esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Despite the fact that many academics have studied how laws, management systems, and emergency plans are developed, the majority of research techniques use qualitative analysis, which makes it challenging to deal with ambiguous factors and leads to subjective and inadequately rigorous conclusions. Simulation and analysis are made more difficult by the complexity of evaluation models, which limits their usefulness in real-world applications. The evaluation of meteorological disaster emergency response capability using RNN autoencoder-based dynamic feature extraction is a novel method proposed in this paper. This ground-breaking approach addresses the issues of dynamicity and inconsistent length of capability data, leading to a more precise assessment [13]. Additionally, the suggested approach makes use of deep learning techniques to gather and analyze large amounts of data, opening up new opportunities for development in the area of meteorological disaster emergency management. It is demonstrated that the RNN autoencoder-based dynamic feature extraction method efficiently and accurately extracts ability features from time series data while reducing dimensionality. This makes it more appropriate for assessing emergency response capabilities for meteorological disasters, particularly in circumstances where simple and unusual samples could result in a flawed assessment. To build a meteorological disaster emergency response capability evaluation index system, the proposed method starts with the selection of evaluation content and indexes. The development of an encoder for dynamic feature extraction based on the encoder-decoder architecture comes next. Then, to effectively extract ability features and reduce dimensionality, the RNN autoencoder deep learning ability dynamic rating method is used. This method is demonstrated through a series of experiments to increase the evaluation model's accuracy by concentrating on challenging samples. In conclusion, the suggested RNN autoencoder-based dynamic feature extraction method offers a more precise and effective method for assessing the capability of emergency response to meteorological disasters. The method has significant practical implications for improving emergency management capabilities in the face of rising meteorological disasters due to its effectiveness in efficiently extracting ability features from time series data, reducing dimensionality, and improving accuracy.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 3 Dataset Requirements
### Requirements for IDS Evaluation Datasets
#### 3.1.1 Content Requirements  
1. Complete capture: complete capture of the network traffic, such as communication between host, broadcast message, domain lookup query, the protocol being used. The most important thing from complete capture is that both flow data and pcap should be available.
2. Payload: payload is not needed for a flow-based approach. However, having comprehensive information and extracting the most out of the data is important. HIKARI-2021 is the dataset that provides labeled encrypted traffic, while the well-known datasets do not focus on encrypted traffic. There is a possibility that a full payload captured might be useful in the future.
3. Anonymity: synthetic traffic should provide full packet capture, while real traffic must anonymize certain packets to preserve privacy.
4. Ground-truth: the datasets should provide realistic traffic from a real production network, compared with the synthetic traffic, and ensure no unlabeled attack in the ground-truth.
5. Up to date: both packet traces from flow data and pcap should be always accessible by repeating the capturing process of the network traffic. Because the data are subject to change over time, repeating the procedures guarantees that the dataset always obtains the latest information.
6. Labeled dataset: correctly labeling data as malicious or benign is important for accurate and reliable analysis. The labeling process is a manual task and determined by the flow with a combination of the source IP address, source port, destination IP address, destination port, and protocol.
7. Encryption Information: information on how to establish benign or malicious traffic must be stated. We are focused on application layer attacks, such as brute force and probing that employ HTTPS with TLS version 1.2 to deliver the attacks.
## 5 Experimental Analysis
### JADEDO Convergence Curve  
The convergence curves for the JADEDO algorithm across the various functions (F1 through F12) depict the algorithm's performance and its ability to minimize the objective function effectively, as seen in Figure 2. The rapid decline in the best score within the initial iterations, as observed in the curves for F1, F2, F4, F5, F6, F9, and F12, indicates the algorithm's efficiency in finding good solutions early in the optimization process. Specifically, these curves show that JADEDO quickly converges toward optimal or near-optimal solutions, stabilizing as the number of iterations increases, which is a desirable trait in optimization algorithms. For F3, F10, and F11, the convergence is more gradual, reflecting a more challenging landscape for optimization where the algorithm continues to find better solutions as the iterations progress. The staircase pattern observed in the F9 curve suggests that the optimization process involves overcoming several local optima before reaching a better global solution. Overall, the JADEDO algorithm demonstrates robust performance across a variety of optimization problems, as evidenced by the smooth and consistent convergence patterns in the majority of the functions tested.  
Figure 2: Convergence curve analysis over CEC2022 benchmark functions (F1–F12).
## 4 Adaptive Fault-Tolerant Communication Scheme
### Fault-Related Information Collection  
The fault-related information collection module is responsible for obtaining the specific information from human body, physical environment, and the BSN itself. Based on the collected information, we can determine the reliability requirements of the sensors during runtime. In AFTCS, three kinds of fault-related information are collected:  
(1) Bioinformation. The bioinformation is mainly physiological data collected by biosensors, such as body temperature, heart rate and blood pressure, _etc_. For a specific biosensor, its reliability requirement might be low when the readings are in normal range (e.g., the range of a body temperature sensor's reading is 36 \(\,\)C-37.2 \(\,\)C), but the level of reliability requirements should increase when the readings indicate abnormality (e.g., the reading of a body temperature sensor is higher than 37.5 \(\,\)C).  
(2) Environmental information. The environmental factors (temperature, humidity, light, _etc._) may affect the health condition of the patient. Therefore, the environmental information will influence the reliability requirement of the biosensor. For example, in warm and humid regions, where water is available as a transmission medium, _Vibrio cholerae_ may proliferate rapidly to the level of an infective dose. Hence, a higher level of fault tolerance should be provided for the biosensors that collect _Vibrio cholerae_ data under the conditions of high temperature and high humidity.  
(3) Runtime system information. These parameters reflect the reliability status of sensor nodes, which include buffer usage, the residual battery life, _etc._ This information can be derived from an inner hardware memory of the biosensor. For example, if the buffer utilization of a certain sensor is larger than 90%, then its reliability requirement will increase; on the other hand, when a sensor's buffer utilization is normal (e.g., 50%), its reliability requirement will not increase or even decrease.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Attention Mechanism
#### 4.3.2 Transformer
[82] employed transformers to directly capture long-distance information from damaged areas. Attention-based models can learn long-distance image information for structural recovery, but they are constrained by the intensive computation required for large image size inference. To address these challenges, [PERSON] et al. [91] utilized a transformer-based model in a fixed low-resolution sketch space, dealing with the overall structure, with edges and lines as sketch tensor space. To maintain computational efficiency without input downsampling, [PERSON] et al. [92] designed an autoencoder based on image blocks, PVQVAE, which uses a non-quantized transformer (UQ-transformer) to negate the information loss caused by quantization. It takes the features of the P-VQVAE encoder directly as input without quantization, using only the quantized tokens as prediction targets, achieving good results. [PERSON] et al. [93] proposed a new transformer-based large hole inpainting model, customizing a transformer block for inpainting, where the attention module only uses non-local information from partially valid data, as represented by dynamic masks. [PERSON] et al. [94] used a pretrained transformer-based model, MAE, and used the features of its decoder output as prior information to guide the image Inpainting network for large damage area repair. Although the introduction of transformers has provided better results in the image inpainting field, the high computational load due to their complexity is significant. Therefore, further research is still needed. CNNs and transformers can be combined, using transformers to realize reconstruction priors and CNNs to supplement textures. While transformers restore the coarse consistency structure, CNNs enhance local texture details based on the coarse prior. The above-mentioned methods for optimizing transformers have been quantitatively evaluated on commonly used datasets, as shown in Table 4.
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
###### Abstract  
Accurate simulations of the spatial and temporal changes in vegetation gross primary production (GPP) play an important role in ecological studies. Previous studies highlighted large uncertainties in GPP datasets based on satellite data with coarse spatial resolutions (>500 m), and implied the need to produce high-spatial-resolution datasets. However, estimating fine spatial resolution GPP is time-consuming and requires an enormous amount of computing storage space. In this study, based on the Eddy Covariance-Light Use Efficiency (EC-LUE) model, we used Google Earth Engine (GEE) to develop a web application (EC-LUE APP) to generate 30-m-spatial-resolution GPP estimates within a region of interest. We examined the accuracy of the GPP estimates produced by the APP and compared them with observed GPP at 193 global eddy covariance sites. The results showed the good performance of the EC-LUE APP in reproducing the spatial and temporal variations in the GPP. The fine-spatial-resolution GPP product (GPP\({}_{\text{L}}\)) explained 64% of the GPP variations and had fewer uncertainties (root mean square error = 2.34 g C m\({}^{-2}\) d\({}^{-1}\)) and bias (\(-0.09\) g C m\({}^{-2}\) d\({}^{-1}\)) than the coarse-spatial-resolution GPP products. In particular, the GPP\({}_{\text{L}}\) significantly improved the GPP estimations for cropland and dryland ecosystems. With this APP, users can easily obtain 30-m-spatial-resolution GPP at any given location and for any given year since 1984.  
Google Earth Engine; Landsat data; gross primary production; EC-LUE
# Progress in the Synthetic Holographic Stereogram Printing Technique
## 3 The Research Status of Synthetic Holographic Stereogram Printing
### Color Reproduction Characteristic  
With the development of the holographic recording medium, color reflection holography has been the focus of research in recent years. The diffraction wavelength selectivity of reflection holography and the colorimetric principle were analyzed, and the expression of reflection hologram colorimetric system was proposed by [PERSON] et al., meanwhile the color reproduction quality of reconstructed images was evaluated [50]. Based on the spectral measurement of reproduced light, a new method of color management for a full-color holographic stereogram printing was proposed by [PERSON] et al. [51]. The color shifts with the variations of illumination beam angle were also discussed, and it was shown that the specified colors could still be reproduced within a certain range of incident angle variation.  
Based on the sensitometric characteristic of the holographic recording medium, multi-exposure [52] or space-division exposure [51] could be used in full-color holographic printing. In the multi-exposure method, the hogels were exposed by a single laser beam which combined the lasers of RGB colors simultaneously, and the recording effect depended on the dynamic range of recording material since there were three different gratings recorded in the region of the hologram [53]. In the space-division exposure method, the hogels were divided into different parts spatially, and each part was exposed with a single-color laser, and the diffracted light intensity would then decrease to one-third of that of monochromatic exposure as the recording density decreased.  
Figure 10: The first RGB pulsed laser holographic printer made by Geola in 2001 (**a**) and the printed hologram (**b**). (Reprinted with permission from ref [47], [SPIE]).  
A high-density light-ray recording method for a full-color, full-parallax holographic stereogram was proposed by [PERSON] et al. [54]. RGB lasers were used for the production and reconstruction of colorful holographic stereogram, and the relationship between the hogel size and the reconstruction effects, such as the angular resolution and the visibility of the hogel's array structure, was discussed. Experiment results showed that the angular resolution of reproduced light rays was 1.08\({}^{\circ}\) when the hogel size was 50 \(\times\) 50 \(\upmu\)m, which could satisfy the demand of the human visual system.
## Materials and Methods
### Research Methods  
During the study, the engine was supplied with gaseous hydrogen with helium addition. Fuel was injected into the prechamber and main chamber at a pressure of 7 bar. Pure hydrogen was always injected into the prechamber. Into the main chamber, the hydrogen-helium fuel was injected. Four measurement series were carried out during the study (Table 2). In each measurement series, fuel with different percentages of hydrogen content was injected into the main chamber. In the first measurement series, pure hydrogen was injected into the main chamber. On the second measurement series, the fuel contained 90% hydrogen and 10% helium. In the following series, the proportion of helium was increased while the proportion of hydrogen was reduced. Appropriate He/H2 compositions were ordered from a specialist supplier of industrial gases. Helium, as a noble gas, dilutes the hydrogen fuel, the properties of which are altered (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measurement Series** & **Helium Content of the Fuel** & **Hydrogen Content of the Fuel** \\ \hline
1 & 0\% & 100\% \\
2 & 10\% & 90\% \\
3 & 20\% & 80\% \\
4 & 30\% & 70\% \\
5 & 40\% & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the fuel in each measurement series.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**He/H2** & **Density** & **Energy in a Unit of Mass** & **Energy in a Unit of** \\  & [kg/m\({}^{3}\)] & **[MJ/kg]** & **Volume [MJ/m\({}^{3}\)]** \\ \hline
0/100 & 0.08985 & 120 & 10.782 \\
10/90 & 0.09871 & 108 & 10.661 \\
20/80 & 0.10758 & 96 & 10.327 \\
30/70 & 0.11644 & 84 & 9.781 \\
40/60 & 0.12531 & 72 & 9.022 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of fuels used in each measurement series.  
Figure 3: Schematic of a test bench for the combustion of hydrogen and hydrogen/helium mixtures.  
The injection time into the prechamber was adjusted so that the center of combustion was at 10 CA after TDC, then, by increasing the fuel dose, it occurred earlier after TDC. During each measurement series, the opening angle of the throttle was increased five times to raise the excess air ratio. Throttle opening angles were 8%, 14%, 19%, 27%, and 36%, respectively. During each throttle opening step, the injection time into the prechamber was increased by 2 ms, starting at 0 ms and ending at 8 ms. The injection time into the main chamber was constant at 9.5 ms. The experiments were carried out at an engine speed of 1500 rpm. The oil and coolant temperature was 90 \({}^{\circ}\)C (Table 4). The way the study was carried out ensured that the measured parameters varied depending on the excess air ratio.
# Optimal Control of an Ultraviolet Water Disinfection System
## 5 Discussion
### Different Control Hypothesis
#### 5.3.2 Flow Rate Imposed Owing to Technical Constraints  
In this case, the flow rate is constant (\(Q_{0}\)) and a priori known due to technical restrictions. The only control variable required to optimize the UVDS is \(I_{0}\). Hence, the performance index aggregating the two criteria has the following expression:  
\[\min_{I_{0}}J(I_{0})\ =\ D(I_{0},t_{f})\ +\ \alpha\cdot\left|A(I_{0},Q_{0},t_{f})\ -\ A_{0}\right| \tag{27}\]The new objective function \(J(I_{0})\) and the other elements defining UV-OCP1 describe a new OP, denoted UV-OCP3. Details concerning the implementation of the new algorithm are given in Appendix D.  
Let us suppose that, for technical reasons, the flow rate is set to \(Q_{0}\) = 0.2. Like in Section 5.2, the UV-OCP3's optimal solutions yield the four curves presented in Figure 7. In this case, the green curve represents the final value of bacterial abatement that can be denoted \(A(I_{0},Q_{0})\) because the final time is considered implicitly. In this new conjuncture, this value may be different from \(A_{0}\) because the UVSD does not follow the reference value.  
Figure 7 depicts the placement of the optimal solutions in two individual cases. Figure 7a illustrates the case that arises when, for example, \(Q_{0}\) = 0.2.  
Analyzing this case, we can note that the four curves have two segments as defined below:  
\[2.5\ \leq\ A_{0}\ <\ 3.7\ \Rightarrow\ \left\{\begin{array}{l}I_{0}\ =\ I_{\min}\ =\ 5\\ A(I_{0},Q_{0})\ =\ 3.7\\ UV\ \text{dose}\ =\ 50\end{array}\right.;\]  
That is, the value \(A\) achieved by the UVDS is greater than the reference value, even if the intensity takes its minimum value. The UV dose also takes its minimum value for the given flow rate. At the same time, the performance index (the black curve) is greater than the UV dose because it holds that:  
\[\left|A(I_{0},Q_{0},t_{f})\ -\ A_{0}\right|\ >\ 0. \tag{28}\]  
\[3.7\ \leq\ A_{0}\ <\ 4.1\ \Rightarrow\ A(I_{0},Q_{0})\ =\ A_{0};\]  
The UVSD succeeds in reaching \(A_{0}\). The greater the value of \(A_{0}\), the greater the values of \(I_{0}\) and UV dose.  
Figure 7 also illustrates the case (b) that arises when, for example, \(Q_{0}\) = 0.4. We can remark that the four curves also have two segments defined below:  
\[2.5\ \leq\ A_{0}\ <\ 3.748\ \Rightarrow\ A(I_{0},Q_{0})\ =\ A_{0};\]  
All curves increase linearly.  
\[3.748\ \leq\ A_{0}\ \leq\ 4.1\ \Rightarrow\ \left\{\begin{array}{l}I_{0}\ =\ I_{\max}\ =\ 10\\ A(I_{0},Q_{0})\ =\ 3.748\\ UV\ \text{dose}\ =\ 50\end{array}\right.;\]  
Figure 7: Optimal solutions with an imposed flow rate: (**a**) \(Q_{0}\) = 0.2; (**b**) \(Q_{0}\) = 0.4.  
The value \(A\) achieved by the UVDS is less than the reference value, even if the intensity takes its maximum value. The UV dose also takes its maximum value for the given flow rate. Simultaneously, the performance index is greater than the UV dose because the inequality (28) is valid.
# Generation and Propagation of a Hermite-Gaussian Correlated Schell-Model LG\({}_{0l}\) Beam
## 5 Conclusions  
Under interaction of nonconventional correlated function and vortex phase, the propagation rules of a partially coherent beam named HGCSMLG\({}_{0l}\) beam are investigated theoretically and experimentally. Our results show that the behavior of the intensity of the beam on propagation is determined by the vortex phase, spatial coherence width and correlation function of the initial beam together. When the spatial coherence width of the initial beam is large, the vortex phase plays a dominant role, and the beam spot in the focal plane still displays a dark hollow beam profile, which can be used for trapping a particle with refractive index smaller than that of the ambient. When the spatial coherence width of the initial beam is small, the correlation function plays a dominant role. The beam spot in the focal plane displays two or four solid beam spots, which can be used for trapping multiple particles simultaneous with refractive indices larger than that of the ambient. For the effect of the coherence width on the modulus of the DOC is not as obvious as the intensity distribution, the modulus of the DOC is affected by both non-conventional correlated function and vortex phase. In spite of the form change of the modulus of the DOC caused by the non-conventional correlated function, the handedness and TC of vortex phase can be distinguished from the modulus of the DOC. The robust of the handedness, and TC of vortex phase, which is not vulnerable to coherence length and correlation function, may be useful in information transfer and recovery.  
**Author Contributions:** Data curation, X.P., X.L. ([PERSON]); writing (original draft), X.P., X.L. ([PERSON]); writing, X.L. ([PERSON]), R.L., L.L., and Y.C.; supervision, C.Z., L.L., and Y.C.; project administration, Y.C. All authors read and approved the final manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (91750201, 11525418, 11774251, 11774250, 11804198); the Project of the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 3 Method
### Background Depth Prediction  
After extracting \(f_{a_{i}}\), a multi-scale depth decoding scheme is then applied to predict background depth maps [14]. The multi-scale depth decoding scheme has a multi-scale autoencoder structure that uses a multi-scale decoder to restore depth maps with multi-scale features \(f_{a_{i}}\) and edge maps \(l_{i}\) (where \(i=1\) to 4), which are extracted by the attention mechanism and a Laplacian pyramid [14], respectively. The multi-scale decoder has five scales. Each decoder consists of a different number of convolutional layers (\(3*3\)) and the number of convolutional layers decreases from high to low scales. After \(f_{a_{i}}\) and \(l_{i}\) pass through the convolutional operations at each scale, the decoder of each scale outputs depth residuals of different scales. The depth residuals are resized using bilinear interpolation and element-wise summation is used to combine the depth residuals of each scale and obtain the background depth map \(d_{b}\). Then, \(d_{b}\) is used to predict the final depth map in the depth map stitching module. To avoid severe errors in the overall depth maps due to scale problems, we adopted a depth loss function based on a scale-invariant error introduced in [2], as shown below:  
\[L_{d}=\sqrt{\frac{1}{N}\sum_{i=0}^{N}y_{i}^{2}-\frac{\lambda_{b}}{N^{2}}(\sum _{i=0}^{N}y_{i})^{2}}, \tag{1}\]  
\[y_{i}=log(d_{i})-log(d_{i}^{*}), \tag{2}\]  
where \(N\) is the total number of valid pixels, \(d_{i}\) is the depth value of the \(i\)th pixel predicted by the background model, \(d_{i}^{*}\) is the ground truth depth value of the \(i\)th pixel, and \(\lambda_{b}\) is a coefficient. As with the settings in [14], we set \(\lambda_{b}\) as 0.85.