esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 3 Experimental Results and Discussion
### Processing Time Comparison  
To obtain a systematic evaluation of the proposed method, the processing time of threshold searching was investigated. The experiment was implemented on a Lenovo T470 PC with an Intel Core-i7 processor and 8 GB RAM. In the experiment, the original image in size of 2560 \(\times\) 1920 shown in Figure 7A was selected as the test image and the 2 Cg-Cr-Cb was used to obtain gray images for the Otsu, NVE (the neighborhood valley-emphasis) and proposed methods. To compare the processing time in the images with different resolutions, we resized the test image to 320 \(\times\) 237, 720 \(\times\) 576 and 1327 \(\times\) 1000. Table 1 shows the processing time of threshold determination. From Table 1, we can see that an increase in the image resolution leads to an increase in processing time. For an image of size of 1280 \(\times\) 960, our method finds the threshold in around 0.031 s, compared with 0.119 s for an image of size 2560 \(\times\) 1920. However, in the same case, the processing time of the Otsu method is more than double that of ours. The NVE method is the most time consuming due to the use of neighborhood information. Therefore, the proposed method with a constrained search range reduces the time consumption while providing better rice seedling segmentation performance compared with the Otsu and NVE methods. In addition, an automated rice transplanter usually moves at speeds of 1-2 m/s. In this regard, each image must be processed with time below 1.8 s and the segmentation consumes below the 20% of total time. For images of size 320 \(\times\) 237, 720 \(\times\) 576, 1327 \(\times\) 1000 and 2560 \(\times\) 1920, the processing time of obtaining 2 Cg-Cb-Cr images is 0.010851 s, 0.3875 s, 0.104278 s and  
Figure 12: ME (Misclassification Error) comparison of the proposed method with the other two methods.  
0.391722 s, respectively. To reduce the computational load, the image size shrank from the original size of 2560 \(\times\) 1920 to 320 \(\times\) 237 pixels in our experiment. According to Table 1, the proposed method can be applied to real-time applications.
# Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving
## 4 Methods
### Trajectory Frame Transform  
We need a transformation method between two frames to convert trajectories from the Cartesian frame to the Frenet frame and vice versa. To compute the coordinate in the Frenet frame, the reference line is needed, and it is represented as a polyline. First, we find the nearest point on the reference line to the current position in the current frame and then perform the coordinate transform. The frame transform steps are as follows:  
1. Find the closest point \(l_{\text{current}}(i)\) to the \(p_{\tau}(j)\) on the reference line in the current frame, where \(l_{\text{current}}(i)\) is position of the \(i\)th point on the reference line in current frame and \(p_{\tau}(j)\) is position of the \(j\)th point on the trajectory.
2. Calculate the difference between that \(p_{\tau}(j)\) and \(l_{\text{current}}(i)\): \[[\Delta x_{j},\Delta y_{j}]=p_{\tau}(j)-l_{\text{current}}(i)\]
3. Compute the angle \(\phi_{l}\) between the \(l_{\text{cartesian}}(i+1)\) and the \(l_{\text{cartesian}}(i)\), then rotate \((\Delta x,\Delta y)\) by \(\phi_{l}\): \[\begin{bmatrix}\Delta x_{j}^{\prime}\\ \Delta y_{j}^{\prime}\end{bmatrix}=\begin{bmatrix}\cos\phi_{l}&-\sin\phi_{l} \\ \sin\phi_{l}&\cos\phi_{l}\end{bmatrix}\begin{bmatrix}\Delta x_{j}\\ \Delta y_{j}\end{bmatrix},\] where \(l_{\text{cartesian}}\) is the reference line in the Cartesian frame. If transforming trajectory from the Cartesian frame to the Frenet frame, then the sign of \(\phi_{l}\) is negative.
4. \(p_{\tau}(j)\) is transformed into the target frame by adding the rotated points \((\Delta x_{j}^{\prime},\Delta y_{j}^{\prime})\) to the \(l_{\text{target}}(i)\), where \(l_{\text{target}}(i)\) is position of the i-th point on the reference line in the target frame: \[p_{\tau}(j)=l_{\text{target}}(i)+(\Delta x_{j}^{\prime},\Delta y_{j}^{\prime})\] Trajectories in the Cartesian frame are transformed into the Frenet frame by repeating these steps iteratively, and vice versa.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
###### Abstract
[17] classified and reviewed the existing detection algorithms of concept drift adaptation methods. However, to the best of our knowledge, there is no detailed introduction and summary of concept drift adaptation methods under the deep learning framework. Therefore, this paper explores the important role of concept drift adaptation methods under the deep learning framework. The main contributions of this paper can be summarized as follows:  
1. We review concept drift adaptation methods under the deep learning framework from four aspects--discriminative learning, generative learning, hybrid learning, and relevant others--so as to fill the gap in this area of investigation in previous work. 2. We reveal the general operation process of concept drift adaptive methods under deep learning frameworks and explain concept drift detection modes and update modes in detail. 3. We summarize the representative algorithms of each subcategory, common datasets, evaluation metrics, their application areas, and limitations. 4. We analyze and discuss the current problems of concept drift adaption methods and point out the future direction. The rest of the paper is structured as follows. Section 2 provides the definition, causes, and types of concept drift and introduces the process of concept drift adaptation methods under the deep learning framework. Section 3 classifies concept drift adaptation methods based on deep learning and reviews the existing methods in the literature. Section 4 summarizes the common datasets and evaluation metrics. Section 5 provides future research, and Section 6 concludes this paper.
## 2 HY-2B/2C/2D Spacecraft
### Attitude Data  
The attitude of the HY-2B/2C/2D satellites plays a critical role in precise orbit determination (POD) as it directly affects the transformation from the satellite coordinate system to the orbital coordinate system. This transformation is crucial for correctly processing GPS observational data and applying phase center variation (PCV) corrections. When the satellite is in the zero attitude, the orbital coordinate system coincides with the satellite coordinate system.  
The satellite attitude is represented by three Euler angles [23], namely yaw angle, roll angle, and pitch angle around the X, Y, and Z axes shown in Figure 1. The rotation matrix is calculated from the Euler angles to obtain the transformation matrix from the satellite coordinate system to the orbital coordinate system, and the rotation matrix is shown in Equation (1):  
\[R=\begin{bmatrix}\cos\varphi\cos\beta&\sin\theta\sin\varphi\sin\beta-\cos\theta \sin\varphi&\cos\theta\sin\varphi\cos\beta+\sin\theta\sin\beta\\ \cos\varphi\sin\beta&\sin\theta\sin\varphi\cos\beta+\cos\theta\cos\beta&\cos \theta\sin\varphi\sin\beta-\sin\theta\cos\varphi\\ -\sin\varphi&\sin\theta\cos\varphi&\cos\theta\cos\beta\end{bmatrix} \tag{1}\]  
where \(R\) is conversion matrix, and \(\varphi\), \(\beta\), and \(\theta\) are the yaw angle, roll angle, and pitch angle, respectively.  
To gain a more intuitive understanding of the relationship between the satellite coordinate system and the orbital coordinate system (attitude), we have depicted attitude figures for the HY-2B/2C/2D satellites, as shown in Figure 2. The figure shows the attitude angles (sampling interval 1 s) of the 2020 DOY350 HY-2B/2C satellite and the 2021 DOY263 HY-2D satellite. It can be seen that the attitude of the HY-2B satellite shows limited variations, and the yaw angle of the HY-2C satellite is large but stable. However, the yaw angle of the HY-2D satellite is large and exhibits a variation of 70\({}^{\circ}\). It is important to note that the HY-2B/2C/2D satellites maintain non-zero attitudes during their operation. Consequently, the attitude data are crucial for the conversion from the satellite coordinate system to the orbital coordinate system.  
Figure 1: Satellite and its payloads.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 2 Background and Related Work
### Multihoming  
In multihoming [9], a network is connected to multiple providers and uses its own range of addresses. The edge routers of the network communicate with the providers usinga dynamic routing protocol, typically BGP. BGP announces its own network address range to all providers. If one of the links fails, the protocol recognizes the failure and re-configures its routing tables. Multihoming requires the use of an address space that is accepted by all providers and causes an increase in the global routing table. Although multihoming provides a way to connect multiple providers, it is not a multi-path routing but rather an SP routing because we cannot immediately select the connection to the providers for every communication.
# Development of Compact and Robust Physical System for Strontium Optical Lattice Clock
## 2 The Compact and Robust Physical System
To improve the anti-vibration capability of the system, we applied 2216-type epoxy adhesive to all screws for further reinforcement of the optical components. Furthermore, to prevent the influence of complex magnetic field environments on the trapping and detection of atoms, we designed a magnetic shielding box made of permalloy. It measures 342 mm \(\times\) 262 mm \(\times\) 254 mm with a wall thickness of 1 mm. The shield was installed outside the MOT chamber, as shown in Figure 2a, and its shielding effectiveness was evaluated using a CH-hall Electronic Devices Company Model 3600 Gaussmeter. The results indicated an approximate 20 dB attenuation in the magnetic field strength at the center of the MOT chamber. Due to the structural design constraints, we cannot install the ion-gettering pump outside the magnetic shielding box. We measured the magnetic field generated by the ion-gettering pump in operation and found that its field strength was very weak. Moreover, the field was stable, and we could eliminate its interference with the clock detection by compensating the field. The effects of the magnetic shielding box on the optical clock experiment will be thoroughly discussed in the \"environmental testing\" section of this manuscript. To reduce the weight of the physics package, mechanical analyses and iterative designs were conducted. The final frame support structure featured large area cutouts and stiffening ribs, as shown in Figure 4. The frame support structure was sustained by aluminum alloy support beams on both long sides to minimize elastic deformation. The physical package was connected to the cold plate through multiple side support beams to reinforce the rigidity of the entire physical system, as illustrated in Figure 1. In summary, engineered modifications have been applied to the physical package in terms of size, weight, power consumption, vacuum, magnetic field, and vibration resistance, making it applicable to a wider range of situations. Figure 3: (**a**) Schematic of the customized integrated optical setups. (**b**) Exploded view of the probe beam optical setup.
## 1 Introduction
### Modified Electrode Types
#### 1.1.2 Carbon Nanotube Electrodes [39]  
Carbon nanotube (CNT) electrodes are electrodes that are made by coating a conductive substrate with a layer of carbon nanotubes. Carbon nanotubes are small, tube-like structures made of carbon atoms that are arranged in a hexagonal lattice. They have a high aspect ratio (length-to-diameter ratio) and a high surface area, which makes them highly conductive and gives them unique electrical and mechanical properties.  
Carbon nanotube electrodes are often used in electroanalytical chemistry, where they are used to measure the concentration of various species in solution. They are also used in sensors, fuel cells, and other electrochemical devices. One advantage of carbon nanotube electrodes is that they have a high surface area, which can enhance the sensitivity of the electrode. They are also highly stable and resistant to fouling, which makes them useful for long-term measurements. However, they can be difficult to fabricate and may be expensive to produce on a large scale.  
[PERSON]'s group, for example, has reported several CNT-based DNA electrochemical sensors for the determination of sequence-specific DNA over the last few years [40]. Improved hybridization responses of covalent DNA immobilized on CNT electrodes were also observed by [PERSON] et al. [41] and [PERSON] et al. [42] using methylene blue and Escherichia coli single-strand binding protein (SSB) as the indicators, respectively.
## 3 Methodology
### Enhancing Model Explainability Through SHAP  
In this study, the SHAP (SHapley Additive eXplanations) technique, rooted in the principles of game theory, is employed to gain profound insights into the class outputs predicted by ML classifiers. The adoption of the SHAP technique facilitates a comprehensive understanding of model predictions by computing feature importance scores. Unlike conventional approaches that evaluate individual features in isolation, SHAP leverages the theoretical underpinnings of Shapley values from game theory. The SHAP method is unique because it carefully examines how each feature contributes to a prediction by considering all possible combinations of features. Unlike just looking at features on their own, this method gives a complete view of how features work together in the model's decision-making. Thus, SHAP provides a sophisticated and easy-to-understand way to figure out the detailed dynamics behind the predictions made by ML models [84]. The SHAP technique has been instrumental in improving the clarity of spatial predictive models. Its impact has been documented in various applications, including modeling landslides [38; 85; 86], floods [35; 87], earthquakes and soil liquefaction [88; 89], wild-fires [31; 74; 90], snow avalanches [69; 91], and erosion [92].  
In essence, the Shapley value [93], as expressed in Equation (5), quantifies the importance of a feature \(i\) within the context of a broader set of features.  
\[\phi_{i}=\frac{1}{|N|!\sum_{S\subseteq N\{i\}}\frac{|S|!(|N|-|S|-1)!}{N}[f(S \cup\{i\})-f(S)]} \tag{5}\]  
The Shapley value (\(\phi_{i}\)) for a feature \(i\) is derived by evaluating all feature subsets (\(S\)) within the complete set (\(N\)) and determining the feature's significance through its average marginal contribution across every possible sequence of feature combinations. Mathematically, this involves averaging the difference in the model's prediction when feature \(i\) is included versus excluded from a subset \(S\), weighted by the probability of \(S\) occurring. This approach inherently accounts for the interdependencies and ordering of features, which is critical when addressing correlated predictors, as the sequence of feature inclusion can alter the model's behavior [94].  
In this study, the SHAP framework was applied via the Python-based SHAP library to explain ML models. The library provides robust analytical tools, including intuitive visualizations to elucidate how individual features influence predictions, thereby enhancing transparency in model decision-making [64].
## 6 Evaluation, Benefits, and Limitations of GA-Based Routing
### Benefits of GAs in Electric and Flying Vehicle Routing  
GAs offer several benefits when applied to the optimization of message routing in EnFVs. These advantages stem from the unique characteristics of GAs, such as their ability to handle multi-objective optimization problems and their capacity to explore a wide range of potential solutions [75]. Firstly, GAs excel in handling multiple conflicting objectives in routing optimization for EnFVs [76]. In these complex transportation systems, the objectives may include minimizing travel time, maximizing energy efficiency, considering environmental factors, and ensuring passenger comfort. Traditional routing algorithms often struggle to effectively balance these objectives. However, GAs leverage the concept of Pareto dominance and non-dominated sorting to generate a diverse set of solutions, known as Pareto-optimal solutions. These solutions represent trade-offs between the different objectives, allowing decision makers to choose the most suitable routing strategies based on their preferences and priorities. Moreover, GAs enable the exploration of a large solution space, providing a more comprehensive search for optimal or near-optimal solutions [31]. The use of crossover and mutation operators facilitates the creation of new routes by combining and modifying existing ones, introducing diversity and preventing premature convergence to suboptimal solutions. This feature is particularly advantageous in dynamic transportation environments, where conditions such as traffic congestion, charging station availability, and flight restrictions may change over time. By adaptively updating and refining the routing solutions, genetic algorithms can respond to real-time data and ensure the resilience and efficiency of the routing strategy. Furthermore, GAs offer flexibility in the representation of routes, allowing the incorporation of various constraints and considerations specific to EnFVs [34, 77]. For example, the representation can include information about charging station locations, battery capacity, vehicle range, flight restrictions, and other relevant factors. This flexibility allows the optimization process to consider these vehicle-specific requirements, leading to more realistic and effective routing solutions.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns
#### 4.3.1 Artificial Loss (PT10)  
Pattern  
Hidden information is encoded by artificially suppressing certain PDUs. To utilize this pattern in the PDCP, the covert sender continuously monitors the sequence numbers of the next PDUs to be transmitted. Subsequently, the covert data are encoded by forwarding or discarding the PDUs. The covert receiver monitors incoming PDUs and the corresponding sequence numbers. Finally, covert data can be extracted by comparing the received sequence numbers with the expected numbers. With this pattern, the covert bits are therefore transferred via the artificial loss of PDCP sequence numbers.
## 5 Discussion  
The outcomes of this study show that combining causal models with neural networks could help players make better strategic decisions in _StarCraft II_. The \(1.1\%\) rise in accuracy and F1-score seen when causal relationships were added (Table 4) shows how important it is to use both explicit knowledge and data-driven machine learning together. Although this increase is modest in absolute terms, it is significant in a highly competitive domain like real-time strategy games, where small advantages can translate into substantial differences in overall performance.  
A key finding is the hybrid model's ability to recommend adaptive strategies based on available resources (Figure 6). The sensitivity matrix reveals that the system dynamically adjusts its recommendations toward defensive strategies when the player is at a resource  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Aspect** & **Baseline Study** & **Our Work** \\ \hline Goal & League classification & Strategy recommendation \\ Data & 46,398 raw replays & 100 matches + synthetic \\ Features & APM, camera switches & Resource ratios, unit counts \\ Model & Random Forest & Causal-DNN hybrid \\ Performance & 75.3\% accuracy & 96.3\% F1-score \\ Latency & Batch processing & 5.94 ms (real-time) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Methodological comparison with baseline study.  
disadvantage and toward aggressive strategies when they have an advantage. This suggests that the model learns complex patterns from the data and incorporates fundamental tactical principles, such as efficient resource management and environmental adaptation.  
The confusion matrix (Figure 7), on the other hand, shows that it is not always possible to find edge cases, especially when there are false positives and false negatives. The imbalanced nature of the original data, which prioritized offensive strategies _(attack)_ in 85% of matches, underrepresents defensive scenarios. Future work should address this bias through class-balancing techniques or by generating additional synthetic data to better represent defensive strategies.  
Another area for improvement is model interpretability. Although 75% of the recommendations were consistent with explicit causal rules, there is still room to increase verifiable strategic coherence. This goal could be achieved by refining the causal model or incorporating feedback from human experts to validate the system's decisions.  
Finally, the results obtained are specific to the Terran race, raising questions about the model's generalization to other races (_Zerg and Protoss_). Future research should evaluate whether the proposed hybrid approach is equally effective in more diverse strategic contexts.  
Basically, this study shows that adding causal models to neural networks can make recommendation systems work better and be easier to understand in environments that are always changing. However, significant challenges remain, particularly in terms of data balance, interpretability, and generalization.
## 4 Numerical Application
### Robustness Analysis
#### 4.3.1 Method Applicability  
To evaluate the applicability of the FAFWI method to complex media, the 2007 BP TTI P-wave velocity model was selected as the test case. The initial velocity model was set as a homogeneous medium with a uniform velocity of 1900 m/s at all the grid points.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 3 Proposed AVSR Method Based on DCM Attention
### Seq2 seq Transformer
#### 3.2.2 Self-Attention Encoder  
Two encoders and one decoder consist of stacks of multi-head attention layers. As shown in Figure 2, we use 6 encoder blocks for each modality and each block consists of a multi-head self-attention layer and two feed-forward linear layers that generate 2048 and 512 outputs. Using input data as queries \(Q\), keys \(K\), and values \(V\), the multi-head self-attention has 512-D features with eight  
Figure 2: Encoder structures. Each structure generates audio or video features using a multi-head self attention followed by two feed-forward layers (FC 1 and FC 2). The number of encoder blocks, \(N\), is six in these structures. (**a**) Video encoder; (**b**) audio encoder.  
heads. Like (1), each modality encoder generates a high-level representation \(\mathbf{h}^{a}\) or \(\mathbf{h}^{v}\), for input \(\mathbf{x}^{a}\) or \(\mathbf{x}^{v}\) after applying the VGG-M-based network [39], respectively, which is expressed as  
\[\mathbf{h}^{a} =\text{AudioEncoder}\left(\text{VGG-M}\left(\mathbf{x}^{a}\right) \right),\] \[\mathbf{h}^{v} =\text{VideoEncoder}\left(\text{VGG-M}\left(\mathbf{x}^{v}\right) \right), \tag{15}\]  
where AudioEncoder and VideoEncoder denote stacked self-attention encoders for audio and video modalities, respectively.
## 2 Design Methodology
### Analysis of Filtering Variation with Angle of Incidence  
The influence of the inclination of the FSS structure is also studied in simulation, as shown in Figure 6. The structure is illuminated with a plane wave at normal incidence along the z-axis that changes from 0\({}^{\circ}\) to 60\({}^{\circ}\).  
Figure 6 reveals that the resonant frequency remains at 2 GHz, and the bandwidth is quite insensitive to variations of the incident angle theta from 0\({}^{\circ}\) to 60\({}^{\circ}\) on the horizontal xz plane (see coordinate system of Figure 3). However, the second frequency exhibits a change in response and proves to be sensitive to angle variation.