esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Peso
#### 3.3.3 Ship-Oriented Decoder  
As described above, the Semantic Location Vector is the spatial semantic representation of each ship's historical trajectories. The Ship-Oriented Decoder is designed to utilize the Semantic Location Vector (SLV) of each ship to guide the prediction of the vessel trajectory. With this approach, we better express the spatial correlation of the predicted trajectories of each ship. The Ship-Oriented Decoder is composed of five LSTM layers. The decoder takes SLV, input track points, \(h_{enc}\), and \(c_{enc}\), generated by the Parallel Encoders as inputs. Finally, the model outputs the predicted coordinate sequence of five track points. In this paper, we focus on predicting the longitude and latitude of the following 5 track points by using the previous 10. Technically, the predicting process during training is different from the testing and validation period.  
The model based on Seq2 Seq structure is able to predict multiple points instead of using a sliding window mechanism. When predicting \(y_{t}\) at the time of \(t\) during training, we use \(y_{t-1}\) as the coordinate of the nearest trajectory point, where \(y_{t}=(lon_{t},lat_{t})\) and \(dec\) denote the mapping function of the Ship-Oriented Decoder.  
\[\widehat{y}_{t-1}=dec(y_{t-1},SLV,h_{enc},c_{enc}) \tag{9}\]  
Specifically, the detailed process in Equation (9) is displayed as follows:  
\[\begin{split} T_{\mathcal{{{{{T}}}}}}&=concat(y_{t -1},SLV)\\ \widehat{T}_{\mathcal{{{{{{{{{{{{{{{{{{{{{{ } } } }}}}}}}}}}}} \]}}}}&=tanh(W\cdot T_{t}+b)\\ \widehat{y}_{t},(h_{dec},c_{dec})&=LSTM( \widehat{T}_{\mathcal{{{{{{{{T}}}}}}}}},h_{enc},c_{enc})\end{split} \tag{10}\]  
where \(LSTM\) denotes the LSTM layer, \(t\in(11,12,13,14,15)\), and \(W\), \(b\) are the parameters of a linear layer. In this way, the Ship-Oriented Decoder predicts the position sequence \(\widehat{Y}=(\widehat{y}_{11},\widehat{y}_{12},\ldots,\widehat{y}_{15})\) with the previous 10 track points \(Y=(y_{1};y_{2};\ldots;y_{10})\).  
While predicting the first track point of the five-length trajectory on the testing or validation period, we use the tenth position track point \(y_{10}\) as input.  
\[\begin{split} cc\widehat{T}_{11}&=concat(y_{10}, \mathcal{O}rt)\\ \widehat{T}_{\mathcal{{{{{e}}}}11}}&=tanh(W\cdot T _{\mathcal{{{{e}}}}11}+b)\\ \widehat{y}_{11},(h_{dec},c_{dec})&=LSTM( \widehat{T}_{\mathcal{{{{e}}}}11},h_{enc},c_{enc})\end{split} \tag{11}\]  
In the next trajectory point prediction, we utilize the previous prediction as part of the input. The process is concluded in Equation (12).  
\[\begin{split} T_{\mathcal{{{{e}}}}_{t}}&=concat( \widehat{y}_{t-1},\mathcal{O}rt)\\ \widehat{T}_{\mathcal{{{{e}}}}_{t}}&=tanh(W\cdot T _{\mathcal{{{{e}}}}}+b)\\ \widehat{y}_{t},(h_{dec},c_{dec})&=LSTM( \widehat{T}_{\mathcal{{{{e}}}}},h_{enc},c_{enc})\end{split} \tag{12}\]  
\(t\in(12,13,14,15)\).
###### Abstract  
With growing environmental concerns, the search for alternative gases to replace SF\({}_{6}\) has become a key focus in the power industry. Perfluoromethyl vinyl ether (PMVE), with its low global warming potential (GWP) and excellent insulation properties, is a promising candidate. When mixed with N\({}_{2}\), PMVE not only decreases the liquefaction temperature but also enhances insulation performance, making the gas mixture more suitable for engineering applications. In this study, reactive molecular dynamics (ReaxFF-MD) and density functional theory (DFT) calculations were combined to investigate the influence of temperature on the decomposition characteristics of a PMVE/N\({}_{2}\) mixture. The reaction pathways and reaction enthalpy of PMVE and its major decomposition products were analyzed in detail. The results showed that, as temperature increases, the decomposition intensity of PMVE is enhanced, leading to a higher reaction rate and accelerated formation of decomposition products. Moreover, the main decomposition products of the PMVE/N\({}_{2}\) mixture include C, C\({}_{2}\)F\({}_{2}\), CF\({}_{2}\), CN, CO, CF\({}_{2}\)O, F, O, and other small molecules and free radicals. The dynamic balance between the generated free radicals helps maintain the system's insulation capacity. However, toxic decomposition byproducts such as CF\({}_{2}\)O, C\({}_{2}\)N\({}_{2}\), and CO were also detected. This study provides valuable insights into the engineering applications of PMVE/N\({}_{2}\) mixtures.  
SF\({}_{6}\) substitute gas; PMVE/N\({}_{2}\); ReaxFF-MD; density functional theory; decomposition mechanism Article
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Training
#### 2.2.3 Configuration and Optimization  
In terms of software and hardware configuration, all operations are implemented on a GeForce GTX 2080 Ti with driver version of the graphics card 450.57. The CUDA version is 11.0 and the CUDNN version is 7.6.4. The neural network framework selects Pytorch, the integration environment selects vs. Code, and the programming environment selects Python 4.5.3.  
In terms of the hyperparameters of the model, the number of training rounds (epoch) of the dataset is 50. Adam [38] is selected as the optimization algorithm with a learning rate of \(10^{-4}\), batch size of 4, and default hyperparameters. In order to limit the memory utilization of the model, the input is cut to 256 \(\times\) 256. The weight coefficients of the message  
Figure 7: VVC compressed reconstructed video frame and corresponding CU mask.  
extraction loss \(\lambda_{1}\) and that of the adversarial (generator) loss \(\lambda_{3}\) are both 1, while the weight coefficient of the video reconstruction loss \(\lambda_{2}\) is 100.
## 2 Energy-Aware Multi-UAV Dispatch Algorithm (EAMUD)  
### The Two-Layer FANET-VANET Network in Disaster  
Whenever the disaster breaks two roads as shown in Figure 1, corresponding communication paths like VANET links through them will disconnect. In this paper, we denote these two broken VANET link as Event A, i.e., \(Ev_{A}\), and B, i.e., \(Ev_{B}\), respectively. For continuing communication for these events, we propose to drive a mobile GCS, which carries multiple UAVs and charging faculties, to a position near these broken roads. The GCS will first connect to existing VANET. Then it will dispatch several UAVs to corresponding locations on each link-broken road to build a temporary FANET in the air by creating _UAV-to-UAV_ (U2U) links between dispatched UAVs. After that, the UAV located at the edge of FANET will connect to the vehicle in the boundary of VANET through the _UAV-to-Vehicle_ (U2V) link, which in turn forms a two-layer FANET-VANET network to recover communication link of each event. According to the energy model of UAV battery described below, GCS and UAVs are able to calculate energy consumption for flying, hovering, and communication. Excluding the energy for flying back to GCS for recharging the battery, the remaining energy of each dispatched UAV is consumed on hovering over its assigned position and maintaining the U2U/U2V communication. Based on the EAMUD algorithm proposed later, GCS can dispatch an optimal UAV to hand over the dispatched one for  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Research Features**} & **Handle Multiple Events and Dispatch Multiple UAVs for an Event & \begin{tabular}{c} **Consider the UAV Charging Progress and** \\ **Energy Consumption for flying and Communication** \\ \end{tabular} & \begin{tabular}{c} **Check whether the** \\ **Dispatched UAVs Can** \\ **Maximize the Event** \\ **Communication Time** \\ \end{tabular} & \
\begin{tabular}{c} **Consider the UAV** \\ **Handover Process and** \\ **Handle Exceptions** \\ \end{tabular} &
\begin{tabular}{c} **Propose the LP Optimization** \\ **Equations to Compute the** \\ **Optimal Event** \\ **Communication Time** \\ \end{tabular} \\ \hline Ref. [22] & No & Yes & No & No & No \\ Ref. [26] & No & No & No & No \\ Ref. [27] & No & No & No & No \\ Ref. [28] & No/Yes & No & No & No \\ EAMUD & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related work.  
continuing communication of the event before it drains its remaining energy. In this way, this two-layer FANET-VANET network with EAMUD achieves the longest communication duration, i.e., the maximum event communication time, for each broken road. In Figure 1, GCS dispatches \(U_{1}\),\(U_{2}\) and \(U_{3}\) to their corresponding positions, which are denoted as the sub-event \(Ev_{A,1}\), \(Ev_{A,2}\), and \(Ev_{A,3}\), respectively. Hence, the communication link of \(Ev_{A}\) is recovered by the FANET, i.e., \(U_{2}\leftrightarrow U_{3}\leftrightarrow U_{1}\), and two U2V links \(V_{1}\leftrightarrow U_{2}\) and \(U_{1}\leftrightarrow V_{3}\).
## 4 Experimental Results
### Experiment Results Based on Feature 3 (Elapsed Time, Scancode, and Flag)  
Finally, we compared the proposed method with the attack technique using C/D bit that steals the keyboard data, although this attack technique causes the overload on the system, and abnormal behavior and access. We constructed a dataset containing the flag included in the attack technique using C/D bit, and analyzed the experimental results. We classified into any number of the training set, validation set, and test set, Table 6 shows experiment results of each set and the cross-validation using Dataset 1.  
As a result, except for special cases, all scores of all machine-learning models were close to 1.0. This means that using flag (C/D bit), it classifies the actual keyboard data effectively and completely. Figure 6  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters** & \begin{tabular}{c} **Training Set** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Validation Set** \\ **Score** \\ \end{tabular} & \begin{tabular}{c} **Test Set Score** \\ \end{tabular} & \begin{tabular}{c} **Cross-Validation** \\ **Score** \\ \end{tabular} \\ \hline KNN & n\_neighbors = 1 & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline Logistic Regression & C = 0.01, L1 regularization & 1.0 & 1.0 & 1.0 & 0.895 \\ \hline Linear SVC & - & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline Decision tree & max\_depth = 1 & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline Random Forest & n\_estimators = 1 & 1.0 & 1.0 & 1.0 & 0.998 \\ \hline Gradient Boosting & \
\begin{tabular}{c} learning\_rate = 0.01, \\ max\_depth = 1 \\ \end{tabular} & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline SVM & C = 1 & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline MLP &
\begin{tabular}{c} max\_iter = 100, \\ alpha = 0.00001 \\ \end{tabular} & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experiment results of training set, validation set, and test set scores using Dataset 1 with elapsed time and flag.  
Figure 5: Performance evaluation results according to Datasets 1 to 3 with the elapsed time.  
shows the detailed performance evaluation results, such as cross-validation, accuracy, precision, recall, F1-score, and AUC according to Datasets 1 to 3 with the elapsed time and flag.  
Specifically, except for the logistic regression of Datasets 1 and 2 and the cross-validation score of the random forest, all models showed performance measures of accuracy, precision, recall, F1-score, and AUC has perfect score of 1.0. Specifically, if the flag is used, the data input from the actual keyboard can be classified, which means that the password of the user can be stolen easily.
## 3 Results
### Co-Occurrence Between Solution and Full Diagnostic Pipeline  
Figure 7 illustrates the relationship between solution proposals and full diagnostic pipeline. A strong association exists: articles with solutions were more likely to address the full diagnostic cycle.  
A key aspect in assessing the maturity and practical relevance of a diagnostic framework is the relationship between the presence of a proposed solution and the completeness of the diagnostic process. In this context, Figure 7 explores how often articles that propose a solution also address the full pipeline of fault detection, localization, and resolution.  
The results show a strong positive association between these two dimensions. Among the 128 articles that proposed at least one type of solution, over half (approximately 52%) also presented a comprehensive diagnostic framework that includes fault localization and subsequent control or maintenance recommendations. These studies typically employ advanced algorithmic methods and are often validated on either simulated or real industrial systems.  
Conversely, among the articles that did not propose any form of solution, and the vast majority also lacked full diagnostic pipeline. These papers were largely limited to detection tasks, often using statistical anomaly detection or preliminary classification methods. While such studies are useful in theory-building and problem scoping, they fall short of delivering actionable outcomes.  
This co-occurrence reflects a logical progression in diagnostic system design: once a fault can be accurately detected and located, it becomes feasible--and often necessary--to introduce a corrective mechanism or control strategy. As such, solution-oriented articles tend to incorporate richer system modeling, real-time control integration, and multi-objective optimization strategies.  
The implications of these findings are significant. They suggest that researchers aiming to enhance the practical deployment of fault diagnosis tools should prioritize end-to-end system design, integrating both algorithmic innovation and operational response mechanisms. Moreover, funding bodies and industrial stakeholders may consider full-diagnostic pipeline and solution proposals as key indicators of project readiness or technological maturity.  
Figure 7: Cooccurrence of solution and full diagnostic pipeline.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 3 Method
### Face Model Super-Resolution
#### 3.3.2 Radial Curve Estimation  
For a given low resolution model \(M_{low}\), we want to estimate radial curves \(\hat{B}_{high}\) of its unknown high resolution model \(M_{high}\). Here we assume that if two radial curves from two low resolution models are similar, the corresponding curves from their high resolution models are similar too. The assumption is rational since the data acquisition conditions (and thus noise models) are consistent for low resolution models. Thus, for a low resolution 3D face model, we first extract its radial curves \(A=\{a_{1},a_{2},...,a_{K}\}\). Then, for each radial curve \(a_{i}\), we search for the best matching curve \(l_{i}\) in the radial curve database \(D_{lowCi}\) by the match cost defined in Equation (2); i.e., the curve with the least match cost. Finally, the radial curve in the database \(D_{highCi}\) corresponding to the best matching curve \(l_{i}\) is considered as the estimated high resolution radial curve \(\hat{B}_{high}^{i}\). Then, \(\hat{B}_{high}=\{\hat{B}_{high}^{1},\hat{B}_{high}^{2},...,\hat{B}_{high}^{K}\}\). Considering the symmetry of the human face, the \(i\)-th and \((K+2-i)\)-th radial curve should be symmetric about the symmetry plane of the human face with a even number \(K\) (see Figure 3). Thus, assume the \(i\)-th radial curve comes from the high resolution face model labeled \(G^{i}\); that is,  
\[\hat{B}_{high}^{i}=D_{highCi}^{G^{i}},i\in(1,K/2) \tag{3}\]  
Correspondingly, the \((K+2-i)\)-th radial curve should come from the same face model; that is,  
\[\hat{B}_{high}^{K+2-i}=D_{highC(K+2-i)}^{G^{i}},i\in(1,K/2) \tag{4}\]
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 1 Introduction
### Use of Ground Waves  
There was no suggestion that the ground waves used in [PERSON]'s experiments were ordinary electromagnetic waves similar to those used in [PERSON]'s experiments. In [PERSON]'s experiments, his electromagnetic waves propagated in a straight line in much the same manner as light propagating in free space. In [PERSON]'s experiments, the energy being sent across the Atlantic Ocean was a ground wave that was tightly bound to the surface of the Earth. The former was an unguided wave traveling at a speed of light, whilst the latter was a guided wave which operates in a similar manner as light being guided in an optical fiber.
## 4 Experimental Results
### Data Washing Algorithm
#### 4.1.1 Effectiveness of Data Washing Algorithm against Paralysis Attacks  
The experiments commenced by observing the performance of the proposed Data Washing algorithm when applied to datasets corrupted by the TensorFlowClog and Category Diverse attacks with a clip coefficient of 0.1 in both cases. Figure 6 presents the typical experimental results obtained for the two attack methods. Comparing the images in the first two rows, it is evident that the Data Washing algorithm achieves a significant reduction in the noise, ir-respective of the attack method applied. Moreover, comparing the images in the second and third rows, it is seen that the apparent effectiveness of the Data Washing algorithm increases with an increasing amount of noise in the image.  
Figure 6: Effectiveness of Data Washing algorithm. First row: without Data Washing. Second row: with Data Washing. Third Row: enhanced Data Washing. (**a**) Without attack. (**b**) TensorFlowClog. (**c**) Category Diverse.  
Table 2 shows the effect of the Data Washing algorithm in improving the accuracy of the ResNet [17] model when subjected to TensorClog and Category Diverse paralysis attacks, respectively. In the case where the Data Washing algorithm is applied to the original clean dataset, the accuracy reduces by 0.0138. Following the TensorClog and Category Diverse attacks, the accuracy reduces to 0.6166 and 0.1660, respectively. In other words, the Category Diverse attack renders the dataset virtually unusable. However, following Data Washing, the accuracy rate is improved to 0.7044. While this result is not as good as that obtained for the original dataset (0.8316), it represents a significant improvement (0.5384) over that of the corrupted dataset.  
Table 3 compares the effectiveness of the Data Washing algorithm with that of a simple denoising autoencoder (DAE). It is seen that for both attack models, the Data Washing algorithm results in a greater improvement in the ResNet performance than the conventional denoising autoencoder. In other words, the Data Washing algorithm provides a superior cleaning performance. The improvement is particularly apparent in the case of the Category Diverse attack.
## 3 Results and Discussion  
### Thermal Properties of DVB-T-ID SOM  
The thermal behavior and stability of DVB-T-ID were investigated by measuring the TGA and DSC, as shown in Figure 1. The TGA plot displays a high decomposition temperature (T\({}_{\text{d}}\)) at \(\sim\)330 \({}^{\circ}\)C with 5% weight loss, indicating a high thermal stability of DVB-T-ID SOMs. In support, the DSC thermogram (Figure 1b) shows a sharp melting temperature (T\({}_{\text{m}}\)) at \(\sim\)188 \({}^{\circ}\)C and an exothermic crystalline transition (T\({}_{\text{c}}\)) occurs at \(\sim\)370 \({}^{\circ}\)C. Strong T\({}_{\text{m}}\) and T\({}_{\text{c}}\) peaks are attributed to a liquid crystal (LC) transition phase and a good intermolecular cohesion in D-\(\pi\)-A, generally due to self-organizational behavior in a solid state, resulting in \(\pi\)-\(\pi\) stacking and possible phase transition [31; 32].
## 2 Study Area and Data
### Data
#### DEM  
The accuracy of topographic correction depends on DEM quality to a relatively large extent. Currently, SRTM (Shuttle Radar Topography Mission) and ASTER GDEM are commonly considered as two kinds of free and high-quality DEM data sources with almost global coverage. ASTER GDEM possesses overall vertical accuracy of 20 m, and has nominal 30 m horizontal resolution [25], much closer to TM image than SRTM. Considering the spatial resolution difference between STRM (90 m) and TM images (30 m), we chose ASTER GDEM (version 2) as the ancillary topographic data source. ASTER GDEM data were downloaded from USGS website ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))), and then mosaicked and re-projected to Universal Transverse Mercator (UTM) projection. After resized to the same area of TM image, GDEM was used to derive slope and aspect for each TM pixel, which would function as important parameters for angular configuration and radiation transformation from inclined surface to horizontal one.  
Since DEMs are representations of topography with inherent errors, which may lead to uncertainty of application [26], a lot of researchers try to smooth the DEM model to suppress the influence of artifacts before application [20, 27, 28, 29]. It was reported that ASTER GDEM is more subject to artifacts such as stripes or cloud anomalies [30, 31]. Thus, we checked the ASTER GDEM of study area visually (by comparing with SRTM), and made sure that there was no conspicuous outstanding deviation points. Also, a 3 by 3 low pass filter was used to smooth the DEM. Furthermore, considering the tremendously undulate terrain of the study area, which leads to huge vertical altitude difference within small horizontal distance, we used the slope function described in reference (smoothing factor 5) to smooth the DEM-derived slopes [29], which has been proven effective for topographic correction [28, 32].
## 3 Methodology
### Trend Analysis  
The Theil-Sen median method, also called Sen slope estimation, is a robust nonparametric statistical trend calculation method. This method is efficient and insensitive to measurement errors and outlier data. This method is often used in the trend analyses of long-term series data. The specific formula is as follows:  
\[\beta=\text{Median}\bigg{(}\frac{x_{j}-x_{i}}{j-1}\bigg{)},j>i,i=1,2,3,\ldots,n \tag{3}\]  
where \(x_{j}\) and \(x_{i}\) are the time series elements of the trend to be analyzed. \(\beta\) is the slope of the regression equation, i.e., the change trend of the variable. When \(\beta>0\), the variable shows an upward trend. When \(\beta<0\), the variable shows a downward trend.  
The Mann-Kendall test method is a nonparametric test method. Compared with other parametric test methods, this method does not require samples to follow a certain distribution. Besides, it is less interfered with by outliers and is more suitable for sequential variables. The specific formula is as follows, where \(S\) is the statistic and \(Z\) is the test statistic. When \(Z\) is positive, it indicates that the data tend to increase with time. When \(Z\) is negative, it indicates that the data tend to decrease with time. A value of \(Z=0\) indicates that the data have no tendency to increase or decrease.  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sign}\big{(}x_{j}-x_{i}\big{)}\qquad \text{ ($i=2,3,\ldots,n$)} \tag{4}\]  
\[Z=\left\{\begin{array}{ll}(S-1)/\sqrt{n(n-1)(2n+5)/18}&S>0\\ 0&S=0\\ (S+1)/\sqrt{n(n-1)(2n+5)/18}&S<0\end{array}\right. \tag{5}\]