esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments
### Performance Analysis
#### 4.3.2 Comparison with High-Quality Competitors  
Due to the randomness and high computational cost of meta-heuristics, we choose one instance with a task scaled of 40, 60, 80, and 100, and repeat it 30 times to obtain ASP, AST, and PSP. Experimental results are shown in Table 9.  
GRILS is shown to obtain the best ASP in all scale instances, demonstrating its state-of-the-art capability to solve the AEOSSP. GDNN-PPO outperforms SDE and SGA in ASP by more than 23%, and its advantage becomes more apparent as the scale increases. Although GDNN-PPO does not perform as well as GRILS in ASP (4.2% lower on average), it decreases ASP by orders of magnitude. Also, unlike GDNN, which constructs the solution at one time, meta-heuristics must repeat the evaluation of the same solution, which is time-consuming. As shown in Figure 9, the ASTs of GRILS and SDE increase nonlinearly and dramatically with the scale of tasks. The AST of SGA increases marginally but is still much higher than that of GNDD-PPO. Therefore, GDNN-PPO has a time-sensitive advantage over high-quality competitors in solving large-scale AEOSSP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Instance Scale**} & **40.0** & **60.0** & **80.0** & **100.0** \\ \hline \multirow{3}{*}{GDNN-PPO} & ASP & **190.3** & **277.4** & **364.0** & **451.7** \\  & AST (S) & 0.283 & 0.581 & 1.175 & 1.671 \\  & PSP (\%) & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \multirow{3}{*}{GDNN-DQN} & ASP & 180.1 & 257.3 & 328.1 & 370.2 \\  & AST (S) & 0.206 & 0.392 & 0.659 & 1.134 \\  & PSP (\%) & 5.61 & 7.82 & 10.94 & 22.01 \\ \hline \multirow{3}{*}{STWA} & ASP & 115.4 & 172.1 & 227.9 & 284.5 \\  & AST (S) & 0.004 & 0.006 & 0.008 & 0.010 \\  & PSP (\%) & 64.82 & 61.24 & 59.71 & 58.75 \\ \hline \multirow{3}{*}{PTD} & ASP & 125.6 & 187.8 & 249.1 & 311.1 \\  & AST (S) & 0.007 & 0.012 & 0.020 & 0.027 \\  & PSP (\%) & 51.49 & 47.74 & 46.16 & 45.21 \\ \hline \multirow{3}{*}{RPID} & ASP & 121.2 & 181.6 & 240.7 & 301.0 \\  & AST (S) & 0.007 & 0.013 & 0.020 & 0.028 \\  & PSP (\%) & 57.02 & 52.75 & 51.22 & 50.07 \\ \hline \multirow{3}{*}{CDTD} & ASP & 99.6 & 149.7 & 197.2 & 246.6 \\  & AST (S) & 0.007 & 0.013 & 0.020 & 0.028 \\ \cline{1-1}  & PSP (\%) & 90.93 & 85.36 & 84.63 & 83.17 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results of construction heuristics in different instances.
## 3 Methods
### Framework  
In this paper, we interpreted risk coupling as the interaction and interdependence between risk factors that contribute to the occurrence of PA or further evolve into SA. To quantify the risk coupling values of accidents and explore the coupling mechanism of different accident categories (ACs), this paper proposes a risk coupling analysis framework for traffic accidents, as depicted in Figure 1. The proposed framework consists of four steps, which are explained further below.  
**Step 1: Text preprocessing**  
In Step 1, all accident reports are categorized into PA and SA. Then, the safety risk factors contributing to the occurrence of PA or the evolution into SA are manually extracted. In a former study [48], the authors manually extracted 22 risk factors for PA. This study expanded the scope to 61 risk factors for PA and 45 risk factors for SA, forming a manually labeled set. Finally, according to the types of factors classified in the previous literature [48], these factors are summarized into human (H), vehicle (V), road environment (R), weather (W), and management (M). At this stage, this process relies on expert input and relevant literature.  
**Step 2: Risk factor identification**  
In this step, a semiautomated method is developed for extracting traffic accident risk factors, enabling high-precision extraction of risk factors and their types from accident investigation reports, thereby reducing the cost of subsequent risk factor identification. Specifically, a traffic accident risk factor identification model is developed, namely, TRBERT-BiLSTM-CRF, based on domain-specific pretraining and the manually labeled set generated in Step 1. Then, cosine similarity is used for entity alignment to eliminate redundant risk factors. Finally, the risk of PA and SA is identified in the following format:  
\[\left\{Risk\ factor\ \xrightarrow{\mathit{belongs\ to}}\ Risk\ type\right\}\]  
**Step 3 Risk type mapping and counting**  
The accident samples are labeled using the five risk types defined in Step 2. For example, if an accident in PA is caused by two risk types, e.g., human and vehicle, this accident is marked as \"11000\" in PA. This step is repeated in each of the two ACs for all accidents in the dataset so that each accident in the two ACs is assigned a mark. Then, a statistical analysis is performed on the given ACs, calculating the frequency of the combination of different risk types. Since this study examined five risk types, the number of risk types in these combinations ranged from one to five.  
**Step 4 Risk coupling value generation**  
Based on the risk type coupling frequency in the given ACs in step 3, the risk coupling probability under different combinations is calculated by Equations (7), (8), (10), and (12). Then, based on the N-K model and mutual information, i.e., Equations (6), (9), (11), and (13), the risk coupling values for two to five risk coupling scenarios in a given AC are generated. Finally, the Wilcoxon signed-rank test is used to perform hypothesis testing on the risk coupling values of the two ACs, exploring the differences in risk coupling between PA and SA.
## 4 Results and Discussion
### Effect of Ambient Temperature
Additionally, for higher-speed railways, tunnel entrance hoods originally designed for existing line train-speed levels are insufficient to suppress the steepening process of the compression waves within the tunnel. Figure 22: The max pressure gradient at a propagating distance of 500 m.  
The Distribution Characteristics of Steepening Critical Distance of Compression Waves at Different Train Speeds  
During the propagation of compression waves in tunnels, there is a tendency for the pressure gradient to increase. Moreover, the larger the initial compression wave pressure gradient, the more significant the steepening during the propagation of compression waves. This section discusses the influence of train speeds on the evolution process of compression waves. Figure 24 shows the variation patterns of the maximum pressure gradient with propagation distance at different train speeds. Figure 25 depicts the variation pattern of the steepening critical distances with train speeds. The initial compression wave pressure gradient increases with the increase in train speed. The larger the pressure gradient of the initial compression waves, the stronger the nonlinear effect. This induces a higher airflow velocity, resulting in stronger frictional effects. Therefore, as the train speed increases, both the nonlinear effects and frictional effects are enhanced. The steepening critical distance shortens with the increasing train speed. Without the tunnel entrance hood, at train speeds of 200 km/h, 250 km/h, 300 km/h, 350 km/h, 400 km/h, and 450 km/h, the steepening critical distances are 14 km, 13 km, 10 km, 7 km, 5 km, and 4 km, respectively. This indicates that, with the increasing train speed, the friction effect becomes more prominent compared to the nonlinear effects. Frictional effects can suppress the steepening process of compression waves, leading to an advance in the steepening critical distance. The steepening extent of compression waves depends on the initial compression wave's pressure gradient, but ultimately, it is strongly influenced by the frictional effects. The friction effects determine the propagation characteristics of compression waves in the tunnel. Figure 23: The impact of tunnel entrance hoods on the reduction rate of maximum pressure gradient. Figure 24: The variation patterns of the max pressure gradient with propagation distance at different train speeds.
# Evaluation Model of Rice Seeding Production Line Seeding Quality Based on Deep Learning
## 4 Discussion
### Advantages of Our Model  
This study introduces a method for assessing rice sowing quality based on a refined version of the ResNet50 model. The experimental outcomes detailed in Section 3.7 clearly indicate that the improved ResNet50 model excels in the task of rice sowing quality detection. In comparison trials, this improved ResNet50 model outshines classic models such as AlexNet, DenseNet, and VGG16, particularly in detecting missed sowing areas within seedling trays. The standout performance of the improved ResNet50 model in this specific task can be ascribed to three key factors:  
1. This study leverages the superior ResNet50 model as the base for feature extraction in rice seedling tray images. The use of a residual neural network design is pivotal in enabling smoother information flow and preventing the vanishing gradient issue, which enhances the network's stability during training. This method also contributes to a reduction in the number of parameters, significantly increasing the efficiency of the model's operation [21, 22].
2. For the detection of missed sowing areas in rice seedling tray images, characterized by the random distribution of seeds, this study introduces the SE (squeeze-and-excitation) channel attention mechanism. This mechanism aims to heighten the model's focus on areas with distinct features. Furthermore, the model employs the Adam optimizer, distinguished for its ability to adjust learning rates adaptively based on different parameters during the process of updating loss gradients. This feature of the Adam optimizer significantly increases the model's concentration on pivotal regions [23].
3. The detection of missed sowing areas in seedling trays typically falls under the realm of object detection or semantic segmentation. However, the challenge of overlapping rice seeds during sowing and the complexities in edge extraction render object detection less effective for this purpose. This research introduces a novel preprocessing approach using virtual grid-style image segmentation. The process entails dividing a full seedling tray image into multiple grid images, which are subsequently categorized and annotated using the model developed in this study. Reassembling these annotated grid images culminates in the successful detection of missed sowing areas. This technique not only achieves the precise detection of these areas but also surpasses the efficiency of conventional object detection methods, laying a solid foundation for the model's integration into rice production lines.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 8 Regulated Structural Alterations in LPS
### Transcriptional Regulation of Major LPS Biosynthetic Operons by RfaH  
RfaH is a paralog of NusG family of universally conserved transcription factors. RfaH is unique in its specificity for recognition of only those operons that contain a short 8-nt conserved sequence (GGCGGTAG) in the 5' UTR called the _ops_ (operon polarity suppressor) pause site. Thus, RfaH regulates the expression of genes that play important functions in virulence such as the synthesis of LPS core, \(O\)-antigen, haemolysin, capsule and for conjugation [11; 12]. Recruitment of RfaH preventstranscriptional termination and enhances transcriptional elongation of long _waaQ_ and _rfb_ operons. Consistent with a role for regulation of expression of _waaQ_ and _rfb_ operons, they contain an 8-nt conserved _ops_ site and the JUMPstart site in their 5' UTR and also lack a ribosome-binding site. In the absence of RfaH, LPS is truncated, since the expression of the _waaQ_ operon is severely compromised and _\(\Delta\)rfaH_ mutants exhibit permeability defects, the induction of RpoE-dependent envelope stress response and are avirulent in pathogenic bacteria [11]. RfaH without interaction with the _ops_ site exists in an inactive form with a closed conformation with its C-terminal domain (CTD) folded in an \(\alpha\) helical hairpin tightly packed against its N-terminal domain (NTD). In this conformation, the NTD of RfaH is not accessible for DNA-RNA polymerase interaction [79]. However, upon encountering the _ops_ site in the presence of RNA polymerase, the CTD of RfaH folds into a \(\beta\)-barrel allowing recruitment of S10 ribosomal protein to couple transcription with translation and also enhance transcriptional elongation. Recently, a new Rira sRNA (RfaH interacting RNA) was identified while analyzing factors that induce transcription of the _rpoE_ gene [11]. Overexpression of the 73-nt RirA sRNA, located in the 5' UTR of the _waaQ_ operon, increases transcription from the _rpoE_P3 promoter that specifically responds to LPS defects. RirA overexpression abrogates the synthesis of \(O\)-antigen, causes reduction of LPS amounts and truncation in the LPS core, thus mimicking the _\(\Delta\)rfaH_ phenotype. Indeed, RirA directly interacts with RfaH in the presence of RNA polymerase and this interaction is dependent on the presence of the _ops_ site within the _rirA_ RNA [11]. This RirA-RfaH interaction could lead to loss of the specificity for recognition of _ops_-containing operons, like _waaQ_ and _rfb_ operons, needed for LPS core and \(O\)-antigen biosynthesis.
## 3 Results and Discussion
### Synthesis and Characterization
#### 3.1.4 EDS Characterizations  
Figure 4a shows the EDS spectrum of GO, which mainly revealed carbon (51.46%) and oxygen in GO composition (46.51%), confirming the added functional groups in graphite basal plane, forming GO. Despite the high percentages of these elements due to the presence of functional groups, we observed a small sulfur contribution (2.03%), likely originating from the synthesis process of graphene oxide [31]. In Figure 4b, the EDS spectrum of natural zeolite reveals that its chemical composition consisted mainly of oxygen (66.56%), silicon (23.64%), and aluminum (6.16%), with lower proportions of sodium (0.67%), potassium (1.46%), and calcium (1.51%). On the other hand, in Figure 4c, the EDS spectrum of the ZGO nanocomposite indicates that it was composed of oxygen (53.76%), carbon (20.90%), silicon (18.17%), aluminum (4.41%), sulfur (1.03%), potassium (1.06%), and calcium (0.69%). The presence of carbon within the nanocomposite structure suggests a redistribution of the percentage concentrations of elements such as silicon and aluminum, indicating an adjustment in the material's composition.  
Figure 4: (**a**) Electron-dispersive spectroscopy (EDS) spectrum of graphene oxide; (**b**) EDS spectrum of natural zeolite (bar, 100 μm) (**c**) EDS spectrum of nanocomposite formed of natural zeolite and graphene oxide (ZOG).  
The determination of surface area and pore size distribution was performed using the Brunauer-Emmett-Teller (BET) technique with an ASAP 2020 Plus system (Micromerities Instrument Corp., Norcross, GA, USA) and liquid nitrogen (77 K). Figure S2a demonstrates an increase in the specific surface area of the ZGO material (1020 m\({}^{2}\) g\({}^{-1}\)), which was attributed to the incorporation of graphene oxide (GO). The pore size distribution analysis revealed a heterogeneous structure, characterized by a higher proportion of mesopores (1.7-60 nm) and the presence of macropores (>60 nm, Figure S2b). This heterogeneity was likely due to the homogeneous dispersion of GO within the zeolitic matrix
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 6 Experimental Results
### Asymptotics for Graph Size  
Similarly, we perform an analysis to evaluate the behavior of the proposed method in terms of the graph size. Here, we set a fixed depth of \(d=\{4,5,6\}\) and perform a series of experiments for graphs of different sizes within the family \(\mathcal{S}^{l}_{1/2}\). For each graph size, we generate 100 random graphs and the average normalized approximation error, \(\epsilon_{\mathrm{n}}\), is reported in Figure 4b.  
As before, the trend of the normalized error follows the expected behavior provided by the theoretical analysis. That is, for graphs with increasing number of nodes, the quality of the approximation improves. This result illustrates the asymptotically efficient behavior of the proposed method for spectrum estimation using the hierarchical decomposition for these families of graphs.
# Sensors and Communication Simulation for Unmanned Traffic Management
## 5 Proposed Simulation Models for Key Elements
### GCS and C2 Links for VLOS and BVLOS Operations  
As previously explained, two possible C2 channels have been considered:  
1. A channel established directly between the GCS and the drone that is used in VLOS operations.
2. A channel that establishes communication using the public mobile communications network, to be used for BVLOS operations management.  
Over this channel, the drone periodically sends to the GCS telemetry messages including the following information: battery level, 3D position and speed, altitude, and alerts related to drone status (LOG). This information is then forwarded to the UTM by the GCS. The other existing interaction between the GCS and the UTM system is related to start/end of flight messages, including the authorized flight plan identifier. In parallel, the GCS continuously sends control commands to the drone. These commands can indicate that the drone must continue with mission execution, or they may schedule major changes in the drone flight plan due to the reception of action messages through the action bus. These last messages correspond to scheduled events such as flight start/finish and pilot actions as those defined in Section 5.1.  
The implementation of the BVLOS channel is an example of communication through the mobile network as explained in Section 5.3. In this way, both the drone and the GCS  
Figure 8: Perturbance simulation processing queue in each communications node.  
will be assigned to a base station that will serve traffic consisting of the aforementioned state and control messages encapsulated in a simulated communication packet. Apart from this, the VLOS case requires the establishment of a new logical channel in the message bus for each drone-GCS pair allowing the direct exchange of events between the two agents. As it is a wireless communication link, the availability of this communication channel demands that the GCS and drone be in their respective coverages. This coverage is defined within the GCS model by a maximum vertical range and a maximum horizontal range in meters and is checked, once again, using the periodic drone state messages and the own GCS position. If the drone is out of range, command messages stream is stopped, and \"received\" state messages from the drone are disregarded.  
The loss of link (LOL) situation is detected by both agents as they detect that no new state or command messages arrive. This situation can also be generated manually (simulating communication equipment failure) when the GCS receives a LOL contingency message through the action bus.
## 1 Introduction
Aiming to resolve the contradiction between optimal throughput performance and low complexity, an efficient resource allocation algorithm is proposed here, which achieves an approximate optimal throughput and quadratic polynomial complexity based on the convex optimization theory. Compared with the classical optimal inter-point method, the proposed algorithm has similar throughput performance and quality of service (QoS) guarantee while reducing the complexity. In the comparisons of system throughput, the proposed algorithm achieves an improvement of 57% in performance compared to the conventional RDR-PA method and the uniform allocation method [21; 22]. In terms of QoS guarantee, the proposed algorithm achieves an improvement of 67% in performance compared to the conventional RDR-PA method and the uniform allocation method. This paper is organized as follows. The system model is described in Section 2. The resource allocation algorithm is proposed in Section 3. The simulation results are presented in Section 4. Finally, the conclusions are provided in Section 5.
## 3 The Proposed Algorithm
### Phase I: Objective Color Correction (OCC)
#### 3.1.2 Network Training  
For network training, a training set is collected. In general, color correction works better if the Color Checker has more color patches with a wider color gamut. Therefore, we chose Color Checker SG [4] with 140 color patterns to train our model. Standard reference chromaticity values are available on the official website of X-Rite's [4]. In this phase, the sRGB color space is chosen because of its device-independent property.  
The loss function is Mean Squared Error (MSE), given by Equation (4):  
\[L(\theta)=\frac{1}{N}\sum_{i=1}^{N}||F(X_{i};W;B)-Y_{i}||^{2}, \tag{4}\]  
where \((X_{i},Y_{i})_{i=1}^{N}\) represents \(N\) training samples, \(X_{i}\) contains R, G, B color channels, and \(F(X_{i};W;B)\) is the output of the OCC network, with \(X_{i}\) as the input and parameters \(W\) and \(B\). To train network parameters, the loss function is optimized by using the [PERSON] algorithm [24].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layer** & **Parameter** & **Kernel** & **Channel** & **Activation** \\ \hline Input Layer & \(W_{1}\) & \(3\times 3\times 3\) & 64 & ReLU \\  & \(W_{2}\) & \(3\times 3\times 64\) & 64 & ReLU \\ Hidden Layers & \(W_{3}\) & \(3\times 3\times 64\) & 64 & ReLU \\  & \(W_{4}\) & \(3\times 3\times 64\) & 64 & ReLU \\ Output Layer & \(W_{5}\) & \(3\times 3\times 64\) & 3 & None \\ \hline \hline \end{tabular}
\end{table}
Table 1: The architecture of the OCC network.
## 2 Materials and Methods
### Experimental Setup  
The datasets used in this study include local datasets, EuRoC [52], KITTI [53], TUM [54], and UMA [55]. The local data were collected using a vehicle equipped with four fisheye cameras mounted on the front and rear bumpers and the left and right mirrors, capturing around-view images in an underground parking lot of an industrial park. The images have a size of 416 \(\times\) 416 pixels and were equipped at a frequency of 10 Hz. Except for the local dataset, all other datasets used in our work are publicly available. The experimental scenarios encompass indoor and outdoor environments, with images collected from around-view monitors, standard cameras, and fisheye cameras using vehicles, drones, and handheld devices. These scenarios cover conditions of normal luminance, low luminance, and high luminance. A detailed description is provided in Table 1. The diversity of experimental scenarios and the richness of the image data allow for a comprehensive evaluation of the effectiveness of the proposed method. Figure 6 shows some complex scenes in the dataset, such as stitched and rotated images. All experiments have been run on an AMD R5 CPU with 16 GB memory. For each input image, it is vertically divided into eight regions. When constructing the feature descriptor matrix, the grayscale values are quantized into 32 indexes, and the gradient direction are quantized into 64 indexes. To balance accuracy and speed in the first layer of detection, the number of candidate frames is set to 10 (\(k=10\)), meaning that 10 candidate frames are selected for further processing in the second layer of detection.