esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Time Series Video Classification Technology Based on CRNN  
The original DL-based video classification methods independently extracted frame features and pooled predictions, neglecting temporal dynamics. Enhancements like long term recurrent convolutional networks (LRCNs) [26] combined 2D CNNs with LSTMs for temporal modeling but lost structural features. Three-dimensional convolutions [27] addressed this by integrating temporal dimensions, while two-stream inflated 3D conv [28] further improved spatiotemporal feature extraction. Recent advances include Transformer-based models like the video transformer network (VTN) [29], which uses 2D convolutions and attention mechanisms for efficient spatiotemporal learning, achieving competitive performance with faster training and inference. Gas leakage dynamics require temporal dependency modeling across sequential frames. Current single-frame approaches neglect structural time series information, reducing the classification accuracy. Exploring temporal feature extraction is critical for monitoring dynamic hazards like UUT natural gas leaks.  
To solve the above problems, this paper proposes a UUT pipeline operation and maintenance monitoring method based on DL and CV. The main contributions are as follows:  
1. Model development and implementation. This paper proposes a CRNN detection method based on LSTM and a CNN, which can fully identify the occurrence of leakages and grade the leakage level using time series information, providing a new technical solution for UUT operation and maintenance management.
2. Experimental design. In order to safely and effectively obtain data for model training, this paper sets up an experimental environment on site to fully restore and simulate the on-site conditions and truly simulate a gas leakage state, which ensures the validity of the data.
3. Dataset creation. To make full use of time series information, this paper uses sliding windows and other means to create a dataset, as well as a variety of data enhancement methods to increase the complexity of the data, which helps to improve the training effect of the model and enhance the robustness of the model.
# Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract
## 5 Experimental Results and Discussion
### Receiver Operating Characteristic of Different Models by Different Classifiers  
Training was also utilized to measure accuracy, recall, and precision, in addition to the F1 score. Each model's performance was calculated using the receiver operating characteristic (ROC) curve, which is a graphical depiction of a binary classification model's performance. For varying classification criteria, the ROC curve displays the true positive rate (TPR) vs. the false positive rate (FPR). A threshold is a judgement limit that is used to assess whether a sample is positive or negative. We may trade off the TPR and FPR by modifying the threshold, and the ROC curve demonstrates how the model's performance varies when the threshold is changed. In our case, the proposed model outperforms the ADABoost algorithm (97.01%) on the classification task and identifies the trade-offs between TPR and FPR. Because the model has a higher TPR than the ADABoost algorithm (97.01%) at the same FPR, this would imply that the proposed model (99.17%) was more effective at detecting fraudulent transactions. Figure 10 depicts the comparable findings. According to the ROC of the suggested technique, which has an area under the curve of 98.87%, the classification accuracy of DL employing the given optimization strategy is 98.87%. A confusion matrix, also shown in Figure 10, is used to evaluate the performance of a classification model. It consists of two classes: class 0 represents non-fraudulent transactions and class 1 represents fraudulent transactions. Values in the four cells represent the number of true positive, true negative, false positive, and false negative predictions made by the model, respectively. The true positive value (TP:1547) represents the number of fraudulent transactions that the model correctly identified as fraudulent. The true negative  
Figure 9: Feature importance comparison.  
value (TN:7) represents the number of non-fraudulent transactions that the model correctly identified as non-fraudulent. The false positive value (FP:12) represents the number of non-fraudulent transactions that the model incorrectly identified as fraudulent. The false negative value (FN:457) represents the number of fraudulent transactions that the model incorrectly identified as non-fraudulent. Analyzing the matrix also helps us to gain insight into the performance of the model and identify areas where it may be underperforming. If the false negative value is high, it could indicate that the model is not performing well in detecting fraudulent transactions. On the other hand, if the false positive value is high, it could indicate that the model is generating a large number of false alarms and flagging non-fraudulent transactions as fraudulent.  
In comparison to other techniques, the proposed deep learning model performs relatively better (Figure 7). As a result, the proposed method for DL with a hyperparameter configuration provides a strong, deepening framework for the identification of fraudulent Ethereum-based exchanges.
## 3 Results and Discussion
### Model Estimation for Weld Bead Shape  
#### 3.2.1 Leg Length  
RSM was applied to quantitatively explain the relationship between the welding parameters and bead shape. Table 6 summarizes the ANOVA results for \(Y_{leg}\). The significance level (\(\alpha\)) was set at 0.05. Therefore, the term with a \(p\)-value \(\leq\) 0.05 was determined as a significant term. Because the \(p\)-values of the interaction and square terms \(X_{1}\times X_{2}\), \(X_{2}\times X_{3}\), \(X_{1}\times X_{3}\), and \(X_{1}\times X_{2}\) were over 0.05, respectively, these terms were determined to be insignificant and were excluded from the reduced model through backward elimination. In addition, F-value is an indicator for comparing sample groups and shows how far apart the mean sample group is between groups. Therefore, a high F-value means that it has a major influence on the independent variable. As a result of the ANOVA, the \(X_{3}\) F-value was highest among the welding parameters. So, that means \(X_{3}\) has a major influence on the \(Y_{leg}\). The coefficient of determination (R\({}^{2}\)) was 0.9414, indicating that the estimated model can predict 94.14% of the data.  
\[Y_{leg}=32.38-0.405X_{1}-0.334X_{2}+0.4814X_{3}+0.00135X_{1}^{2}+0.0617X_{2}^{2} \tag{1}\]  
Figure 8: Main effects plots under welding conditions: (**a**) \(Y_{leg}\); (**b**) \(Y_{pen}\).
## 4 Evaluation
### Simulation Environment  
In the present work, we use the event driven simulator ns-2 [25] for our simulations. The network consists of 100 nodes for predefined confined in a \(500\times 600\) m\({}^{2}\) area. Transmission range of each node is assumed 40 m. The simulation runs for 500 seconds. BS is located near the middle-left at (0, 300). We simulate three CBR flows originating from randomly chosen nodes across the network. Each flow sends 32 byte packets at 256 bps.  
We generate and evaluate different possible random and predefined scenarios for simulation. The key performance measures are path length, energy consumption and routing overhead. The results presented here are the average values taken from multiple simulation results. We use Gabriel Graph (GG) for our simulation with beacon interval of 5 seconds. The other simulation parameters are given in Table 2.  
We draw a virtual line from source to destination and divide the area into two sides, namely L side and R side. Different possible scenarios are generated and visualized using Network Animator (NAM) [26].
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators
### Ionic Polymer Metal Composite (IPMC)
#### 2.1.1 Recent Trends in the Modification of IPMC
The quality of the electrodes on the IPMC surface has a great impact on the properties and operation of the prepared IPMC actuators. Therefore, one of the intensively developed research directions is the modification of the electrode's composition in order to improve its properties. During the fabrication of the electrode, the plating is usually performed twice. The first time the metal is deposited by chemical reduction, while the second layer can be deposited identically or by electrochemical metal deposition. Therefore, one direction of the actually conducted research is the use of other metals like Cu [26; 28; 29] or Ga/In [30] to deposit the metallic layer during the second plating. Another direction of investigation presents the application of metallic electrodes modified by other, nonmetallic additives like graphene [12; 27], carbon nanotubes [27], or conductive polymers [17; 18]. Additionally, conductive polymers (e.g., PEDOT) or polymer composites with conductive nanoparticles (e.g., Nafion with carbon nanotube), with sufficient conductivity that can replace metallic electrodes on polymer membranes, were widely studied [31; 32].
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 3 Semiconductor Stresses and AC Analysis of the Proposed SEPIC-Based Converter
### Comparative Study to Other Step-Down/Step-Up Topologies
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Type of Converter**} \\ \hline \multicolumn{1}{c}{**Classical Buck-Boost [13]**} & \multicolumn{1}{c}{**Classical Nonisolated SEPIC**} & \multicolumn{1}{c}{**Hybrid SEPIC [24]**} \\ \hline Switches & 1 & 1 & 1 \\ \hline Diodes & 1 & 1 & 4 \\ \hline No. of cores/windings & \(1/1\) & 2/2 & 3/3 \\ \hline Total no. of components & 4 & 6 & 10 \\ \hline System order & 2 & 4 & 5 \\ \hline Static conversion ratio-\(M\) & \(\frac{D}{1-D}\) & \(\frac{D}{1-D}\) & \(\frac{D\times(1+D)}{1-D}\) \\ \hline Duty cycle-\(D\) & \(\frac{M}{1+M}\) & \(\frac{M}{1+M}\) & \(\frac{-1-M+\sqrt{(1+M)^{2}+4\times M}}{2}\) \\ \hline Switch current stress & \(M^{2}\times\frac{V_{g}}{R}\) & \(M^{2}\times\frac{V_{g}}{R}\) & \(M^{2}\times\frac{V_{g}}{R}\) \\ \hline Switch voltage stress & \((1+M)\times V_{g}\) & \((1+M)\times V_{g}\) & \(\frac{2\times M}{-1-M+\sqrt{(1+M)^{2}+4\times M}}\times V_{g}\) \\ \hline Maximum diode dc current stress & \(M^{2}\times\frac{V_{g}}{R}\) & \(M^{2}\times\frac{V_{g}}{R}\) & \(M\times\frac{V_{g}}{R}\) \\ \hline Maximum diode voltage stress & \((1+M)\times V_{g}\) & \((1+M)\times V_{g}\) & \(\frac{-1-M+\sqrt{(1+M)^{2}+4\times M}}{3+M-\sqrt{(1+M)^{2}+4\times M}}\times V _{g}\) \\ \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{3}{c}{**Type of Converter**} \\ \hline \multicolumn{1}{c}{**Proposed Hybrid SEPIC**} \\ \hline Switches & & 1 \\ \hline Diodes & & 3 \\ \hline No. of cores/windings & & 2/3 \\ \hline Total no. of components & & 8 \\ \hline System order & & 4 \\ \hline Static conversion ratio-\(M\) & \(\frac{D}{(1-D)}\times\frac{R+D}{R}\) \\ \hline Duty cycle-\(D\) & & \(\frac{-n\times(1+M)+\sqrt{n^{2}\times(1+M)^{2}+4\times n\times M}}{2}\) \\ \hline Switch current stress & \(M^{2}\times\frac{V_{g}}{R}\) \\ \hline Switch voltage stress & \(\frac{2\times M}{-n\times(1+M)+\sqrt{n^{2}\times(1+M)^{2}+4\times n\times M}} \times V_{g}\) \\ \hline Maximum diode dc current stress & \(\frac{(1+n)\times\left[n^{2}\times(1+M)^{2}+2\times n\times M-n\times(1+M) \times\left[n^{2}\times(1+M)^{2}+4\times n\times M\right]\right]}{n\times\left[ 2+n\times(1+M)-\sqrt{n^{2}\times(1+M)^{2}+4\times n\times M}\right]}\times M \times\frac{V_{g}}{R}\) \\ \hline Maximum diode voltage stress & \(\frac{2\times M}{-n\times(1+M)+\sqrt{n^{2}\times(1+M)^{2}+4\times n\times M}} \times V_{g}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison between the main parameters of different step-down/step-up converters. classical SEPIC, or Buck-Boost converter. Therefore, in the step-up region, a wide static conversion ratio is achieved at a moderate duty cycle, and this can be observed also from the dependency of the static conversion ratios against the duty cycle for the proposed converter, for the classical and the hybrid SEPIC converters presented in Figure 7.
## 1 Introduction
Novelties of the manuscript are summarized as follows: (i) to our best knowledge, the site of the application has been used for the very first time to demonstrate the application of the coastal flooding early warning system; (ii) the relevance of the selected site relies on the fact that the area is faced with coastal flooding more than 15 days per year on average, thus increasing the need for early warning system development, (iii) the manuscript couples three mechanisms contributing to the coastal flooding vulnerability assessment, those being barometric pressure-induced sea level changes, wind-generated waves and tidal-induced fluctuations, (iv) barometric- and tidal-induced sea level changes are obtained in real time, fully relying on local conditions arising from the observations, (v) wind-generated wave heights are site specific, taking into consideration bathymetric features and coastal structure type and (vi) the whole procedure has been implemented in the form of a real-time mobile application, thus resulting in a reliable tool for the end users. Compared to relevant publications [16; 21], this paper couples three of the mechanisms leading to the sea water level rise and refers to a site-specific area. Although it does not offer general findings, the procedure shows the potential to be applied to other sites all over  
Figure 1: (**a**) Location of the city of Kašela in Kašela Bay; (**b**) A view of an old historical core, Kastel Kambelovac. Figure 2: Coastal flooding events in the city of Kastela. the Mediterranean basin after prior modifications. The latter refers to bathymetric features, the reflection of the coastline, the determination of incident deep water wave parameters and tidal observations. The paper is structured as follows: Section 2 is the Methodology and Study area section, which couples the methodology used in the study with representative site related data. Section 3 offers Results from the study, while Section 4 consists of relevant Discussion topics and Section 5 summarizes main conclusion points.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
###### Abstract  
In order to accurately identify the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries, this paper proposes an SOH estimation algorithm for lithium-ion batteries based on stream learning and LightGBM. To address the problem of inconsistent data length, which makes it difficult to establish the state mapping relationship between degraded data and health state, the health factors in this paper are extracted from capacity degradation features, entropy features, and correlation coefficient features. Then, the landmark isometric mapping (L-ISOMAP) manifold learning algorithm is used to dimensionally reduce the input feature set and map the high-dimensional features to the low-dimensional space to solve the dimensional explosion problem. Finally, a LightGBM prediction model is developed to perform SOH prediction on different datasets, and the superiority of the multidimensional model is evaluated. The experimental results show that the goodness-of-fit is 0.98 and above, and the MSE values are below 4 \(\times\)\(10^{-4}\). Comparing several prediction models, the LightGBM model has the best performance and better results in several indexes, such as MSE and RMSE. Under different working conditions, the proposed model in this paper has a goodness-of-fit of more than 0.98 in dataset B, which proves that the proposed model has a strong generalization ability.  
capacity degradation feature; SOH prediction; manifold learning; LightGBM model +
Footnote †: journal: Applied  
Article  
Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
# Research on the Performance of an Aircraft Landing
## 2 System Modeling
### Constraint Equations  
As shown in Figure 2, there are three primary elements in the hook lock mechanism besides the lock bracket (linkage 1, linkage 2, and hook 3). Their locations can be described as \((x_{i},y_{i},\theta_{i})\). This indicates that the mechanism has nine state variables, four revolute joints, and one degree of freedom (DOF). Therefore, eight constraint equations can be obtained, as presented in Equation (1).  
\[\left\{\begin{array}{c}x_{1}-m-a_{1}\;\cdot\;\cos(\theta_{1}+\alpha_{1})=0\\ y_{1}-a_{1}\;\cdot\;\sin(\theta_{1}+\alpha_{1})=0\\ x_{2}-m-l_{1}\;\cdot\;\sin\theta_{1}+b_{1}\;\cdot\;\cos(\theta_{2}-\beta_{1}) =0\\ y_{2}-l_{1}\;\cdot\;\cos\theta_{1}-b_{1}\;\cdot\;\sin(\theta_{2}-\beta_{1})=0 \\ x_{3}-c_{1}\;\cdot\;\cos(\theta_{3}+\gamma_{1})=0\\ y_{3}-n-c_{1}\;\cdot\;\sin(\theta_{3}+\gamma_{1})=0\\ m+l_{1}\;\cdot\;\cos\theta_{1}+l_{2}\;\cdot\;\cos\theta_{2}-l_{3}\;\cdot\; \cos\theta_{3}=0\\ n+l_{1}\;\cdot\;\sin\theta_{1}+l_{2}\;\cdot\;\sin\theta_{2}-l_{3}\;\cdot\; \sin\theta_{3}=0\end{array}\right.. \tag{1}\]  
The coordinate position of the spring point is obtained via Equation (2).  
\[\left\{\begin{array}{c}x_{2}^{s}=m+l_{1}\;\cdot\;\cos\theta_{1}+b_{1}\;\cdot \;\cos(\theta_{2}-\beta_{2})\\ y_{2}^{s}=l_{1}\;\cdot\;\sin\theta_{1}+b_{2}\;\cdot\;\sin(\theta_{2}-\beta_{2}) \\ x_{3}^{s}=c_{2}\;\cdot\;\cos(\theta_{3}+\gamma_{2})\\ y_{3}^{s}=n+c_{2}\;\cdot\;\sin(\theta_{3}+\gamma_{2})\end{array}\right.. \tag{2}\]  
Here, \((x_{i},y_{i})\) represents the center-of-gravity coordinates of the ith component; \((x_{i}^{s},y_{i}^{s})\) represents the coordinate of the spring application point of the \(i\)th component; \(l_{i}\) is the length between two hinge points of the \(i\)th component; and \(m\) and \(n\) are the distances between the two hinge joints A and D of the lock bracket in the directions of x and y, respectively. \(a_{1}\), \(b_{1}\), and \(c_{1}\) denote the distances from the hinge points A, B, and D to the centers of gravity of the corresponding components. \(a_{2}\) is the distance from hinge joint A to action point E of the unlocking force \(F_{Ullock}\); \(b_{2}\) and \(c_{2}\) are the distances from hinge joints B and D to the application points of the spring, respectively; \(\theta_{i}\) is the angle between the line which connects the two hinge points of the \(i\)th component and the \(x\)-axis (counterclockwise); and \(\alpha_{1}\), \(\beta_{1}\), \(\gamma_{1}\), \(\alpha_{2}\), \(\beta_{2}\), and \(\gamma_{2}\) represent the angles of \(\angle 0_{1}AB\), \(\angle 0_{2}BC\), \(\angle 0_{3}DC\), \(\angle BAE\), \(\angle RBC\), and \(\angle GDC\), respectively.  
Figure 2: Hook lock mechanism sketch.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 2 Background  
### Rdma  
RDMA is characterized by the implementation of transport layer protocols within hardware (i.e., Network Interface Card, NIC), facilitating direct access to the memory of remote servers. As a result, RDMA is capable of transferring data between servers with low latency and high throughput while incurring minimal CPU overhead.  
There are three principal solutions for RDMA: InfiniBand[20], iWARP[21], and RoCE[22]. Originally developed for HPC, InfiniBand Verbs RDMA was embraced widely in the supercomputing field as a standardized solution. Nevertheless, its closed-source ecosystem, dependence on proprietary devices, and the following high costs associated with deployment posed significant barriers. In response, the emergence of iWARP and RoCE as new protocols enabled RDMA deployment within Ethernet-based DCs. RoCEv1 simply adopts an InfiniBand-like transport layer (i.e., the Base Transport Header, BTH) on top of Ethernet's L2 headers. iWARP enables RDMA over standard TCP/IP networks by encapsulating RDMA operations within TCP packets, eliminating the need for specialized hardware. Both protocols were designed to lower the costs associated with RDMA deployment on pre-existing Ethernet-based infrastructures. Nonetheless, the complexity and high cost of hardware offloading a complete TCP/IP stack rendered iWARP less prevalent compared to RoCE's fundamentally simpler protocol. Subsequently, RoCEv2 incorporated IP/UDP Layer 3 headers, enabling routing capabilities within and across DCs, thereby addressing the scalability limitations inherent in RoCEv1. Consequently, RoCEv2 is not only compatible with conventional Ethernet networks, utilizing established infrastructure, but also capitalizes on the high performance and low latency advantages of RDMA. Given these benefits, RoCEv2 has been extensively adopted in DCN, with the deployment of RoCEv2 NICs surpassing that of InfiniBand NICs [24].
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 3 Results
### Dust and Cloud Losses
Water ice absorptivities are also expected to be in the same range which enables both dust and water ice to be detected from signal amplitude measurements during the occultation. We also provide an estimation of expected signal loss during a dust storm at 1 THz and 10 THz. The dust distribution for the four scenarios is described in Table 1. We consider the refraction indices derived from a case of an MGS radio science experiment to calculate the signal loss expected over the transmission path. It is to be noted that in a real scenario, the refractive indices and bending angles are expected to vary differently from radio occultation. Figure 6 shows that dust storm scenarios can exhibit a wide range of signal loss from \(\sim\)1 dB to 300 dB. Global dust storms at 10 THz can provide signal losses above 100 dB contributing to signal attenuation. However, at 1 THz, the signal loss due to dust activity is expected to be in the detectable range of \(\sim\)1-10 dB in the PBL. It shows that the lower end of the frequency band between 1 and 10 THz is suitable for retrieval of dust and water vapor opacities from occultation experiments. Figure 5: Dust absorptivity at frequencies from 1 to 10 THz for (**A**) global dust storm in MY 28 and (**B**) no dust storm; (**C**) the range of water ice absorptivities between 1 and 10 THz compared with VeRa S- and X-band absorptivities [40].