esa-sceva/satcom-chunk-collection huggingface dataset:
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 4 Definitions and Problem Statement
### Data Preprocessing  
AIS data preprocessing is essential for training deep learning models, especially for models that require trajectory information and vessel characteristics. In VEPO-S2S, we selected AIS data from southwestern and southeastern coastal waters in the US for training, validation, and testing. The dataset includes static attributes such as the Maritime Mobile Service Identity (MMSI), vessel length, and width. Additionally, it encompasses dynamic information of vessel navigation such as the longitude, latitude, speed, and course. The original AIS data may experience adverse weather conditions during the reception process, leading to signal transmission delays and reception errors [48]. Moreover, the performance of deep learning models could be adversely affected by data loss resulting from technical issues and equipment maintenance. Therefore, we conducted comprehensive preprocessing of the AIS data before training (see Figure 2).  
The process is shown in the following steps:  
(1) Sort and Classify: We filtered vessels with complete information on the length, width, draft, and type, then separated the trajectory data of each vessel based on the Maritime Mobile Service Identity (MMSI) number, and sorted them in ascending order of timestamps.  
Figure 2: The process of data preprocessing.  
(2) Denoise: We removed points with duplicate timestamps and unreasonable longitude and latitude.  
(3) Segment: We separated the trajectory into different segments when the time interval between two adjacent trajectory points exceeded 60 min or when the distance between three consecutive trajectory points was less than 100 m.  
(4) Interpolate: We employed cubic spline interpolation to ensure a 10-min interval between consecutive trajectory points.  
(5) Compute: We computed the course and speed for each trajectory point.  
(6) Normalize: We normalized the longitude, latitude, speed, course, length, width, and draft using the min-max normalization method, as expressed in Equation (1)  
\[x_{\text{norm}}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
where \(x\) is the original data, \(x_{\text{min}}\) and \(x_{\text{max}}\) represent the minimum and maximum value in the trajectory data, respectively. \(x_{\text{norm}}\) is the normalized data.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Experimental Results
#### 5.2.1 Evaluation Criteria  
This section defines the measurement of the evaluation classification model, that is, the accuracy of event prediction. Two evaluation criteria are adopted: area under roc curve (AUC), ACCURACY (ACC).  
(1) ACC is the ratio of correctly predicted events to the total number of actual events in the test data set, which reflects the ability of the classification model to predict abnormal events. Acc needs to convert the prediction result probability of the classification model into a category before calculating the prediction accuracy, which is defined as follows:  
\[ACC=\frac{\sum_{i\in N}c\big{(}x_{i}^{\prime},\ x_{i}\big{)}}{|N|} \tag{38}\]  
where, \(x_{i}^{\prime}\) is the predicted event, \(x_{i}\) is the actual event, and \(|N|\) is the total number of events in the test set. \(c\big{(}x_{i}^{\prime},\ x_{i}\big{)}\) is used to judge whether \(x_{i}^{\prime}\) is equal to \(x_{i}\) or not. If they are the same, \(c=1\), otherwise \(c=0\). In fact, \(x_{i}^{\prime}\) is a probability result, subject to the threshold \(d\) of the classification model.  
(2) AUC  
The AUC randomly selects a sample from the positive sample and the negative sample, respectively. Then, it sets the probability that the positive sample is predicted to be 1 by the classifier as \(p_{1}\), and the probability that the negative sample is predicted to be 1 as \(p_{0}\), then \(AUC=P(p_{1}>p_{0})\); that is, AUC means the sorting ability of the classifier to the samples, and is not limited by the threshold d. The result is used to measure the performance of the classification model.  
Therefore, we adopt AUC as a criterion to evaluate the performance of classifier models. AUC is a probability, and the larger the value, the better the effect of the classifier (model). When \(AUC=0.5\), the effect of the classifier is a random selection, and the model has no predictive value; when \(AUC<0.5\), the prediction effect of the model is worse than that of random sampling; when \(0.5<AUC<1\), the classifier model has a better prediction effect when an appropriate threshold is set; when \(AUC=1\), the prediction result is grand truth, in specific application scenarios, and it is difficult for this kind of classifier to exist. The calculation formula of AUC is as follows:  
\[AUC=\frac{\sum_{i\in positiveClass}rank_{i}-\frac{M(1+M)}{2}}{M\times N} \tag{39}\]  
where \(rank\) is the sorting of positive samples according to \(p_{1}\), \(M\) is the number of positive samples, \(N\) is the number of negative samples, \(M\times N\) is all positive and negative sample pairs, \(M(1+M)/2\) is the sorted sum of the probability of repeated \(M-1\) (positive, positive) samples.
## 4 QoS and QoE
### QoS/QoE Mapping Models  
QoS and QoE mapping models bridge the technical parameters of a network (i.e., QoS) and the subjective perception of users (i.e., QoE). These models vary in complexity, inputs, accuracy, working principle, and applications. The mapping models can be divided into the following three classes: black-box media-based models, glass-box parameter-based models, and gray-box parameter-based models [37; 38].  
Black-box media-based quality models assess media captured at the system's input and output, relying entirely on empirical data for analysis. They are characterized by their lack of interpretability, meaning that the internal workings of these systems are not easily understandable to users [39]. These models can be further categorized into full-reference and no-reference quality models, depending on the availability of feedback.  
Glass-box models, also known as white-box models, consider the underlying mechanisms influencing QoE based on detailed knowledge of the system's internal workings. These models typically involve looking inside the multimedia transmission pipeline and understanding how individual components contribute to user perception. For example, ITU-T G.1070, a glass-box model, considers factors such as video encoding parameters, packet loss rates, and network latency to predict QoE.  
Hybrid or gray-box models [38] combine the benefits of black-box and glass-box mapping models. They examine essential characterization factors at the system's output, along with some information about the clean stimulus. In other words, they leverage both detailed knowledge of the system internals and empirical observations of user behavior [40]. Because of their higher accuracy compared to glass-box models and easier implementation process, this class of models is gaining popularity.
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Management  
Management is key for the effective deployment and value extraction of DTNs in production environments. It integrates network data into a model to optimize, predict, and guide operations. This process involves continuous feedback and analysis to ensure outcomes meet expectations, creating a closed-loop system. Key aspects of management include orchestrating multiple DTN instances, ensuring their efficient deployment and resource utilization. Collaboration management is also important, coordinating roles among network administrators, data scientists, and security teams to maintain the twin's accuracy and performance. Additionally, Conflict Detection involves managing interactions within the DTN, using policies and algorithms to address issues like user intents and access controls. Finally, energy-efficient management focuses on reducing energy consumption in both network operations incited by the DTN (e.g., additional monitoring schemes) and the DTN system, contributing to greener network practices.  
As with interfaces and visualization, the management of DTNs is often omitted in research, as it is more practical and less theoretical.
## 3 Analysis Method  
Elastico-plastic finite displacement dynamic response analysis is used as an analysis method in this study. The incremental equation of motion considers the effect of geometrical and material nonlinearities. The incremental finite element dynamic equilibrium equation at time \(t+\Delta t\) over all the elements can be expressed in the following matrix form:  
\[[M]\{\ddot{u}\}^{t+\Delta t}+[C]\{\dot{u}\}^{t+\Delta t}+[K]^{t+\Delta t}\{ \Delta u\}^{t+\Delta t}=-[M]\{\ddot{z}\}^{t+\Delta t} \tag{2}\]  
Where [\(M\)], [\(C\)], and [\(K\)]\({}^{t+\Delta t}\) represent the mass, damping, and tangent stiffness matrices of the viaduct structure at time \(t+\Delta t\), respectively. In the meantime, \(\bar{u}\), \(\dot{u}\), \(\Delta u\) and \(\ddot{z}\) denote the structural accelerations, velocities, incremental displacements, and earthquake accelerations at time \(t+\Delta t\), respectively.  
Figure 5: Force–displacement relationships for steel bearing supports. (**a**) Fixed bearing. (**b**) Roller bearing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bearing Type**} & **K1** & **K2** & **F1** & **F2** \\  & **(MN/m)** & **(MN/m)** & **(MN)** & **(MN)** \\ \hline Fixed & 980 & - & - & - \\ Roller & 49.0 & 0.0098 & 0.0735 & 0.0754 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structural properties of steel bearing supports.  
Figure 6: Force–displacement relationships for steel stoppers. (**a**) Modified Hertz-damp model. (**b**) Pounding element model.  
A bilinear type is used to simulate the stress-strain relationship of beam-column element. The piers are made of steel. The elastic modulus is 200 GPa, and the yield stress is 235 MPa. The strain hardening coefficient is 0.01. [PERSON]'s step-by-step method of constant acceleration is adopted in this study to obtain the integration of the equations of motion. The Newton-Raphson iteration scheme is adopted to achieve the acceptable accuracy in the response calculations. The damping of the structure is also simulated by Rayleigh's type. The damping coefficient of the structure is 2%. In this study, the program has been validated through a comparison with different commercial software, such as EDYNA, DYNA2E, and DYNAS [24].  
The input ground motion records are obtained from Takatori Stations (JRT) during the 1995 Kobe earthquake. Three direction earthquake records, namely, North-South (NS), East-West (EW), and Up-Down (UD), are shown in Figure 7. The NS component shakes the viaduct parallel to the X-axis in Figure 1, whilst the EW and UD components are acting in the Y- and Z-axes, respectively. The JRT record is characterised by high intensities of accelerations and velocities [25]. Thus, JRT is selected as input ground motion. The acceleration response spectrum for a damping ratio (\(\xi\) = 0.02) is shown in Figure 8. The fundamental natural periods for continuous span and simple supported span are 1.393 and 0.770 s, respectively.
## 4 Results and Discussion
### Regeneration Heat and Cooling Capacity  
A definition of regeneration heat is provided in Equation (9). Regeneration heat is heat rate to heat up the regeneration air by hot water produced by CHP.  
\[q_{Rgg}=\dot{m}_{\text{R}}(h_{HWC,\ out}-h_{HWC,\ in}). \tag{9}\]  
Figure 9a,b show the regeneration heat required to maintain the humidity ratio of each space at an average of 10 g/kg during 3-6 h operation according to the ambient conditions. Similar to the results of the regeneration heat obtained from the field tests in the residential environment in Figure 9c, more regeneration heat is required under low temperature and high humidity conditions in this case. The desiccant rotor used in the field test had a limited dehumidification capacity, and therefore, compensating for the increasing outdoor humidity ratio was difficult. Consequently, the regeneration heat demand remained at approximately 3 kW under humid conditions. However, in the simulation, the capacity of the desiccant rotor was increased, and the prediction results indicated the use of up to 6.14 kW of regeneration heat. Furthermore, regenerative heat was not required since the heat from the condenser of the heat pump was sufficient to maintain an indoor humidity ratio of 10 g/kg under low humidity conditions.  
Figure 8: Changes in thermal comfort conditions of cooling spaces with an increase in the desiccant rotor capacity.  
Figure 9: Regeneration heat of (**a**) Unit 1 and (**b**) Unit 2. (**c**) Field test data [10] of regeneration heat for the existing system.  
Figure 10a,b present the simulated cooling capacities (Equation (7)) with respect to the ambient conditions. To maintain an indoor humidity ratio of 10 g/kg in the simulation, the higher the humidity level, the more the amount of latent heat that is treated, and the cooling capacity increases. According to the prediction results, after dehumidification with the desiccant rotor, evaporative cooling with the REC improves with increasing outdoor temperature. Accordingly, the cooling capacity is high under high temperature and humidity conditions. However, the results of the field test (Figure 10c) show that the cooling capacity increases under low temperature and high humidity conditions. As the desiccant rotor of the HDCS used in the field test had limited capacity, it was impossible to dehumidify the supply air (approximately 9.5 g/kg humidity remained). Furthermore, cooling the space sufficiently with evaporative cooling only was difficult at the high humidity level, and therefore, dependence on the heat pump increased. Consequently, the cooling capacity was high at low outdoor temperatures.
# Analysis on the Exhaust Air Recirculation of the Ventilation System in Multi-Story Building
## 3 Results of CFD Analysis
### The Result of the Recirculation Rate Analysis  
Table 2 presents the recirculation rates of exhaust air emitted from the ventilation system on each floor by case. In Cases 1-3, where the external wind direction with the installation of a guide panel was toward the front of the building, the recirculation rate of exhaust air was less than 0.1%. In Cases 4-6, where the external wind direction was southwesterly (\(\theta=225^{\circ}\)) at \(45^{\circ}\) from the front of the building, the recirculation rate of exhaust air on the first and second floors with downdraft was less than 0.1%, and that on the fourth and fifth floors with updraft was relatively higher compared with the first and second floors. Regarding the change in ventilation rate, it was determined that when the external airflow velocity blowing in the direction opposite to the exhaust port is equal to or smaller than the airflow velocity of the exhaust air, the amount of recirculation increases due to the neutral zone formed near the building's outer wall. In Cases 7-9, where the external wind direction was toward the back of the building, exhaust air was recirculated on all floors, and the recirculation rate was at its highest on the third floor. This is because negative pressure is formed on the front of the building where the exhaust and intake ports are located, and the airflow is considerably reduced so that a stagnant area is created. InCases 1-9, with the installation of a guide panel to prevent the recirculation of exhaust air, the maximum recirculation rate was 7.7% on the fifth floor in Case 4. The maximum recirculation rate of a case which is identical to the case that experienced the maximum recirculation rate in terms of external velocity, direction, and ventilation rate from the ventilation system that did not have a guide panel, was 20.0%. Conversely, it was found that the amount of exhaust air recirculation can be reduced by 62% due to the installation of the guide panel, but it was confirmed that the effect was insufficient in the lower floors. Therefore, installing the guide panel only on the upper floors of the target building can be an economical alternative for preventing the recirculation of exhaust air.  
Figure 8: Normalized concentration distribution of exhaust air (Section-ABCD): (**a**) Case 1; (**b**) Case 4; (**c**) Case 7; (**d**) Case 10.
## 3 Results and Discussion
### Ratcheting Results  
The Chaboche kinematic hardening rule in ANSYS software was employed to assess the ratcheting response of steel samples with two different element types. Figure 10 compares the measured [51; 52], predicted, and simulated ratcheting behavior of the SA508 and SA333 steel samples at various temperatures using different element types. Panels (a) to (c) present the ratcheting strain progression of SA508 at 298 K, 473 K, and 623 K, respectively, while panels (d) to (f) illustrate the ratcheting behavior for SA333. In each case, the predicted curves obtained from the A-V hardening model, simulated results for nonlinear brick elements, and nonlinear tetrahedral elements are compared against the experimental data. In these figures, the predicted and simulated ratcheting curves obtained through the A-V model and the FE method demonstrate close agreement with those of the experimentally measured values. The deviation of the simulated and predicted ratcheting strain from the measured data in these figures was found to be less than 16%. The results obtained using the tetrahedron elements yielded an \(R^{2}\) value of 0.85, while the brick-type elements achieved a higher \(R^{2}\) of 0.91, indicating better agreement with the experimental data. Among the models, the A-V formulation demonstrated the highest accuracy, with an \(R^{2}\) of 0.96, representing closer agreement to experimental results. At 298 K, the ratcheting response showed rapid initial accumulation followed by gradual saturation in both materials, with SA333 demonstrating a higher strain amplitude than SA508. At 473 K, the ratcheting rate increased due to a noticeable decrease in yield strength. The smaller yield strength at this temperature resulting in higher accumulation of plastic strain. At 623 K, despite thermal softening effects, SA333 continued to exhibit higher ratcheting levels, reflecting its lower resistance to cyclic plastic deformation. Among the different element types, the nonlinear tetrahedral elements showed slightly more deviation from the experimental results compared to the brick elements. The simulation results confirm that both material composition and temperature significantly influence ratcheting strain evolution under cyclic loading.  
Figure 10: Simulated and measured [51; 52] ratcheting curves for (**a**–**c**) SA508 (**d**–**f**) SA333. The simulation was conducted for two different element types.
## 5 Research Progress on Membranes for Redox Flow Batteries  
In all the redox flow systems described in the previous sections, identifying and developing suitable membranes to provide the right balance of performance and cost have proven to be a major challenge. As identified earlier, the major problem with the iron/chromium redox flow cell was the cross contamination of the two half-cell electrolytes due to the passage of iron and chromium ions across the membrane. One method of overcoming this problem was the use of premixed Fe-Cr reactants in the two half cell solutions [51]. Other problems faced by Fe-Cr redox cell researchers included, the fouling of the anion selective membranes due to the formation of ferric chloride complexes [52]. Cationic membranes Nafion(r) 117 and NEOSEPTA(r) CR-2, as well as the non-selective, microporous separator Damic (W.R. Grace) have also been tested. The Nafion(r) 117 has produced the best result to date [53, 54].  
In the Regenesys S-Br flow cell, electrolytes are separated by a cation exchange membrane, which must allow sodium ions to be transported through in order to achieve electrical balance. It is important for the efficient running and lifetime of the cell that anions do not cross the membrane [55]. Sulphide diffusing from the negative to the positive side of the cell reacts directly with the bromine to produce sulphates that are not readily retrievable from the positive electrolyte and thus represent a net loss of sulphur from the system [56]. The challenge is to prevent anion diffusion without compromising the overall resistivity of the membrane. In vanadium redox batteries, a polysulfone based anion exchange membrane has been developed and used by both Kashima-Kita Electric Power Corporation and by Sumitomo Electric Industries in their initial 200 kW x 4 hr rate demonstration system which has been operating since 1997 [57]. In a more recent study, an optimised 8 module 10 kW VRB stack incorporating a Nafion membrane, provided an overall energy efficiency of 82.35% at a charge/discharge capacity of 50 mA\(\cdot\)cm\({}^{-2}\)[58]. In an attempt to highlight some of the significant developments in the synthesis and modification of membranes, the following sections provide an overview of the work on membranes that has been done in vanadium redox batteries, to date.
# Gradient Agreement Hinders the Memorization of Noisy Labels
## 4 Experiments
### Ablation Study
#### 4.3.2 Ablation Study on Agreement Sampling Methods  
In this section, we perform ablation studies on different data sampling methods to verify the effectiveness of the proposed class distribution agreement sampling. With Random Sampling, instances fed into \(\theta_{A}\) and \(\theta_{B}\) in every batches are different and both randomly sampled from the whole training set. Models fail to learn from clean labels because the updates of parameters are hindered by gradient divergences caused by data difference. With Identical Sampling, exact same samples are fed into \(\theta_{A}\) and \(\theta_{B}\) in every iteration. Naturally, the gradients of the two models are always in agreement. Therefore, GAL fails to prevent models from memorizing noisy labels. Instead, the proposed class distribution agreement sampling strategy avoids gradient disagreement caused by different input data, meanwhile enables models to distinguish noisy gradients. As is shown in Table 5, the test accuracy is significantly higher utilizing class distribution agreement sampling. \"CDAS w/o Reversed Sampling\" refers to class distribution agreement sampling without reversed sampling from \(\theta_{B}\) to \(\theta_{A}\). As Table 5 presents, the contribution of reversed sampling strategy to the performance of trained models is not very significant. However, by reducing the deviation of accuracy, it is conducive to stabilize the performance of GAL.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**CIFAR-10**} \\
**Method** & **Sym 20\%** & **Sym 50\%** & **Sym 80\%** & **Asym 40\%** \\ \hline Cross Entropy & 86.8 & 79.4 & 62.9 & 85.0 \\ Random Updating & 83.1 & 74.2 & 56.5 & 81.8 \\ \(\theta_{A}+\theta_{B}+\text{Cross Entropy}\) & 88.9 & 83.6 & 63.9 & 86.5 \\ \(\theta_{A}+\theta_{B}+\text{Random Updating}\) & 83.4 & 74.3 & 57.1 & 82.2 \\ \(\theta_{A}+\theta_{B}+\text{Gradient Agreement Updating}\) & 93.5 & 89.8 & 77.7 & 91.1 \\ GAL & 93.7 & 90.5 & 78.9 & 91.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation study on CIFAR-10 with different types and levels of label noise  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**CIFAR-10**} \\
**Method** & **Sym 20\%** & **Sym 50\%** & **Sym 80\%** & **Asym 40\%** \\ \hline Random Sampling & 84.9 \(\pm\) 0.4 & 81.9 \(\pm\) 0.6 & 65.2 \(\pm\) 0.9 & 85.2 \(\pm\) 0.2 \\ Identical Sampling & 86.8 \(\pm\) 0.1 & 81.4 \(\pm\) 0.1 & 65.1 \(\pm\) 0.2 & 86.5 \(\pm\) 0.1 \\ CDAS w/o Reversed Sampling & 93.2 \(\pm\) 0.5 & 89.8 \(\pm\) 0.8 & 78.1 \(\pm\) 0.8 & 91.2 \(\pm\) 0.4 \\ Class Distribution Agreement Sampling & 93.6 \(\pm\) 0.2 & 90.3 \(\pm\) 0.3 & 78.6 \(\pm\) 0.4 & 91.5 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation Study on Agreement Sampling Methods. The mean accuracy and its standard deviation are computed over five tries.