energy consumption during surface flooding. Each approach addresses specific sediment management challenges but encounters unique constraints in its implementation. The \(\checkmark\) in Table 4 shows that the method increases the performance of the particular metric, whereas the blank space indicates that the method does not consider the performance of the particular metric. The summary conclusions of gully pot monitoring are listed below. * The summary offers proactive solutions for managing sewer flooding, addressing extreme rainfall, poor drainage, and blockages with strategically placed sensors for monitoring and preventing overflows. * Advanced sensor technologies and network configurations, including Zigbee protocols, optimize gully pot sediment management, pollutant control, and maintenance scheduling. * Distributed network topology excels in real-time monitoring and data collection for sediment management. The star network topology, while useful for resource optimization, faces challenges with complexity and energy consumption. * The methodologies improve sewer flooding management, enhance public health, and promote environmental sustainability through innovative sensor technologies and network configurations.
## 2 Materials and Methods
### Deep Learning Reconstruction  
RDN [9] was used as the network for deep learning. Low NEX images (NEX2, 4, and 8) were used as input images, and high NEX images (NEX12) were used as teacher images to improve the image quality. The teacher image was used as the ground truth. The NEX2 images were the noisiest, and the noise became less noticeable with the NEX8. Since the noise was not artificially added, it was not known how much noise was contained in the image. We chose NEX12 as the teacher image because we determined that the image quality of NEX12 would be sufficient clinically in our laboratory. The rat dataset consisted of 33 cases for training, 5 for validation, and 5 for testing. The dataset for the teacher images was randomly selected from 5 of the 43 rats acquired. The parameters were as follows: RDB blocks, 20; convolution layers, 6; channels, 32; kernel size, 3 \(\times\) 3; patch size, 48 \(\times\) 48; epochs, 200; batch size, 16; learning rate, 0.0001, loss, L1 loss; activation, PReLU; and optimizer, Adam. The experimental environment was as follows: OS, Ubuntu 20.04 LTS; CPU, Intel Core i9-11900; GPU, NVIDIA GeForce RTX 3090; RAM, 128 GB; and PyTorch 1.11.0, Python 3.9.12. The network structure of the RDN is shown in Figure 1.