esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Model Building  
The optimal configuration of the prediction model based on CNN, GRU, and multi-head attention is illustrated in Figure 3, and specific parameters are provided in Table 1.  
Figure 3: Structure of SwFB.  
Our server system runs on Ubuntu 18.04.5 LTS, equipped with NVIDIA GeForce RTX 2080 Ti GPUs \(\times\) 8 (NVIDIA, Santa Clara, CA, USA) and an Intel(R) Xeon(R) Gold 6226R CPU @ 2.90 GHz (Intel, Santa Clara, CA, USA). We established our model with Python 3.7.16 and PyTorch 1.13.1. The optimizer employed is Adam, with a learning rate ranging from \(1\times 10^{-5}\) to \(1\times 10^{-4}\) during the model training. We used a warm-up strategy, where the warm-up steps account for 10% of the total steps. Considering varying aspect ratios in the original images, we used an input resolution of 960 \(\times\) 1280 on the COFIE dataset, and 1280 \(\times\) 960 on the CORD and SROIE datasets.
# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 4 Approach
### Overview  
An overview of the process of this research is given below.  
* Training: The stacked autoencoder is sufficient to extract features and reconstruct. However, there is a need to find the structure of the model that is the most efficient. The stacked autoencoder models are trained to classify the features of input data, while the Adam optimizer is utilized as the loss function. In the training process, five structures, which are composed of depth and size, were trained by latent layer values (3, 6, 9, and 12).
* Selection of models for comparison: After completing the training process, the true positive rate (TPR), false positive rate (FPR), accuracy, and Matthews correlation coefficient (MCC) were used to select the best performance model for each structure. Thus, only five models were kept for a final comparison. Trained models classified ships as being in one of three states: In Port, Stand By, and At Sea. In this process, the MCC was considered to be the most important of all the evaluation metrics.
* Performance comparison: Five models from the previous step of the process were compared. For comparison, we used the same evaluation metrics and a confusion matrix. The confusion matrix is a tabular summary for users to check precisely the correct and incorrect results made by classification models. By using this technique, the performance of the classification models was analyzed in detail.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 4 Development Trend and Key Technology of ISAC towards 6G ISTNs
### Future Application Scenario
#### 4.1.4 ISAC towards ISTNs  
In the future, ISTNs will mainly consist of three layers, which are the satellite network, air network, and terrestrial and oceanic networks. Some terrestrial users fail to establish a connection with the satellite network due to signal fading in the satellite-terrestrial link and differences in communication protocols. In that case, the air network can extend connectivity outside the BS coverage area.  
To implement ISAC towards ISTNs, ISAC technology will be integrated into the three-layer space-air-ground communication network. Firstly, ISAC technology will be integrated into the satellite terminal to facilitate satellite networking and management, while also enhancing space environment sensing. Additionally, it will enable sensing and imaging of the ground environment using LEO satellites [21]. At the same time, the air network integrates ISAC technology to effectively sense ground user targets and acquire the motion information of vehicles. By leveraging the cooperative capabilities of UAVs, these aerial platforms can effectively operate as mobile antenna arrays. This collaborative arrangement enables the achievement of high-resolution imaging capabilities through synchronized movements and coordinated signal processing techniques in all types of weather, day or night [20]. Moreover, their flexibility makes them instrumental in controlling smart cities [102]. Ultimately, the general ISAC technology is deployed at the ground terminal to facilitate user target localization and management of resources.  
The overall network structure is shown in Figure 5. In this network, each device senses and communicates with each other, establishing an integrated space-air-ground-sea network for the future.  
Figure 5: 6G ISAC towards space–air–ground–sea networks.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology
### Data Preprocessing  
All time series were transformed into percentage changes or growth rates, where appropriate, to ensure stationarity and comparability. Missing values were handled using mean imputation, where the missing entries were replaced with the mean of the respective variable over the training period. The proportion of missing data was below 2% for all variables, and no extended sequences of missing values were observed. Since the dataset is monthly and time-ordered, temporal consistency was taken into account by ensuring that imputation was performed only within the training set to avoid data leakage. Additionally, standardization was applied to all input variables to normalize feature scales, which is essential for optimizing multilayer perceptron (MLP) performance [7].  
The dataset was split into a training set (80%) and a test set (20%), preserving temporal order to avoid data leakage. This split was chosen to ensure that the models were trained on a sufficiently large portion of the historical data while retaining enough out-of-sample data for robust performance evaluation. The 80/20 division aligns with common practice in the time-series forecasting literature and balances the need for learning temporal patterns with the ability to assess generalization across future periods.
## 2 Related Work
### Attention Mechanism  
The attention mechanism is derived from human visual features [42; 43; 44; 45]. Several studies have demonstrated that incorporating attention modules during feature extraction can enhance network performance [46; 47; 48]. This mechanism encompasses lightweight modules that focus on feature space and channels, as well as self-attention mechanisms involving constant calculations of query (Q), key (K), and value (V) matrices [42]. For instance, in the Attention-Unet network [49], the authors introduced an Attention Gate module to the feature fusion of skip connections, resulting in improved segmentation accuracy. However, with the continuous advancement of attention mechanism, although the novel attention modules can enhance the segmentation accuracy in models, they also introduce a significant increase in parameter count. In their study, [PERSON] et al. [50] explored the integration of various attention models into the U-Net network and conducted experiments. While achieving an improvement of 4-5% in segmentation accuracy, the augmented number of parameters due to attention models emerged as a new challenge, thereby directing their future efforts towards further enhancements.  
Figure 3: Remote sensing image and annotation of the Qinghai–Tibet Plateau Lake datasets.  
To address the issue of increasing model parameters due to attention, we propose a lightweight and efficient attention module for water body extraction models. This module utilizes a global normalized response (GRN) operation to fuse the spatial feature information and significantly reduces the parameter count through depth separable convolution. Unlike previous attention modules that directly process features, our approach enables learning of attention based on the fusion of feature heights, thereby enhancing image feature learning efficiency. In terms of performance, our proposed attention module demonstrates excellent segmentation capabilities in detecting small water bodies and lake edges in remote sensing images.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.6 Summary and Future Direction  
Based on both the advantages and disadvantages of RS systems for iceberg mapping and monitoring, several strategies can be considered in future research to enhance iceberg studies. In this regard, synergistic use of RS systems could help in obtaining results with a higher confidence [10]. Additionally, multi-source RS systems resolve the revisit time limitation and provide further opportunities for iceberg detection [28]. Furthermore, the development of RS systems with more advanced specifications (e.g., higher spatial and temporal resolutions) would benefit iceberg studies [29]. For example, developing SAR systems with higher penetration capability (e.g., L-band SAR systems) can considerably facilitate iceberg detection and relevant parameter estimation [29]. Finally, the availability of a huge volume of RS data requires more sophisticated data mining and processing algorithms (e.g., Deep Learning (DL)) and big data processing platforms (e.g., Google Earth Engine (GEE)) to exploit the full potential of RS data for iceberg studies [30; 31; 32].
## 1 Introduction
Recent studies have applied Flow to evaluate the effectiveness of an automated vehicle (AV) in a network [38; 39] and reduce the frequency and magnitude of formed waves with AV penetration rates [40]. The experimental results showed that the multi-agents RL policy outperformed according to average velocity and rewards. In addition, the high average velocity leads to reduce the delay time, fuel consumption, and emissions. Thus, the average velocity has become an effective metric to train a deep RL policy in the real world. In this study, we present a deep RL method for simulating mixed-autonomy traffic at a non-signalized intersection. Our proposed method combines RL and multilayer perceptron (MLP) algorithms and considers the effectiveness of the leading autonomous vehicles. In addition, we apply a set of PPO hyperparameters to enhance the simulation's performance. First, we perform a leading autonomous vehicle experiment at a non-signalized intersection with a varying AV penetration rate that ranges from 10% to 100% in 10% increments. Second, we input the PPO hyperparameters into the MLP algorithm for the leading autonomous vehicle experiment. Finally, human-driven leading vehicle and all human-driven vehicle experiments are used to evaluate the superiority of the proposed method. The major contributions of this work are as follows. * An enhanced hybrid deep RL method is presented that uses a PPO algorithm through MLP and RL models in order to consider the effectiveness of the leading autonomous vehicle experiment at a non-signalized intersection based on an AV penetration rate that ranges from 10% to 100% in 10% increments. The leading autonomous vehicle experiment yields a significant improvement when compared with the leading human-driven vehicle and all human-driven vehicle experiments in terms of training policy, mobility, and energy efficiency. * A set of PPO hyperparameters is proposed in order to explore the effect of the automated extraction feature on policy prediction and to obtain reliable simulation performance at a non-signalized intersection within the real traffic volume. * The demonstration of a significant improvement in traffic perturbations at a non-signalized intersection is based on an AV penetration rate that ranges from 10% to 100% in 10% increments. The rest of this paper is organized as follows. Section 2 presents the deep RL framework, the longitudinal dynamic models, the policy optimization method, and the proposed model's architecture. Section 3 describes the simulation experiments and presents the results. Section 4 contains our conclusions.
# Measurement of Wafer Focus by Grating Shearing Interferometry
## 1 Introduction  
The wafer focusing technique (WFT) [1; 2] is a pivotal section in optical lithography instruments (OLIs) [3], which affects the image quality and exposure effect [4]. In traditional OLIs, charge coupled device (CCD) sensors [5] and position sensitive detectors (PSDs) [6] are applied and wafer defocus with micron precision is measured by analyzing the image removement of the gap in the WFT system based on a triangle method.  
With the improvement of the exposure field of view (FOV) and resolution in new-fashioned OLI [7], the WFT needs higher measurement precision and thus the WFT is widely investigated [8; 9]. Based on the Talbot-moire effect [10] and interferometric spatial-phase imaging [11], a diffractive grating [12; 13] is applied for wafer defocus measurement to improve the precision; another technique based on a probe beam scanner [14; 15] is also introduced. By complicated analyzation and calculation, these two WFTs have nanometer measurement precision.  
To improving the measurement efficiency of WFT with nanometer measurement precision, a simple WFT based on grating shearing interferometry is proposed in this paper. The defocus of the wafer is immediately detected by measuring the phase difference in the interference pattern at meridian position through the measurement system, which is simply composed of a diffractive grating and a CCD sensor. Like traditional grating interferometry [16; 17], the advantages of grating shearing interferometry are its high precision and simple setup, while its calculation is complex. This paper proposes a spatial domain phase shift technique for calculating the phase difference in the interferometry fringe between the focus and defocus positions of the wafer.  
In summary, a novel WFT based on grating shearing interferometry and a spatial domain phase shift technique is introduced to improve the measuring precision and efficiency. An experiment is carried out to verify the feasibility and calibrate the measurement precision of this technology in this paper.
## 3 Distributed Jamming System Model for LEO Signal
### Distributed Interference Optimisation Implementation Process  
In order to optimise the jamming effectiveness, multi-dimensional strategies in the air, time, frequency, and power domains need to be considered to ensure that multiple jammers can achieve efficient co-operation. Figure 2 represents the block diagram of the distributed interference optimisation algorithm flow. In the spatial domain, dynamic determination of the primary and relative jammers is achieved by calculating the minimum reception distance of the corresponding LEO satellite. The primary jammers perform a hybrid decision game to determine the set of secondary jamming source strategies to be used for relaying LEO satellite jamming signals. The multi-dimensional information optimisation method is used to construct the strategy payoff function by combining information from the time, frequency, and power domains. Assigning an unknown probability to each strategy, such that the expected payoffs from all primary sources of interference are equal, constructs the game equation. The equation is solved with the help of the mathematical iterative method to obtain the probability value of the strategy when the number of iterations is greater than N\({}_{0}\). This strategy probability corresponds to a Nash equilibrium point. The pseudo-random algorithm is constructed by strategy probability.  
Figure 1: Distributed interference scenario model.  
Each primary jammer plays the game several times, with the help of a pseudo-random algorithm, to filter out the optimal set of relative jammer strategies under the Nash equilibrium. The primary jammer receives the LEO signals from the area to be jammed and processes the signals, including down-conversion, code stream extraction, etc., modulates them into a special frequency band, and broadcasts them to the relative jammers. The relative jammers receive the jamming command and jamming LEO information from the primary jammers, adds time delay and up-conversion to the signal, and then broadcasts it to the jamming target through the directional antenna to complete the jamming of the jamming target.
## 4 Experimental Results and Discussion
### Effect of Ambient Temperature on the Electrical Efficiency of the PV System  
In this section, the electrical efficiency was determined numerically for 30 and 40 (\({}^{\circ}\)C) ambient temperature. According to Figure 11, the ambient temperature of the studied locations does not exceed 40 (\({}^{\circ}\)C). It can be considered that the losses \(Q_{convPV}\) and \(Q_{radPV}\) remain constant if the solar irradiance remains constant. According to [PERSON] et al. [61], if the solar irradiance remains constant, the temperature of the \(PV\) system increases in the same proportion as the ambient temperature. Figures 21 and 22 show the electrical efficiency for ambient temperatures of 30 and 40 (\({}^{\circ}\)C), respectively.  
Figures 21 and 22 suggest the following conclusions as concerns the electrical efficiency:  
1. The electrical efficiency obviously decreases as
## 4 Simulation Verification
### Verification of Power Coordinated Control
In the simulation, the capacitor voltage setting values of each sub-module of the input stage and the AC output port are 3.75 kV and 1.25 kV, respectively. It can be seen from Figure 15a, the capacitor voltage fluctuation of each sub-module of the input stage is about \(\pm\) 120 V, and the ripple coefficient is about 3.2%. Figure 15b illustrates that the capacitor voltage fluctuation of each sub-module of the AC output port is about \(\pm\) 50 V, and the ripple coefficient is about 4%. The results indicate that the MMCs operate steadily and the capacitor voltage of the sub-modules are well balanced under the control strategy used in this paper. It can be demonstrated from the above analysis that when the power demand of the output stage changes, PET can quickly and accurately coordinate the power distribution under the proposed power coordinated control strategy, thereby ensuring stable and reliable operatio  
Figure 14: Current waveform of the DC output port. Figure 15: Capacitor voltage of the sub-modules of (**a**) the input stage and (**b**) AC output port. distribution network. The input stage adopts the VSM control strategy, which increases the friendliness to the upper grid and provides reactive power support if necessary. It can also be concluded that the PET can ensure the power quality of each port and effectively reduce the harmonic pollution to the grid.
## 6 The Case for Cross-Layer Design
### Methodology
#### Layering as Optimization Decomposition  
This method of cross-layer design effectively dispenses with the traditional protocol stack and replaces it with a completely new architecture. Layering as optimization decomposition is a concept that effectively decouples the traditional protocol stack and provides a framework for cross-layer design [78]. In this method the traditional protocol stack is dispensed with and replaced by a single integrated solution. The research by [PERSON] _et al._[78] positions this theory as carrying out an asynchronous distributed computation over the network to implicitly solve a global optimization problem modeling the network. The theory exposes the interconnections between layers as different ways to modularize and distribute a centralized computation. Separate vertical decompositions (functions) of an optimization problem in the form of a generalized network utility maximization (NUM) are mapped to a layer or the interfaces between specific layers. The NUM can take the form of vertical or horizontal decomposition. _Vertical decomposition:_ entails the decoupling of the protocol stack where network protocol functions such as energy detection, congestion management, routing, error control etc are coordinated as part of modules that perform the same functions as the separate layers. _Horizontal decomposition:_ can be carried out to further optimize and control the functional layer interaction. The net effect of this is to decompose the optimization problem into sub-problems each associated or mapped to a specific protocol layer with defined functions managing the interfaces between the layers. Layering as Optimization Decomposition presents a \"clean sheet\" approach to the design of a protocol stack. [PERSON] _et al._ positions the theory as being outside of and separate to theexisting research on cross-layer design. However, Layering as Optimization Decomposition does not supersede the need for cross-layer design. The decomposition process involves the development of modules that retain coupling between layers and could be seen as justifying the requirement for cross-layer design [79]. The need for cross-layer design solutions is reinforced in cases of architecture mismatch between an existing protocol stack and an optimal decomposition method. Layering as Optimization Decomposition provides a top down approach to designing network architecture from first principles. The resulting conceptual simplicity stands in contrast to the ever increasing complexity of communication networks [80]. Although the case for Layering as Optimization Decomposition is still to be fully made in terms of its practical application, it can provide benefits for the modeling of a decomposition scenario in providing a direct comparison to an existing protocol stack identifying which layers to integrate.