esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation
## 3 Background
### Partially Observable Markov Games  
In this paper, agents need to cooperate with each other to complete different tasks in partially observable environments, which are considered as partially observable Markov games that are an extension of Markov games [23]. They are defined by environment state \(S^{t}\), action spaces \(A^{t}{=}\big{[}a_{1}^{t},\ldots,a_{N}^{t}\big{]}\) where \(N\) is the number of agents, \(a_{i}^{t}\) is the action of agent \(i\) at time \(t\), and observation spaces \(O^{t}=\big{[}o_{1}^{t},\ldots,o_{N}^{t}\big{]}\). Each agent \(i\) learns a policy \(\pi^{i}:o_{i}^{t}\to P_{a}\big{(}a_{i}^{t}\big{)}\), which maps each agent's observation to a distribution over its set of actions. Then, the next states are produced according to the transition function \(T:S^{t}\times a_{1}^{t}\times\cdots\times a_{N}^{t}\to P_{t}(S^{\prime})\). Each agent \(i\) obtains rewards \(r_{i}\) as a function of the state space and action spaces: \(S^{t}\times a_{1}^{t}\times\ldots\times a_{N}^{t}\to\mathbb{R}\). The goal of each agent is to maximize following expected discounted returns with the policy \(\pi^{i}\):  
\[J_{i}(\pi^{i})=E_{a_{1}\sim\pi^{1},\ldots,a_{N}\sim\pi^{N},\rho\sim T}\Big{[} \sum_{t=0}^{\infty}\gamma^{t}r_{i}^{t}\big{(}S^{t},a_{1}^{t},\ldots,a_{N}^{t} \big{)}\Big{]} \tag{1}\]  
where \(E[\cdot]\) represents the expectation. \(r_{i}^{t}\) represents the reward that agent \(i\) obtains at time \(t\) and \(\gamma\in[0,1]\) denotes the discount factor determining the importance of future rewards. If \(\gamma=0\), the agent will be completely myopic and only learn about actions that produce an immediate reward. If \(\gamma=1\), the agent will evaluate each of its actions based on the sum total of all of its future rewards.
## 5 Discussion
### Wound Segmentation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wound** & & & & & & \\
**Type** & **Accuracy\%** & **Precision\%** & **Recall\%** & **F1\%** & **F2\%** & **AUC\%** \\
**Model** & & & & & & \\ \hline CW & \(91.8\pm 1.4\) & \(36.0\pm 4.3\) & \(71.4\pm 5.4\) & \(47.8\pm 4.7\) & \(59.7\pm 5.0\) & \(81.8\pm 7.0\) \\ DW & \(91.0\pm 2.5\) & \(33.2\pm 6.6\) & \(68.4\pm 7.4\) & \(44.7\pm 6.7\) & \(56.4\pm 7.2\) & \(69.4\pm 13.2\) \\ LW & \(87.2\pm 2.4\) & \(52.6\pm 4.0\Regarding the wound segmentation model, since the IoU metric penalizes the badly classified instances harder, it is the best metric to evaluate the proposed system along with the final mean average precision. It is vital not to have bad segmentation results since these can penalize and lead to wrong final classifications, such as FN, which can cause a wound with actual alterations to be overlooked by clinicians. Hence, it can be concluded that the segmentation system achieved a good score and performed well on the proposed task. The final average precision of the segmentation, 90.1%, shows promising results in the pixel-wise classification made by the segmentation model, which is extremely important for dividing the several wounds along the three classifiers. In addition, it indicates that the system seems suitable for our purpose.
# Change and Driving Factor Analysis of Eco-Environment of Typical Lakes in Arid Areas
## 3 Results  
### Temporal and Spatial Variations in Lake Area of Taitema Lake  
According to the data of the Taitema Lake area from 2000 to 2020, the Mann-Kendall test was used to analyze the trend and mutation of the lake area. The test results show that the Taitema Lake area increased in the selected 20 years, passing the 0.05 significance test, indicating a significant increasing trend of the Taitema Lake area.  
From an annual perspective, the inter-annual variation in the area of Taitema Lake was as follows: The area of Taitema Lake was only 1.8 km\({}^{2}\) in 2000 and reached 402.12 km\({}^{2}\) in 2017, which was the largest area in nearly 20 years (Figure 3a). The average lake area over 20 years was 114.8 km\({}^{2}\), with an annual growth rate of 5.58 km\({}^{2}\) a\({}^{-1}\), and the area change was significant (\(p<0.05\)). Based on the position of the intersection of the UF and UB curves, it was determined that the increase in the area of Taitema Lake was a sudden change, specifically starting from around 2002 and 2011 (Figure 3b). The sudden change test of Taitema Lake indicates that there were an extremely significant expansion in 2011 (\(p<0.01\)) and a significant expansion in 2002 (\(p<0.05\)).  
Due to factors such as snow cover and lake ice, this article selected the monthly average area changes in Taitema Lake from May to October for study (Figure 4a). The overall trend of the lake area from May to October was one of increase, which was basically consistent with the annual scale of lake area changes. The annual changes in the Taitema Lake area were as follows: From May to June, the area showed a shrinking trend, decreasing from 211.6 km\({}^{2}\) to 173.3 km\({}^{2}\), and then an overall expanding trend month after month, reaching the maximum value in October. From a monthly scale perspective (Figure 4b), the area of Taitema Lake was 26.79 km\({}^{2}\) in October 2002, and it expanded to 139.71 km\({}^{2}\) by May 2003. During this period, the ecological water conveyance volume increased from 3.31 \(\times\) 10\({}^{8}\) m\({}^{3}\) to 6.25 \(\times\) 10\({}^{8}\) m\({}^{3}\). By August 2009, the area of Taitema Lake shrank to 1.79 km\({}^{2}\), and the corresponding ecological water conveyance volume was 0.11 \(\times\) 10\({}^{8}\) m\({}^{3}\). With the increase in ecological water conveyance volume, the area of Taitema Lake reached 149.95 km\({}^{2}\) in June 2011. The maximum area of Taitema Lake occurred in October 2017, which was also the peak of the selected 20 years in terms of ecological water conveyance volume.  
Figure 4: Annual area change map of Lake Taitema from 2000 to 2020.  
Figure 3: Inter-annual area changes and mutation test chart of Taitema Lake from 2000 to 2020.
## 2 Background
### Patient-Ventillator Asynchrony Detection
PIP has an obvious meaning and is vital to patient safety, as  
Figure 1: Process of respiration [37]. excessive pressure can, in the worst case, rupture a lung. _Pplat_ is the pressure in the airway at zero airflow after inhalation [38]. Patient-ventilator asynchrony (PVA) occurs when the patient's own breathing reflex is at odds with the mechanical ventilator's control timings. In particular, if a patient who is not under deep sedation has not received enough oxygen, then they may continue attempting to inhale even after the ventilator has moved to the breath-hold or even exhalation phase. As a result, the patient might not fully exhale before the next inhalation starts. This is particularly dangerous if the ventilator compensates for low flow with higher pressures. Lung injuries are a frequent outcome in this case. Thus, avoiding or correcting PVA is a key concern. It is, for instance, a key reason why ventilated patients are sedated to suppress the breathing reflex. Modern ventilators also display the real-time waveforms so that medical personnel can check for PVA manually [38]. Clinical experience indicates that there are two principal types of PVA. Double Triggering Asynchrony (DTA) is presented in Figure 2(a), and Breath Stacking Asynchrony (BSA) is presented in Figure 2(b). In both cases, we can see that TVi is considerably greater than TVe, meaning the lungs become hyperinflated [39]. However, the waveforms in Figure 3 are only examples; a number of other patterns would also constitute DTA or BSA. Indeed, visually differentiating DTA and BSA waveforms has been shown to be inaccurate, and so heuristic rules have been adopted to recognize them. Caers first determine whether a breath is normal or abnormal and then apply the heuristics to diagnose a specific PVA. Furthermore, some breaths match these heuristics, but are actually coughs, suctions, or movements; these must be discounted [39]. Given how serious PVA can be, the difficulty in manually differentiating different PVAs and the fact that qualified ICU personnel _cannot_ spend their whole shift looking at a single patient's monitor, automated systems for detecting or even responding to PVA are essential. Most of the existing PVA alarm algorithms use heuristic rules encoded as thresholds on waveforms or features derived from them [40]. However, physiologic manifestations vary from person to person, increasing the false-positive and false-negative alarms rates. There are also some artifacts, such as coughs, suctions, and movements by patients, that can lead to frequent false positive alarms [41], leading to alarm fatigue that can put the patient at greater risk. As a result, some researchers have begun investigating machine learning for detecting PVA from waveform data. We review this literature in Section 3.3. Figure 2: Ventilator flow and pressure waveforms for one breath.
## 2 Engineering Background  
### Project Overview  
This paper relies on the shield project of \"Keyuan Station \(\sim\) Shenzhen University Station\" section of Shenzhen Metro Line 13. The subway tunnel shield project (mileage Z/YDK4 + 562.489-Z/YDK4 + 805.489) is two single-line tunnels repaired separately on the left and right lines with a total length of 243 m, and an average distance between the left and right lines of the tunnel of 11 m, as shown in Figure 1. The shield segment is a single-layer assembled lining with an outer diameter of 6.0 m and an inner diameter of 5.0 m. There are 6 segments exist in each ring with a thickness of 0.5 m each. In the early stage, the right line has been fully penetrated, and the settlement has stabilized. Therefore, this paper only investigates the stratum settlement law during the penetration of the left line shield tunnel and assumes that the right line does not affect the results, see Figure 1a.  
The shield section passes through a complex and changeable stratum. The stratum from top to bottom consists of a plain fill layer, a muddy gravel sand layer, a plastic gravel clay layer, a hard plastic gravel clay layer, and fully weathered biotite granite. The tunnel body is mainly located in hard plastic gravel clay soil. The tunnel mainly passes  
Figure 1: Engineering environment diagram. (**a**) A schematic view of the study site. (**b**) Stratum structure diagram.  
through plastic gravel clay soil, hard plastic gravel clay soil, and fully weathered biotite granite. The average overburden thickness of the shield is 20 m, see Figure 1b. The strength characteristics, compressibility, permeability, and sensitivity of interval soft soil muddy affect the safety of engineering construction and long-term operation and cause great difficulties and risks in the construction and operation of rail transit.
## 3 Results and Discussion
### Deep Neural Network (DNN) Results
After about 200 epochs, the R\({}^{2}\) values reached a plateau point with a value of about 0.93 for the training set and slightly lower for the validation set. The observation of a plateau in the model's performance suggests that the predictive accuracy has reached its optimal point, with minimal improvements in subsequent epochs. The parallel behavior of the training and validation metrics indicates good generalization capabilities with minimal overfitting. The almost identical trends obtained for MAE and R\({}^{2}\) confirm that the performance of the model is robust and maintains accuracy across different data splits. Figure 9 demonstrated the comparison between the RMSE and Huber loss metrics for both training and validation datasets after 1000 training epochs. The bar chart provides an intuitive understanding of the performance metrics, allowing a comparison between these loss functions. The RMSE of the training set is slightly lower than that of the validation set, which is consistent with the fact that the model is optimized on the training data. Moreover, the difference between the two values is minimal and strengthens the ability of the model to generalize well to unseen data. The low RMSE values indicate that the model has effectively minimized prediction errors. In addition to RMSE values, the Huber loss values showed a similar trend, with the training set having a marginally lower loss than the validation set. The robustness to outliers of the Huber loss, as indicated by the minimal difference between the training and validation metrics, underscores the effectiveness of using this loss function for this dataset, which may contain noisy data or outliers. The consistently low Huber loss values further highlight the model's balanced performance, successfully managing both typical cases and potential outliers in the data. In summary, both RMSE and Huber loss showed how effective the chosen architecture and hyperparameters are, confirming the model's strong predictive capabilities, robust performance and efficient processing of the given dataset. Figure 8: Best validation and training MAE and R\({}^{2}\) score for 1000 training epochs.
## 5 Performance Evaluation
### Performance Evaluation for Consumed Energy  
We recorded all transmissions (data and overhead) in the simulation experiments and calculated the total consumed energy during the experiments (Figure 17). We consider both data and overhead for proper evaluation of the performance concerning energy-efficiency. In DTN based approach, it is common that multiple copies of data packets can exist in the network that often cause extensive transmissions consuming significant energy. We can observe from Figure 17, GPRS and GPRS-Q consume the lowest energy. As explained earlier, GPSR and GPSR-Q often fail to forward the packet to the destination due to a lack of a proper mechanism to adapt in a sparsely populated network scenario. Additionally, they have the lowest overhead and packet delivery ratio. Thus, they result in the lowest transmission considering both data and overhead resulting in the lowest energy consumption. On the other hand, being the best performer in terms of packet delivery ratio, LECAR consumes the next lowest energy compared with LER, LAROD-LoDiS, and Spray and Wait. The low energy consumption by LECAR can be attributed to its small number of transmissions per data packet while keeping the number of packet copies at a minimum. The intelligent routing mechanism of LECAR allows it to avoid congested paths and routes towards the destination with each transmission. This primary factor helps LECAR overcome the extra overhead even in high-density scenarios. Similarly, the increased energy consumption by LER, LAROD-LoDiS, and Spray and Wait can be attributed to the increased number of transmissions per data packet along with the generated overhead.
## 5 Discussion
### Factory Layout Verification and Digital Twin Visualization  
In order to minimize the cost of virtual objects and enhance the user's sense of reality, MR technology is proposed to solve the layout problem of factory production lines. As early as 2011, [PERSON] [45] proposed a virtual factory layout planning system based on deploying the method of a mixed reality digital manufacturing environment. Similarly, MR technology is also applied to the layout of indoor furniture, where various three-dimensional computer graphics (such as sofas, beds and lamps) are superimposed on the real space, allowing users to visualize interior design [46]. The factory layout works the same way as the interior layout, positioning virtual objects in the real world and observing the rationality of the layout with the help of an MR headset. The factory layout verification scheme proposed in this paper not only realizes the traditional production line layout, but also realizes the virtual production line simulation and simulates the real working environment. Considering the complexity of the actual production line, the current production simulation can only simulate routine operations, but cannot simulate the abnormal phenomenon such as equipment failure. MR tools are superior to traditional simulation software in terms of both planning quality and flexibility.  
Digital twinning is a technology that maps virtual space to reflect the full life-cycle process of corresponding physical equipment. Digital twin technology in this paper is applied to the welding production process of milling rotors, this research is distinct from [PERSON]'s [47] digital twinning of parameterized design and robot manufacturing, which adding holographic content to images in the real world and superimposing them as holographic content. In this paper, with the help of MR remote collaboration technology, a mixed reality virtual sand table is constructed to transmit real-time parameters of equipment on the production line to the virtual sand table. The virtual sand table synchronizes data in real time and presents the simulation process of the production line in proportion, realizing remote monitoring through MR device. Compared with [PERSON]'s [48] sand table prototype based on projection, the technology is simpler and the presentation effect is better.
## 4 Vehicular Ad Hoc Networks
### Applications  
In this section, we introduce some VANET applications:  
* **Traffic and safety efficiency:** Exchanging beacon and event-driven messages in V2V and V2P communication allows other vehicles and road users to control the environment. These messages include the location of sender vehicles and systematic parameters. For example, when an accident warning is sent by the driver to inform other drivers [26; 27].
* **Autonomous driving:** For accurate routing by smart vehicles, they must be aware of their location, the surrounding environment, and the neighboring vehicles because they move at high speeds (i.e., above 200 km/h).
* **Tele-operated driving:** This application is used in dangerous environments for example, nuclear events, earthquakes, snowfall, and road construction. In this case, the driver is out of the vehicle and controls his driving operations using a camera and sensor data.
* **Entertainment and Internet of Vehicles (IoV):** This application provides comfortable services for drivers and pedestrians such as access to mobile Internet, messengers, dialog between vehicles, and collaborative games on the network.
## 4 Fragility Assessment of Structural Pounding
### Curvature-Based Fragility Curves
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Methodology**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**MM**} & \multicolumn{2}{c}{**MLE**} & \multicolumn{2}{c}{**IM—Percentiles**} & \multicolumn{2}{c}{**PSDM**} \\ \hline
**EDPs** & **Examined Case** & \(\mu\) **(g)** & \(\beta\) & \(\mu\) **(g)** & \(\beta\) & \(\mu\) **(g)** & \(\beta\) & \(\mu\) **(g)** & \(\beta\)1/\(\beta\)2 \({}^{\dagger}\) \\ \hline  & without bounding & 0.239 & 0.265 & 0.240 & 0.286 & 0.231 & 0.324 & 0.300 & 0.281 \\ \cline{2-10}
**IDRmax** & \(\text{d}_{\text{g}}=0.0\) cm & 0.198 & 0.170 & 0.192 & 0.267 & 0.201 & 0.274 & 0.215 & 0.270 \\ \cline{2-10}
**(\%hst)** & \(\text{d}_{\text{g}}=4.5\) cm & 0.239 & 0.262 & 0.246 & 0.285 & 0.236 & 0.277 & 0.255 & 0.290 \\ \cline{2-10}  & \(\text{d}_{\text{g}}=9.0\) cm & 0.243 & 0.266 & 0.246 & 0.285 & 0.240 & 0.330 & 0.270 & 0.287 \\ \hline  & without bounding & 0.704 & 0.323 & 0.700 & 0.369 & 0.642 & 0.372 & 0.710 & 0.320 \\ \cline{2-10}
**TDRmax** & \(\text{d}_{\text{g}}=0.0\) cm & - * & - * & 0.691 & 0.273 & 0.644 & 0.272 & 0.740 & 0.352 \\ \cline{2-10}
**(\%Hot)** & \(\text{d}_{\text{g}}=4.5\) cm & - * & - * & 0.682 & 0.302 & 0.595 & 0.272 & 0.700 & 0.324 \\ \cline{2-10}  & \(\text{d}_{\text{g}}=9.0\) cm & 0.661 & 0.338 & 0.665 & 0.347 & 0.589 & 0.384 & 0.691 & 0.330 \\ \hline  & without bounding & 0.785 & 0.274 & 0.821 & 0.287 & 0.745 & 0.298 & 0.307 & 0.334 \\ \cline{2-10}
**C20** & \(\text{d}_{\text{g}}=0.0\) cm & 0.364 & 0.373 & 0.368 & 0.315 & 0.403 & 0.453 & 0.308 & 0.289/0.676 \\ \cline{2-10} \(\mu_{\varphi,\text{max}}\) & \(\text{d}_{\text{g}}=4.5\) cm & 0.358 & 0.317 & 0.381 & 0.268 & 0.376 & 0.314 & 0.340 & 0.315/0.689 \\ \cline{2-10}  & \(\text{d}_{\text{g}}=9.0\) cm & 0.536 & 0.238 & 0.546 & 0.231 & 0.530 & 0.279 & 0.445 & 0.300/0.768 \\ \hline \hline \end{tabular}  
* \(\beta\)1 depicts the \(\beta\) value at the first linear branch of bilinear PSDM or the \(\beta\) value of a linear PSDM \(\beta\)2 depicts the \(\beta\) value at the second branch of bilinear PSDM,
* no available result.  
\end{table} 
Table 2: Median \(\mu\) and logarithm standard deviation (dispersion) \(\beta\).
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 3 System Framework
### Seasonal-Trend Decomposition Based on Loess  
The stock market is influenced by a multitude of factors that are both diverse and complex in nature, encompassing long-term trends, cyclical patterns, and irregular fluctuations. These factors vary significantly in their characteristics, making it challenging to analyze and model stock price movements comprehensively.  
1. Long-term Trends: The trend component reflects the long-term directional movement of stock prices, capturing factors such as company fundamentals, market sentiment, and broader economic cycles. It represents the underlying value changes of a stock over an extended period, smoothing out short-term fluctuations to reveal the overall trajectory--whether upward, downward, or sideways.
2. Cyclical patterns: The seasonal component captures the periodic fluctuations in stock prices, highlighting patterns that repeat at regular intervals. These fluctuations are often driven by time-related factors such as quarterly earnings reports, annual holidays, or monthly economic data releases. By isolating these cyclical patterns, the seasonal component helps identify predictable market behaviors tied to specific timeframes.
3. Irregular fluctuations: The residual component represents the random noise and unpredictable events in the stock market, including sudden market shocks, unexpected news, or shifts in investor sentiment. While it is an important feature of the market, its inherent unpredictability makes it challenging to model or forecast. After separating the trend and seasonal components, the residual must be decomposed and retained to account for the impact of these irregular and often significant market movements, ensuring a more comprehensive understanding of stock price dynamics.  
To address this complexity, it is essential to decompose the stock price data into distinct components that capture different aspects of its behavior. In this study, we employed seasonal and trend decomposition using the Loess method to decompose the original stock price data into trend, seasonal, and residual components. This decomposition enables a more detailed analysis of the underlying forces driving stock price dynamics, facilitating a deeper understanding of long-term trends, periodic patterns, and irregular noise. By isolating these components and using the predictable trend and seasonal elements for training in the graph attention and temporal convolution modules, while retaining the residual features through an MLP, STGAT's predictive accuracy and interpretability are enhanced.